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Sample records for classifications severity weighting

  1. Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm

    Directory of Open Access Journals (Sweden)

    E. Parvinnia

    2014-01-01

    Full Text Available Electroencephalogram (EEG signals are often used to diagnose diseases such as seizure, alzheimer, and schizophrenia. One main problem with the recorded EEG samples is that they are not equally reliable due to the artifacts at the time of recording. EEG signal classification algorithms should have a mechanism to handle this issue. It seems that using adaptive classifiers can be useful for the biological signals such as EEG. In this paper, a general adaptive method named weighted distance nearest neighbor (WDNN is applied for EEG signal classification to tackle this problem. This classification algorithm assigns a weight to each training sample to control its influence in classifying test samples. The weights of training samples are used to find the nearest neighbor of an input query pattern. To assess the performance of this scheme, EEG signals of thirteen schizophrenic patients and eighteen normal subjects are analyzed for the classification of these two groups. Several features including, fractal dimension, band power and autoregressive (AR model are extracted from EEG signals. The classification results are evaluated using Leave one (subject out cross validation for reliable estimation. The results indicate that combination of WDNN and selected features can significantly outperform the basic nearest-neighbor and the other methods proposed in the past for the classification of these two groups. Therefore, this method can be a complementary tool for specialists to distinguish schizophrenia disorder.

  2. [Severity classification of chronic obstructive pulmonary disease based on deep learning].

    Science.gov (United States)

    Ying, Jun; Yang, Ceyuan; Li, Quanzheng; Xue, Wanguo; Li, Tanshi; Cao, Wenzhe

    2017-12-01

    In this paper, a deep learning method has been raised to build an automatic classification algorithm of severity of chronic obstructive pulmonary disease. Large sample clinical data as input feature were analyzed for their weights in classification. Through feature selection, model training, parameter optimization and model testing, a classification prediction model based on deep belief network was built to predict severity classification criteria raised by the Global Initiative for Chronic Obstructive Lung Disease (GOLD). We get accuracy over 90% in prediction for two different standardized versions of severity criteria raised in 2007 and 2011 respectively. Moreover, we also got the contribution ranking of different input features through analyzing the model coefficient matrix and confirmed that there was a certain degree of agreement between the more contributive input features and the clinical diagnostic knowledge. The validity of the deep belief network model was proved by this result. This study provides an effective solution for the application of deep learning method in automatic diagnostic decision making.

  3. 42 CFR 412.60 - DRG classification and weighting factors.

    Science.gov (United States)

    2010-10-01

    ... DRG classification system provides a DRG, and an appropriate weighting factor, for the group of cases... 42 Public Health 2 2010-10-01 2010-10-01 false DRG classification and weighting factors. 412.60... Determining Prospective Payment Federal Rates for Inpatient Operating Costs § 412.60 DRG classification and...

  4. Classification of Company Performance using Weighted Probabilistic Neural Network

    Science.gov (United States)

    Yasin, Hasbi; Waridi Basyiruddin Arifin, Adi; Warsito, Budi

    2018-05-01

    Classification of company performance can be judged by looking at its financial status, whether good or bad state. Classification of company performance can be achieved by some approach, either parametric or non-parametric. Neural Network is one of non-parametric methods. One of Artificial Neural Network (ANN) models is Probabilistic Neural Network (PNN). PNN consists of four layers, i.e. input layer, pattern layer, addition layer, and output layer. The distance function used is the euclidean distance and each class share the same values as their weights. In this study used PNN that has been modified on the weighting process between the pattern layer and the addition layer by involving the calculation of the mahalanobis distance. This model is called the Weighted Probabilistic Neural Network (WPNN). The results show that the company's performance modeling with the WPNN model has a very high accuracy that reaches 100%.

  5. Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification

    National Research Council Canada - National Science Library

    Han, Euihong; Karypis, George; Kumar, Vipin

    1999-01-01

    .... The authors present a nearest neighbor classification scheme for text categorization in which the importance of discriminating words is learned using mutual information and weight adjustment techniques...

  6. Bilateral weighted radiographs are required for accurate classification of acromioclavicular separation: an observational study of 59 cases.

    Science.gov (United States)

    Ibrahim, E F; Forrest, N P; Forester, A

    2015-10-01

    Misinterpretation of the Rockwood classification system for acromioclavicular joint (ACJ) separations has resulted in a trend towards using unilateral radiographs for grading. Further, the use of weighted views to 'unmask' a grade III injury has fallen out of favour. Recent evidence suggests that many radiographic grade III injuries represent only a partial injury to the stabilising ligaments. This study aimed to determine (1) whether accurate classification is possible on unilateral radiographs and (2) the efficacy of weighted bilateral radiographs in unmasking higher-grade injuries. Complete bilateral non-weighted and weighted sets of radiographs for patients presenting with an acromioclavicular separation over a 10-year period were analysed retrospectively, and they were graded I-VI according to Rockwood's criteria. Comparison was made between grading based on (1) a single antero-posterior (AP) view of the injured side, (2) bilateral non-weighted views and (3) bilateral weighted views. Radiographic measurements for cases that changed grade after weighted views were statistically compared to see if this could have been predicted beforehand. Fifty-nine sets of radiographs on 59 patients (48 male, mean age of 33 years) were included. Compared with unilateral radiographs, non-weighted bilateral comparison films resulted in a grade change for 44 patients (74.5%). Twenty-eight of 56 patients initially graded as I, II or III were upgraded to grade V and two of three initial grade V patients were downgraded to grade III. The addition of a weighted view further upgraded 10 patients to grade V. No grade II injury was changed to grade III and no injury of any severity was downgraded by a weighted view. Grade III injuries upgraded on weighted views had a significantly greater baseline median percentage coracoclavicular distance increase than those that were not upgraded (80.7% vs. 55.4%, p=0.015). However, no cut-off point for this value could be identified to predict an

  7. [Determinant-based classification of acute pancreatitis severity. International multidisciplinary classification of acute pancreatitis severity: the 2013 German edition

    NARCIS (Netherlands)

    Layer, P.; Dellinger, E.P.; Forsmark, C.E.; Levy, P.; Maravi-Poma, E.; Shimosegawa, T.; Siriwardena, A.K.; Uomo, G.; Whitcomb, D.C.; Windsor, J.A.; Petrov, M.S.; Geenen, E.J.M. van; et al.,

    2013-01-01

    OBJECTIVE: The aim of this study was to develop a new international classification of acute pancreatitis severity on the basis of a sound conceptual framework, comprehensive review of published evidence, and worldwide consultation. BACKGROUND: The Atlanta definitions of acute pancreatitis severity

  8. Fire severity classification: Uses and abuses

    Science.gov (United States)

    Theresa B. Jain; Russell T. Graham

    2003-01-01

    Burn severity (also referred to as fire severity) is not a single definition, but rather a concept and its classification is a function of the measured units unique to the system of interest. The systems include: flora and fauna, soil microbiology and hydrologic processes, atmospheric inputs, fire management, and society. Depending on the particular system of interest...

  9. Crown-level tree species classification from AISA hyperspectral imagery using an innovative pixel-weighting approach

    Science.gov (United States)

    Liu, Haijian; Wu, Changshan

    2018-06-01

    Crown-level tree species classification is a challenging task due to the spectral similarity among different tree species. Shadow, underlying objects, and other materials within a crown may decrease the purity of extracted crown spectra and further reduce classification accuracy. To address this problem, an innovative pixel-weighting approach was developed for tree species classification at the crown level. The method utilized high density discrete LiDAR data for individual tree delineation and Airborne Imaging Spectrometer for Applications (AISA) hyperspectral imagery for pure crown-scale spectra extraction. Specifically, three steps were included: 1) individual tree identification using LiDAR data, 2) pixel-weighted representative crown spectra calculation using hyperspectral imagery, with which pixel-based illuminated-leaf fractions estimated using a linear spectral mixture analysis (LSMA) were employed as weighted factors, and 3) representative spectra based tree species classification was performed through applying a support vector machine (SVM) approach. Analysis of results suggests that the developed pixel-weighting approach (OA = 82.12%, Kc = 0.74) performed better than treetop-based (OA = 70.86%, Kc = 0.58) and pixel-majority methods (OA = 72.26, Kc = 0.62) in terms of classification accuracy. McNemar tests indicated the differences in accuracy between pixel-weighting and treetop-based approaches as well as that between pixel-weighting and pixel-majority approaches were statistically significant.

  10. Impact of low-weight severity and menstrual status on bone in adolescent girls with anorexia nervosa.

    Science.gov (United States)

    Kandemir, Nurgun; Becker, Kendra; Slattery, Meghan; Tulsiani, Shreya; Singhal, Vibha; Thomas, Jennifer J; Coniglio, Kathryn; Lee, Hang; Miller, Karen K; Eddy, Kamryn T; Klibanski, Anne; Misra, Madhusmita

    2017-04-01

    Clinicians currently use different low-weight cut-offs both to diagnose anorexia nervosa (AN) and to determine AN severity in adolescent girls. The purpose of this study was to evaluate the clinical utility of existing cut-offs and severity criteria by determining which are most strongly associated with risk for low bone mineral density (BMD). Height adjusted BMD Z scores were calculated for 352 females: 262 with AN and 90 healthy controls (controls) (12-20.5 years), using data from the BMD in Childhood Study, for the lumbar spine, whole body less head, and total hip. For most cut-offs used to define low weight (5th or 10th BMI percentile, BMI of 17.5 or 18.5, and 85 or 90% of median BMI), AN had lower BMD Z scores than controls. AN at >85 or >90% expected body weight for height (EBW-Ht) did not differ in BMD Z scores from controls, but differed significantly from AN at ≤85 or ≤90% EBW-Ht. Among AN, any amenorrhea was associated with lower BMD. AN had lower BMD than controls across DSM-5 and The Society for Adolescent Health and Medicine (SAHM) severity categories. The SAHM moderate severity classification was differentiated from the mildly malnourished classification by lower BMD at hip and spine sites. Amenorrhea and %EBW-Ht ≤ 85 or ≤ 90% are markers of severity of bone loss within AN. Among severity categories, BMI Z scores (SAHM) may have the greatest utility in assessing the degree of malnutrition in adolescent girls that corresponds to lower BMD. © 2017 Wiley Periodicals, Inc.

  11. Joint learning and weighting of visual vocabulary for bag-of-feature based tissue classification

    KAUST Repository

    Wang, Jim Jing-Yan

    2013-12-01

    Automated classification of tissue types of Region of Interest (ROI) in medical images has been an important application in Computer-Aided Diagnosis (CAD). Recently, bag-of-feature methods which treat each ROI as a set of local features have shown their power in this field. Two important issues of bag-of-feature strategy for tissue classification are investigated in this paper: the visual vocabulary learning and weighting, which are always considered independently in traditional methods by neglecting the inner relationship between the visual words and their weights. To overcome this problem, we develop a novel algorithm, Joint-ViVo, which learns the vocabulary and visual word weights jointly. A unified objective function based on large margin is defined for learning of both visual vocabulary and visual word weights, and optimized alternately in the iterative algorithm. We test our algorithm on three tissue classification tasks: classifying breast tissue density in mammograms, classifying lung tissue in High-Resolution Computed Tomography (HRCT) images, and identifying brain tissue type in Magnetic Resonance Imaging (MRI). The results show that Joint-ViVo outperforms the state-of-art methods on tissue classification problems. © 2013 Elsevier Ltd.

  12. A note on multi-criteria inventory classification using weighted linear optimization

    Directory of Open Access Journals (Sweden)

    Rezaei Jafar

    2010-01-01

    Full Text Available Recently, Ramanathan (R., Ramanathan, ABC inventory classification with multiple-criteria using weighted linear optimization, Computer and Operations Research, 33(3 (2006 695-700 introduced a simple DEA-like model to classify inventory items on the basis of multiple criteria. However, the classification results produced by Ramanathan are not consistent with the domination concept encouraged some researchers to extend his model. In this paper, we produce the correct results and compare them to the original results and those of the extended models. We also improve this model to rank items with an optimal score of 1 using a cross-efficiency technique. The classification results are considerably different from the original results. Despite the fact that the correct results are obtained in this paper, there is no significant difference between the original model and its extensions, while the original model is more simple and suitable for the situations in which decision-maker cannot assign specific weights to individual criteria.

  13. 42 CFR 419.31 - Ambulatory payment classification (APC) system and payment weights.

    Science.gov (United States)

    2010-10-01

    ... 42 Public Health 3 2010-10-01 2010-10-01 false Ambulatory payment classification (APC) system and... Outpatient Services § 419.31 Ambulatory payment classification (APC) system and payment weights. (a) APC... of resource use into APC groups. Except as specified in paragraph (a)(2) of this section, items and...

  14. Application Study of Fire Severity Classification

    International Nuclear Information System (INIS)

    Kim, In Hwan; Kim, Hyeong Taek; Jee, Moon Hak; Kim, Yun Jung

    2013-01-01

    This paper introduces the Fire Incidents Severity Classification Method for Korean NPPs that may be derived directly from the data fields and feasibility study for domestic uses. FEDB was characterized in more detail and assessed based on the significance of fire incidents in the updated database and five fire severity categories were defined. The logical approach to determine the fire severity starts from the most severe characteristics, namely challenging fires, and continues to define the less challenging and undetermined categories in progress. If the FEDB is utilized for Korean NPPs, the ways of Fire Severity Classification suggested in 2.4 above can be utilized for the quantitative fire risk analysis in future. The Fire Events Database (FEDB) is the primary source of fire data which are used for fire frequency in Fire PSA (Probabilistic Safety Assessment). The purpose of its development is to calculate the quantitative fire frequency at the comprehensive and consolidated source derived from the fire incident information available for Nuclear Power Plants (NPPs). Recently, the Fire Events Database (FEDB) was updated by Electric Power Research Institute (EPRI) and Nuclear Regulatory Commission (NRC) in U. S. The FEDB is intended to update the fire event history up to 2009. A significant enhancement to it is the reorganization and refinement of the database structure and data fields. It has been expanded and improved data fields, coding consistency, incident detail, data review fields, and reference data source traceability. It has been designed to better support several Fire PRA uses as well

  15. 42 CFR 412.517 - Revision of LTC-DRG group classifications and weighting factors.

    Science.gov (United States)

    2010-10-01

    ... 42 Public Health 2 2010-10-01 2010-10-01 false Revision of LTC-DRG group classifications and... the LTC-DRG classifications and recalibration of the weighting factors described in paragraph (a) of... SERVICES Prospective Payment System for Long-Term Care Hospitals § 412.517 Revision of LTC-DRG group...

  16. [International multidisciplinary classification of acute pancreatitis severity: the 2013 Spanish edition].

    Science.gov (United States)

    Maraví-Poma, E; Patchen Dellinger, E; Forsmark, C E; Layer, P; Lévy, P; Shimosegawa, T; Siriwardena, A K; Uomo, G; Whitcomb, D C; Windsor, J A; Petrov, M S

    2014-05-01

    To develop a new classification of acute pancreatitis severity on the basis of a sound conceptual framework, comprehensive review of the published evidence, and worldwide consultation. The Atlanta definitions of acute pancreatitis severity are ingrained in the lexicon of specialist in pancreatic diseases, but are suboptimal because these definitions are based on the empiric description of events not associated with severity. A personal invitation to contribute to the development of a new classification of acute pancreatitis severity was sent to all surgeons, gastroenterologists, internists, intensivists and radiologists currently active in the field of clinical acute pancreatitis. The invitation was not limited to members of certain associations or residents of certain countries. A global web-based survey was conducted, and a dedicated international symposium was organized to bring contributors from different disciplines together and discuss the concept and definitions. The new classification of severity is based on the actual local and systemic determinants of severity, rather than on the description of events that are non-causally associated with severity. The local determinant relates to whether there is (peri) pancreatic necrosis or not, and if present, whether it is sterile or infected. The systemic determinant relates to whether there is organ failure or not, and if present, whether it is transient or persistent. The presence of one determinant can modify the effect of another, whereby the presence of both infected (peri) pancreatic necrosis and persistent organ failure has a greater impact upon severity than either determinant alone. The derivation of a classification based on the above principles results in four categories of severity: mild, moderate, severe, and critical. This classification is the result of a consultative process among specialists in pancreatic diseases from 49 countries spanning North America, South America, Europe, Asia, Oceania and

  17. Effect of weight loss on the severity of psoriasis

    DEFF Research Database (Denmark)

    Jensen, P; Zachariae, Claus; Christensen, R

    2013-01-01

    Psoriasis is associated with adiposity and weight gain increases the severity of psoriasis and the risk of incident psoriasis. Therefore, we aimed to measure the effect of weight reduction on the severity of psoriasis in obese patients with psoriasis.......Psoriasis is associated with adiposity and weight gain increases the severity of psoriasis and the risk of incident psoriasis. Therefore, we aimed to measure the effect of weight reduction on the severity of psoriasis in obese patients with psoriasis....

  18. [Multidisciplinar international classification of the severity of acute pancreatitis: Italian version 2013].

    Science.gov (United States)

    Uomo, G; Patchen Dellinger, E; Forsmark, C E; Layer, P; Lévy, P; Maravì-Poma, E; Shimosegawa, T; Siriwardena, A K; Whitcomb, D C; Windsor, J A; Petrov, M S

    2013-12-01

    The aim of this paper was to present the 2013 Italian edition of a new international classification of acute pancreatitis severity. The Atlanta definitions of acute pancreatitis severity are ingrained in the lexicon of pancreatologists but suboptimal because these definitions are based on empiric description of occurrences that are merely associated with severity. A personal invitation to contribute to the development of a new international classification of acute pancreatitis severity was sent to all surgeons, gastroenterologists, internists, intensivists, and radiologists who are currently active in clinical research on acute pancreatitis. A global web-based survey was conducted and a dedicated international symposium was organized to bring contributors from different disciplines together and discuss the concept and definitions. The new international classification is based on the actual local and systemic determinants of severity, rather than description of events that are correlated with severity. The local determinant relates to whether there is (peri)pancreatic necrosis or not, and if present, whether it is sterile or infected. The systemic determinant relates to whether there is organ failure or not, and if present, whether it is transient or persistent. The presence of one determinant can modify the effect of another such that the presence of both infected (peri)pancreatic necrosis and persistent organ failure have a greater effect on severity than either determinant alone. The derivation of a classification based on the above principles results in 4 categories of severity-mild, moderate, severe, and critical. This classification provides a set of concise up-to-date definitions of all the main entities pertinent to classifying the severity of acute pancreatitis in clinical practice and research.

  19. Using ELM-based weighted probabilistic model in the classification of synchronous EEG BCI.

    Science.gov (United States)

    Tan, Ping; Tan, Guan-Zheng; Cai, Zi-Xing; Sa, Wei-Ping; Zou, Yi-Qun

    2017-01-01

    Extreme learning machine (ELM) is an effective machine learning technique with simple theory and fast implementation, which has gained increasing interest from various research fields recently. A new method that combines ELM with probabilistic model method is proposed in this paper to classify the electroencephalography (EEG) signals in synchronous brain-computer interface (BCI) system. In the proposed method, the softmax function is used to convert the ELM output to classification probability. The Chernoff error bound, deduced from the Bayesian probabilistic model in the training process, is adopted as the weight to take the discriminant process. Since the proposed method makes use of the knowledge from all preceding training datasets, its discriminating performance improves accumulatively. In the test experiments based on the datasets from BCI competitions, the proposed method is compared with other classification methods, including the linear discriminant analysis, support vector machine, ELM and weighted probabilistic model methods. For comparison, the mutual information, classification accuracy and information transfer rate are considered as the evaluation indicators for these classifiers. The results demonstrate that our method shows competitive performance against other methods.

  20. An anthropometric classification of body contour deformities after massive weight loss.

    Science.gov (United States)

    Iglesias, Martin; Butron, Patricia; Abarca, Leonardo; Perez-Monzo, Mario F; de Rienzo-Madero, Beatriz

    2010-08-01

    Deformities caused by massive weight loss were originally subsidized at the Instituto Nacional de Ciencias Médicas y Nutrición "Salvador Zubirán." This caused great economical losses, which led to the development of a classification to select patients with functional problems secondary to massive weight loss. The parameter used is the size of the pannus in relation to fixed anatomic structures within the following anatomic regions: abdomen, arms, thighs, mammary glands, lateral thoracic area, back, lumbar region, gluteal region, sacrum, and mons pubis. Grade 3 deformities are candidates for body contouring surgery because they constitute a functional problem. Grade 2 deformities reevaluated whether the patient has comorbidities. Lesser grades are considered aesthetic procedures and are not candidates for surgical rehabilitation at the Instituto Nacional de Ciencias Médicas y Nutrición "Salvador Zubirán." This classification allowed an improvement in communication between the different surgical-medical specialties; therefore, we suggest its application not only for surgical-administrative reasons but also for academic purposes.

  1. Asian Americans: Diabetes Prevalence Across U.S. and World Health Organization Weight Classifications

    OpenAIRE

    Oza-Frank, Reena; Ali, Mohammed K.; Vaccarino, Viola; Narayan, K.M. Venkat

    2009-01-01

    OBJECTIVE To compare diabetes prevalence among Asian Americans by World Health Organization and U.S. BMI classifications. RESEARCH DESIGN AND METHODS Data on Asian American adults (n = 7,414) from the National Health Interview Survey for 1997–2005 were analyzed. Diabetes prevalence was estimated across weight and ethnic group strata. RESULTS Regardless of BMI classification, Asian Indians and Filipinos had the highest prevalence of overweight (34–47 and 35–47%, respectively, compared with 20–...

  2. Algorithm for Optimizing Bipolar Interconnection Weights with Applications in Associative Memories and Multitarget Classification

    Science.gov (United States)

    Chang, Shengjiang; Wong, Kwok-Wo; Zhang, Wenwei; Zhang, Yanxin

    1999-08-01

    An algorithm for optimizing a bipolar interconnection weight matrix with the Hopfield network is proposed. The effectiveness of this algorithm is demonstrated by computer simulation and optical implementation. In the optical implementation of the neural network the interconnection weights are biased to yield a nonnegative weight matrix. Moreover, a threshold subchannel is added so that the system can realize, in real time, the bipolar weighted summation in a single channel. Preliminary experimental results obtained from the applications in associative memories and multitarget classification with rotation invariance are shown.

  3. A hybrid clustering and classification approach for predicting crash injury severity on rural roads.

    Science.gov (United States)

    Hasheminejad, Seyed Hessam-Allah; Zahedi, Mohsen; Hasheminejad, Seyed Mohammad Hossein

    2018-03-01

    As a threat for transportation system, traffic crashes have a wide range of social consequences for governments. Traffic crashes are increasing in developing countries and Iran as a developing country is not immune from this risk. There are several researches in the literature to predict traffic crash severity based on artificial neural networks (ANNs), support vector machines and decision trees. This paper attempts to investigate the crash injury severity of rural roads by using a hybrid clustering and classification approach to compare the performance of classification algorithms before and after applying the clustering. In this paper, a novel rule-based genetic algorithm (GA) is proposed to predict crash injury severity, which is evaluated by performance criteria in comparison with classification algorithms like ANN. The results obtained from analysis of 13,673 crashes (5600 property damage, 778 fatal crashes, 4690 slight injuries and 2605 severe injuries) on rural roads in Tehran Province of Iran during 2011-2013 revealed that the proposed GA method outperforms other classification algorithms based on classification metrics like precision (86%), recall (88%) and accuracy (87%). Moreover, the proposed GA method has the highest level of interpretation, is easy to understand and provides feedback to analysts.

  4. Radiotherapy on the neck nodes predicts severe weight loss in patients with early stage laryngeal cancer

    International Nuclear Information System (INIS)

    Langius, Jacqueline A.E.; Doornaert, Patricia; Spreeuwenberg, Marieke D.; Langendijk, Johannes A.; Leemans, C. Rene; Schueren, Marian A.E. van Bokhorst-de van der

    2010-01-01

    Background and purpose: Although patients with early stage (T1/T2) laryngeal cancer (LC) are thought to have a low incidence of malnutrition, severe weight loss is observed in a subgroup of these patients during radiotherapy (RT). The objective of this study was to evaluate weight loss and nutrition-related symptoms in patients with T1/T2 LC during RT and to select predictive factors for early identification of malnourished patients. Methods: Of all patients with T1/T2 LC, who received primary RT between 1999 and 2007, the following characteristics were recorded: sex, age, TNM classification, tumour location, radiation schedule, performance status, quality of life, weight loss, and nutrition-related symptoms. The association between baseline characteristics and malnutrition (>5% weight loss during RT) was investigated by Cox regression analysis. Results: The study population consisted of 238 patients. During RT, 44% of patients developed malnutrition. Tumour location, TNM classification, RT on the neck nodes, RT dose, nausea/vomiting, pain, swallowing, senses problems, trouble with social eating, dry mouth and the use of painkillers were all significantly associated with malnutrition. In the multivariate analysis, RTs on both the neck nodes (HR 4.16, 95% CI 2.62-6.60) and dry mouth (HR 1.72, 95% CI 1.14-2.60) remained predictive. Nevertheless, RT on the neck nodes alone resulted in the best predictive model for malnutrition scores. Conclusions: Patients with early stage laryngeal cancer are at risk of malnutrition during radiotherapy. Radiotherapy on the neck nodes is the best predictor of malnutrition during radiotherapy. Therefore, we suggest to offer nutritional counselling to all the patients who receive nodal irradiation.

  5. Determinant-Based Classification of Acute Pancreatitis Severity: An International Multidisciplinary Consultation

    NARCIS (Netherlands)

    Dellinger, E.P.; Forsmark, C.E.; Layer, P.; Levy, P.; Maravi-Poma, E.; Petrov, M.S.; Shimosegawa, T.; Siriwardena, A.K.; Uomo, G.; Whitcomb, D.C.; Windsor, J.A.; Geenen, E.J.M. van; et al.,

    2012-01-01

    OBJECTIVE:: To develop a new international classification of acute pancreatitis severity on the basis of a sound conceptual framework, comprehensive review of published evidence, and worldwide consultation. BACKGROUND:: The Atlanta definitions of acute pancreatitis severity are ingrained in the

  6. International Multidisciplinary Classification of Acute Pancreatitis Severity: The 2013 Spanish Edition

    NARCIS (Netherlands)

    Maraví-Poma, E.; Patchen Dellinger, E.; Forsmark, C. E.; Layer, P.; Lévy, P.; Shimosegawa, T.; Siriwardena, A. K.; Uomo, G.; Whitcomb, D. C.; Windsor, J. A.; Petrov, M. S.; Abu Hilal, M.; Abu-Zidan, F. M.; Acosta, J. M.; Ainsworth, A. P.; Aizcorbe Garralda, M.; Alagozlu, H.; Al'aref, S. J.; Albeniz Arbizu, E.; Alhajeri, A.; Almeida, J. L.; Ammori, B. J.; Andersson, R.; Ardengh, J. C.; Arroyo-Sanchez, A. S.; Arvanitakis, M.; Ashley, S. W.; Aygencel, G.; Ayoub, W. A.; Baillie, J.; Bala, M.; Ball, C. G.; Banks, P. A.; Baron, T. H.; Barreto, S. G.; Basaranoglu, M.; Beger, H. G.; Bernal Monterde, V.; Besselink, M. G.; Bharwani, N.; Bhasin, D. K.; Bong, J. J.; Botoi, G.; Bruennler, T.; Bruno, M. J.; Cairoli, E.; Carter, C. R.; Cernea, D.; Chari, S. T.; Chooklin, S.

    2014-01-01

    Objective: To develop a new classification of acute pancreatitis severity on the basis of a sound conceptual framework, comprehensive review of the published evidence, and worldwide consultation. Backgrounds: The Atlanta definitions of acute pancreatitis severity are ingrained in the lexicon of

  7. Learning Word Embeddings with Chi-Square Weights for Healthcare Tweet Classification

    Directory of Open Access Journals (Sweden)

    Sicong Kuang

    2017-08-01

    Full Text Available Twitter is a popular source for the monitoring of healthcare information and public disease. However, there exists much noise in the tweets. Even though appropriate keywords appear in the tweets, they do not guarantee the identification of a truly health-related tweet. Thus, the traditional keyword-based classification task is largely ineffective. Algorithms for word embeddings have proved to be useful in many natural language processing (NLP tasks. We introduce two algorithms based on an existing word embedding learning algorithm: the continuous bag-of-words model (CBOW. We apply the proposed algorithms to the task of recognizing healthcare-related tweets. In the CBOW model, the vector representation of words is learned from their contexts. To simplify the computation, the context is represented by an average of all words inside the context window. However, not all words in the context window contribute equally to the prediction of the target word. Greedily incorporating all the words in the context window will largely limit the contribution of the useful semantic words and bring noisy or irrelevant words into the learning process, while existing word embedding algorithms also try to learn a weighted CBOW model. Their weights are based on existing pre-defined syntactic rules while ignoring the task of the learned embedding. We propose learning weights based on the words’ relative importance in the classification task. Our intuition is that such learned weights place more emphasis on words that have comparatively more to contribute to the later task. We evaluate the embeddings learned from our algorithms on two healthcare-related datasets. The experimental results demonstrate that embeddings learned from the proposed algorithms outperform existing techniques by a relative accuracy improvement of over 9%.

  8. Joint learning and weighting of visual vocabulary for bag-of-feature based tissue classification

    KAUST Repository

    Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

    2013-01-01

    their power in this field. Two important issues of bag-of-feature strategy for tissue classification are investigated in this paper: the visual vocabulary learning and weighting, which are always considered independently in traditional methods by neglecting

  9. Focal liver lesions segmentation and classification in nonenhanced T2-weighted MRI.

    Science.gov (United States)

    Gatos, Ilias; Tsantis, Stavros; Karamesini, Maria; Spiliopoulos, Stavros; Karnabatidis, Dimitris; Hazle, John D; Kagadis, George C

    2017-07-01

    To automatically segment and classify focal liver lesions (FLLs) on nonenhanced T2-weighted magnetic resonance imaging (MRI) scans using a computer-aided diagnosis (CAD) algorithm. 71 FLLs (30 benign lesions, 19 hepatocellular carcinomas, and 22 metastases) on T2-weighted MRI scans were delineated by the proposed CAD scheme. The FLL segmentation procedure involved wavelet multiscale analysis to extract accurate edge information and mean intensity values for consecutive edges computed using horizontal and vertical analysis that were fed into the subsequent fuzzy C-means algorithm for final FLL border extraction. Texture information for each extracted lesion was derived using 42 first- and second-order textural features from grayscale value histogram, co-occurrence, and run-length matrices. Twelve morphological features were also extracted to capture any shape differentiation between classes. Feature selection was performed with stepwise multilinear regression analysis that led to a reduced feature subset. A multiclass Probabilistic Neural Network (PNN) classifier was then designed and used for lesion classification. PNN model evaluation was performed using the leave-one-out (LOO) method and receiver operating characteristic (ROC) curve analysis. The mean overlap between the automatically segmented FLLs and the manual segmentations performed by radiologists was 0.91 ± 0.12. The highest classification accuracies in the PNN model for the benign, hepatocellular carcinoma, and metastatic FLLs were 94.1%, 91.4%, and 94.1%, respectively, with sensitivity/specificity values of 90%/97.3%, 89.5%/92.2%, and 90.9%/95.6% respectively. The overall classification accuracy for the proposed system was 90.1%. Our diagnostic system using sophisticated FLL segmentation and classification algorithms is a powerful tool for routine clinical MRI-based liver evaluation and can be a supplement to contrast-enhanced MRI to prevent unnecessary invasive procedures. © 2017 American

  10. Severity of Airflow Obstruction in Chronic Obstructive Pulmonary Disease (COPD): Proposal for a New Classification.

    Science.gov (United States)

    Coton, Sonia; Vollmer, William M; Bateman, Eric; Marks, Guy B; Tan, Wan; Mejza, Filip; Juvekar, Sanjay; Janson, Christer; Mortimer, Kevin; P A, Mahesh; Buist, A Sonia; Burney, Peter G J

    2017-10-01

    Current classifications of Chronic Obstructive Pulmonary Disease (COPD) severity are complex and do not grade levels of obstruction. Obstruction is a simpler construct and independent of ethnicity. We constructed an index of obstruction severity based on the FEV 1 /FVC ratio, with cut-points dividing the Burden of Obstructive Lung Disease (BOLD) study population into four similarly sized strata to those created by the GOLD criteria that uses FEV 1 . We measured the agreement between classifications and the validity of the FEV 1 -based classification in identifying the level of obstruction as defined by the new groupings. We compared the strengths of association of each classification with quality of life (QoL), MRC dyspnoea score and the self-reported exacerbation rate. Agreement between classifications was only fair. FEV 1 -based criteria for moderate COPD identified only 79% of those with moderate obstruction and misclassified half of the participants with mild obstruction as having more severe COPD. Both scales were equally strongly associated with QoL, exertional dyspnoea and respiratory exacerbations. Severity assessed using the FEV 1 /FVC ratio is only in moderate agreement with the severity assessed using FEV 1 but is equally strongly associated with other outcomes. Severity assessed using the FEV 1 /FVC ratio is likely to be independent of ethnicity.

  11. Discriminative clustering on manifold for adaptive transductive classification.

    Science.gov (United States)

    Zhang, Zhao; Jia, Lei; Zhang, Min; Li, Bing; Zhang, Li; Li, Fanzhang

    2017-10-01

    In this paper, we mainly propose a novel adaptive transductive label propagation approach by joint discriminative clustering on manifolds for representing and classifying high-dimensional data. Our framework seamlessly combines the unsupervised manifold learning, discriminative clustering and adaptive classification into a unified model. Also, our method incorporates the adaptive graph weight construction with label propagation. Specifically, our method is capable of propagating label information using adaptive weights over low-dimensional manifold features, which is different from most existing studies that usually predict the labels and construct the weights in the original Euclidean space. For transductive classification by our formulation, we first perform the joint discriminative K-means clustering and manifold learning to capture the low-dimensional nonlinear manifolds. Then, we construct the adaptive weights over the learnt manifold features, where the adaptive weights are calculated through performing the joint minimization of the reconstruction errors over features and soft labels so that the graph weights can be joint-optimal for data representation and classification. Using the adaptive weights, we can easily estimate the unknown labels of samples. After that, our method returns the updated weights for further updating the manifold features. Extensive simulations on image classification and segmentation show that our proposed algorithm can deliver the state-of-the-art performance on several public datasets. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. [Body weight evolution and classification of body weight in relation to the results of bariatric surgery: roux-en-Y gastric bypass].

    Science.gov (United States)

    Novais, Patrícia Fátima Sousa; Rasera Junior, Irineu; Leite, Celso Vieira de Souza; Oliveira, Maria Rita Marques de

    2010-03-01

    The objective of this study was to assess the evolution and classification of body weight in relation to the results of bariatric surgery in women who underwent the procedure more than two years ago. A total of 141 women underwent banded Roux-en-Y gastric bypass (RYGB). The participants were divided according to the time elapsed since surgery and the percentage of excess weight lost (%EWL): 75. The women in the group with %EWL 75 (36.2%) ranged from normal to pre-obese and presented lower late weight gain than the women in the other groups. Weight evolution two or more years after surgery showed the expected reductions, with some individuals responding better to surgery than others. This shows that it is necessary to monitor, investigate and intervene to obtain the desired results.

  13. Investigating Perceived vs. Medical Weight Status Classification among College Students: Room for Improvement Exists among the Overweight and Obese

    Science.gov (United States)

    Duffrin, Christopher; Eakin, Angela; Bertrand, Brenda; Barber-Heidel, Kimberly; Carraway-Stage, Virginia

    2011-01-01

    The American College Health Association estimated that 31% of college students are overweight or obese. It is important that students have a correct perception of body weight status as extra weight has potential adverse health effects. This study assessed accuracy of perceived weight status versus medical classification among 102 college students.…

  14. Resting and exercise energy metabolism in weight-reduced adults with severe obesity.

    Science.gov (United States)

    Hames, Kazanna C; Coen, Paul M; King, Wendy C; Anthony, Steven J; Stefanovic-Racic, Maja; Toledo, Frederico G S; Lowery, Jolene B; Helbling, Nicole L; Dubé, John J; DeLany, James P; Jakicic, John M; Goodpaster, Bret H

    2016-06-01

    To determine effects of physical activity (PA) with diet-induced weight loss on energy metabolism in adults with severe obesity. Adults with severe obesity (n = 11) were studied across 6 months of intervention, then compared with controls with less severe obesity (n = 7) or normal weight (n = 9). Indirect calorimetry measured energy metabolism during exercise and rest. Markers of muscle oxidation were determined by immunohistochemistry. Data were presented as medians. The intervention induced 7% weight loss (P = 0.001) and increased vigorous PA by 24 min/wk (P = 0.02). During exercise, energy expenditure decreased, efficiency increased (P ≤ 0.03), and fatty acid oxidation (FAO) did not change. Succinate dehydrogenase increased (P = 0.001), but fiber type remained the same. Post-intervention subjects' resting metabolism remained similar to controls. Efficiency was lower in post-intervention subjects compared with normal-weight controls exercising at 25 W (P ≤ 0.002) and compared with all controls exercising at 60% VO2peak (P ≤ 0.019). Resting and exercise FAO of post-intervention subjects remained similar to adults with less severe obesity. Succinate dehydrogenase and fiber type were similar across all body weight statuses. While metabolic adaptations to PA during weight loss occur in adults with severe obesity, FAO does not change. Resulting FAO during rest and exercise remains similar to adults with less severe obesity. © 2016 The Obesity Society.

  15. Weight loss alters severity of individual nocturnal respiratory events depending on sleeping position

    International Nuclear Information System (INIS)

    Kulkas, A; Leppänen, T; Tiihonen, P; Mervaala, E; Töyräs, J; Sahlman, J; Seppä, J; Kokkarinen, J; Randell, J; Tuomilehto, H

    2014-01-01

    Weight loss is an effective treatment for obstructive sleep apnea (OSA). The mechanisms of how weight loss affects nocturnal breathing are not fully understood. The severity of OSA is currently estimated by the number of respiratory events per hour of sleep (i.e. apnea-hypopnea-index, AHI). AHI neglects duration and morphology of individual respiratory events, which describe the severity of individual events. In the current paper, we investigate the novel Adjusted-AHI parameter (incorporating individual event severity) and AHI after weight loss in relation to sleeping position. It was hypothesised that there are positional differences in individual event severity changes during weight loss. Altogether, 32 successful (> 5% of weight) and 34 unsuccessful weight loss patients at baseline and after 1 year follow-up were analysed. The results revealed that individual respiratory event severity was reduced differently in supine and non-supine positions during weight loss. During weight loss, AHI was reduced by 54% (p = 0.004) and 74% (p < 0.001), while Adjusted-AHI was reduced by 14% (p = 0.454) and 48% (p = 0.003) in supine and non-supine positions, respectively. In conclusion, the severity of individual respiratory events decreased more in the non-supine position. The novel Adjusted-AHI parameter takes these changes into account and might therefore contribute additional information to the planning of treatment of OSA patients. (paper)

  16. Accuracy of automated classification of major depressive disorder as a function of symptom severity

    Directory of Open Access Journals (Sweden)

    Rajamannar Ramasubbu, MD, FRCPC, MSc

    2016-01-01

    Conclusions: Binary linear SVM classifiers achieved significant classification of very severe depression with resting-state fMRI, but the contribution of brain measurements may have limited potential in differentiating patients with less severe depression from healthy controls.

  17. Multispectral imaging burn wound tissue classification system: a comparison of test accuracies between several common machine learning algorithms

    Science.gov (United States)

    Squiers, John J.; Li, Weizhi; King, Darlene R.; Mo, Weirong; Zhang, Xu; Lu, Yang; Sellke, Eric W.; Fan, Wensheng; DiMaio, J. Michael; Thatcher, Jeffrey E.

    2016-03-01

    The clinical judgment of expert burn surgeons is currently the standard on which diagnostic and therapeutic decisionmaking regarding burn injuries is based. Multispectral imaging (MSI) has the potential to increase the accuracy of burn depth assessment and the intraoperative identification of viable wound bed during surgical debridement of burn injuries. A highly accurate classification model must be developed using machine-learning techniques in order to translate MSI data into clinically-relevant information. An animal burn model was developed to build an MSI training database and to study the burn tissue classification ability of several models trained via common machine-learning algorithms. The algorithms tested, from least to most complex, were: K-nearest neighbors (KNN), decision tree (DT), linear discriminant analysis (LDA), weighted linear discriminant analysis (W-LDA), quadratic discriminant analysis (QDA), ensemble linear discriminant analysis (EN-LDA), ensemble K-nearest neighbors (EN-KNN), and ensemble decision tree (EN-DT). After the ground-truth database of six tissue types (healthy skin, wound bed, blood, hyperemia, partial injury, full injury) was generated by histopathological analysis, we used 10-fold cross validation to compare the algorithms' performances based on their accuracies in classifying data against the ground truth, and each algorithm was tested 100 times. The mean test accuracy of the algorithms were KNN 68.3%, DT 61.5%, LDA 70.5%, W-LDA 68.1%, QDA 68.9%, EN-LDA 56.8%, EN-KNN 49.7%, and EN-DT 36.5%. LDA had the highest test accuracy, reflecting the bias-variance tradeoff over the range of complexities inherent to the algorithms tested. Several algorithms were able to match the current standard in burn tissue classification, the clinical judgment of expert burn surgeons. These results will guide further development of an MSI burn tissue classification system. Given that there are few surgeons and facilities specializing in burn care

  18. [Intractable diarrhoea and severe weight loss by roflumilast].

    Science.gov (United States)

    Horna, Oihana; Toyas, Carla

    2013-08-04

    Roflumilast is a recently marketed drug, indicated for maintenance treatment of severe chronic obstructive pulmonary disease associated with chronic bronchitis in adult patients with a history of frequent exacerbations as add on to bronchodilator treatment. The safety data of this drug have always been subjected to controversy and concerns. The Food and Drug Administration rejected the drug after the first evaluation, asking the company to clarify the adverse reactions during the investigation process, the European Medicines Agency approved the drug including a Risk Management Plan, designed to promote a safe use of the drug. During the first months after the marketing process, the Spanish Pharmacovigilance System has already been acquainted of several adverse events notifications; therefore, these patients may be closely monitored, mainly because of digestive and psychiatric disorders. Here we report the case of a female patient who showed a serious digestive clinical profile and a severe weight loss, more than 25% of her initial weight, when a treatment with roflumilast was started. The suspicion of a side effect as the cause of the reported clinical profile and its resolution required 3 hospital admissions. Copyright © 2013 Elsevier España, S.L. All rights reserved.

  19. Spatial Analysis of Accident Spots Using Weighted Severity Index ...

    African Journals Online (AJOL)

    ADOWIE PERE

    Spatial Analysis of Accident Spots Using Weighted Severity Index (WSI) and ... pedestrians avoiding the use of pedestrian bridges/aid even when they are available. ..... not minding an unforeseen obstruction, miscalculations and wrong break.

  20. Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study.

    Science.gov (United States)

    Vidić, Igor; Egnell, Liv; Jerome, Neil P; Teruel, Jose R; Sjøbakk, Torill E; Østlie, Agnes; Fjøsne, Hans E; Bathen, Tone F; Goa, Pål Erik

    2018-05-01

    Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentially be improved by machine learning. To evaluate classification of malignant and benign tumors and breast cancer subtypes using support vector machine (SVM). Prospective. Fifty-one patients with benign (n = 23) and malignant (n = 28) breast tumors (26 ER+, whereof six were HER2+). Patients were imaged with DW-MRI (3T) using twice refocused spin-echo echo-planar imaging with echo time / repetition time (TR/TE) = 9000/86 msec, 90 × 90 matrix size, 2 × 2 mm in-plane resolution, 2.5 mm slice thickness, and 13 b-values. Apparent diffusion coefficient (ADC), relative enhanced diffusivity (RED), and the intravoxel incoherent motion (IVIM) parameters diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The histogram properties (median, mean, standard deviation, skewness, kurtosis) were used as features in SVM (10-fold cross-validation) for differentiation of lesions and subtyping. Accuracies of the SVM classifications were calculated to find the combination of features with highest prediction accuracy. Mann-Whitney tests were performed for univariate comparisons. For benign versus malignant tumors, univariate analysis found 11 histogram properties to be significant differentiators. Using SVM, the highest accuracy (0.96) was achieved from a single feature (mean of RED), or from three feature combinations of IVIM or ADC. Combining features from all models gave perfect classification. No single feature predicted HER2 status of ER + tumors (univariate or SVM), although high accuracy (0.90) was achieved with SVM combining several features. Importantly, these features had to include higher-order statistics (kurtosis and skewness), indicating the importance to account for heterogeneity. Our

  1. The impact of weight classification on safety: timing steps to adapt to external constraints

    Science.gov (United States)

    Gill, S.V.

    2015-01-01

    Objectives: The purpose of the current study was to evaluate how weight classification influences safety by examining adults’ ability to meet a timing constraint: walking to the pace of an audio metronome. Methods: With a cross-sectional design, walking parameters were collected as 55 adults with normal (n=30) and overweight (n=25) body mass index scores walked to slow, normal, and fast audio metronome paces. Results: Between group comparisons showed that at the fast pace, those with overweight body mass index (BMI) had longer double limb support and stance times and slower cadences than the normal weight group (all psmetronome paces revealed that participants who were overweight had higher cadences at the slow and fast paces (all ps<0.05). Conclusions: Findings suggest that those with overweight BMI alter their gait to maintain biomechanical stability. Understanding how excess weight influences gait adaptation can inform interventions to improve safety for individuals with obesity. PMID:25730658

  2. Handling Imbalanced Data Sets in Multistage Classification

    Science.gov (United States)

    López, M.

    Multistage classification is a logical approach, based on a divide-and-conquer solution, for dealing with problems with a high number of classes. The classification problem is divided into several sequential steps, each one associated to a single classifier that works with subgroups of the original classes. In each level, the current set of classes is split into smaller subgroups of classes until they (the subgroups) are composed of only one class. The resulting chain of classifiers can be represented as a tree, which (1) simplifies the classification process by using fewer categories in each classifier and (2) makes it possible to combine several algorithms or use different attributes in each stage. Most of the classification algorithms can be biased in the sense of selecting the most populated class in overlapping areas of the input space. This can degrade a multistage classifier performance if the training set sample frequencies do not reflect the real prevalence in the population. Several techniques such as applying prior probabilities, assigning weights to the classes, or replicating instances have been developed to overcome this handicap. Most of them are designed for two-class (accept-reject) problems. In this article, we evaluate several of these techniques as applied to multistage classification and analyze how they can be useful for astronomy. We compare the results obtained by classifying a data set based on Hipparcos with and without these methods.

  3. Effectiveness of a Low-Calorie Weight Loss Program in Moderately and Severely Obese Patients

    Directory of Open Access Journals (Sweden)

    Julia K. Winkler

    2013-10-01

    Full Text Available Aims: To compare effectiveness of a 1-year weight loss program in moderately and severely obese patients. Methods: The study sample included 311 obese patients participating in a weight loss program, which comprised a 12-week weight reduction phase (low-calorie formula diet and a 40-week weight maintenance phase. Body weight and glucose and lipid values were determined at the beginning of the program as well as after the weight reduction and the weight maintenance phase. Participants were analyzed according to their BMI class at baseline (30-34.9 kg/m2; 35-39.9 kg/m2; 40-44.9 kg/m2; 45-49.9 kg/m2; ≥50 kg/m2. Furthermore, moderately obese patients (BMI 2 were compared to severely obese participants (BMI ≥ 40 kg/m2. Results: Out of 311 participants, 217 individuals completed the program. Their mean baseline BMI was 41.8 ± 0.5 kg/m2. Average weight loss was 17.9 ± 0.6%, resulting in a BMI of 34.3 ± 0.4 kg/m2 after 1 year (p Conclusion: 1-year weight loss intervention improves body weight as well as lipid and glucose metabolism not only in moderately, but also in severely obese individuals.

  4. A study of several CAD methods for classification of clustered microcalcifications

    Science.gov (United States)

    Wei, Liyang; Yang, Yongyi; Nishikawa, Robert M.; Jiang, Yulei

    2005-04-01

    In this paper we investigate several state-of-the-art machine-learning methods for automated classification of clustered microcalcifications (MCs), aimed to assisting radiologists for more accurate diagnosis of breast cancer in a computer-aided diagnosis (CADx) scheme. The methods we consider include: support vector machine (SVM), kernel Fisher discriminant (KFD), and committee machines (ensemble averaging and AdaBoost), most of which have been developed recently in statistical learning theory. We formulate differentiation of malignant from benign MCs as a supervised learning problem, and apply these learning methods to develop the classification algorithms. As input, these methods use image features automatically extracted from clustered MCs. We test these methods using a database of 697 clinical mammograms from 386 cases, which include a wide spectrum of difficult-to-classify cases. We use receiver operating characteristic (ROC) analysis to evaluate and compare the classification performance by the different methods. In addition, we also investigate how to combine information from multiple-view mammograms of the same case so that the best decision can be made by a classifier. In our experiments, the kernel-based methods (i.e., SVM, KFD) yield the best performance, significantly outperforming a well-established CADx approach based on neural network learning.

  5. New classification of geometric ventricular patterns in severe aortic stenosis: Could it be clinically useful?

    Science.gov (United States)

    Di Nora, Concetta; Cervesato, Eugenio; Cosei, Iulian; Ravasel, Andreea; Popescu, Bogdan A; Zito, Concetta; Carerj, Scipione; Antonini-Canterin, Francesco; Popescu, Andreea C

    2018-04-16

    In severe aortic stenosis, different left ventricle (LV) remodeling patterns as a response to pressure overload have distinct hemodynamic profiles, cardiac function, and outcomes. The most common classification considers LV relative wall thickness and LV mass index to create 4 different groups. A new classification including also end-diastolic volume index has been recently proposed. To describe the prevalence of the newly identified remodeling patterns in patients with severe aortic stenosis and to evaluate their clinical relevance according to symptoms. We analyzed 286 consecutive patients with isolated severe aortic stenosis. Current guidelines were used for echocardiographic evaluation. Symptoms were defined as the presence of angina, syncope, or NYHA class III-IV. The mean age was 75 ± 9 years, 156 patients (54%) were men, while 158 (55%) were symptomatic. According to the new classification, the most frequent remodeling pattern was concentric hypertrophy (57.3%), followed by mixed (18.9%) and dilated hypertrophy (8.4%). There were no patients with eccentric remodeling; only 4 patients had a normalLV geometry. Symptomatic patients showed significantly more mixed hypertrophy (P < .05), while the difference regarding the prevalence of the other patterns was not statistically significant. When we analyzed the distribution of the classic 4 patterns stratified by the presence of symptoms, however, we did not find a significant difference (P = .157). The new classification had refined the description of different cardiac geometric phenotypes that develop as a response to pressure overload. It might be superior to the classic 4 patterns in terms of association with symptoms. © 2018 Wiley Periodicals, Inc.

  6. Reliability assessment of AOSpine thoracolumbar spine injury classification system and Thoracolumbar Injury Classification and Severity Score (TLICS) for thoracolumbar spine injuries: results of a multicentre study.

    Science.gov (United States)

    Kaul, Rahul; Chhabra, Harvinder Singh; Vaccaro, Alexander R; Abel, Rainer; Tuli, Sagun; Shetty, Ajoy Prasad; Das, Kali Dutta; Mohapatra, Bibhudendu; Nanda, Ankur; Sangondimath, Gururaj M; Bansal, Murari Lal; Patel, Nishit

    2017-05-01

    The aim of this multicentre study was to determine whether the recently introduced AOSpine Classification and Injury Severity System has better interrater and intrarater reliability than the already existing Thoracolumbar Injury Classification and Severity Score (TLICS) for thoracolumbar spine injuries. Clinical and radiological data of 50 consecutive patients admitted at a single centre with a diagnosis of an acute traumatic thoracolumbar spine injury were distributed to eleven attending spine surgeons from six different institutions in the form of PowerPoint presentation, who classified them according to both classifications. After time span of 6 weeks, cases were randomly rearranged and sent again to same surgeons for re-classification. Interobserver and intraobserver reliability for each component of TLICS and new AOSpine classification were evaluated using Fleiss Kappa coefficient (k value) and Spearman rank order correlation. Moderate interrater and intrarater reliability was seen for grading fracture type and integrity of posterior ligamentous complex (Fracture type: k = 0.43 ± 0.01 and 0.59 ± 0.16, respectively, PLC: k = 0.47 ± 0.01 and 0.55 ± 0.15, respectively), and fair to moderate reliability (k = 0.29 ± 0.01 interobserver and 0.44+/0.10 intraobserver, respectively) for total score according to TLICS. Moderate interrater (k = 0.59 ± 0.01) and substantial intrarater reliability (k = 0.68 ± 0.13) was seen for grading fracture type regardless of subtype according to AOSpine classification. Near perfect interrater and intrarater agreement was seen concerning neurological status for both the classification systems. Recently proposed AOSpine classification has better reliability for identifying fracture morphology than the existing TLICS. Additional studies are clearly necessary concerning the application of these classification systems across multiple physicians at different level of training and trauma centers to evaluate not

  7. Incidence and severity of stage IV bronchopulmonary dysplasia in infants of very low birth weight

    International Nuclear Information System (INIS)

    Parker, B.R.; Stevens, S.K.; Northway, W.H.

    1989-01-01

    To evaluate the incidence and severity of stage IV bronchopulmonary dysplasia (BPD) in infants of very low birth weight, the authors reviewed the clinical course and chest radiographs of 138 such infants. In the lowest weight group ( 1500 g, 10%). However, the severity of BPD (graded by the Toce-Edwards Scale) was highest (6.3) in the < 1500-g group (< 900 g, 5.4; 1200-1500 g, 5.9). These data showed that, although the incidence of stage IV BVD significantly decreased as birth weight increased, the severity of chronic changes was independent of birth weight

  8. Accuracy of automated classification of major depressive disorder as a function of symptom severity.

    Science.gov (United States)

    Ramasubbu, Rajamannar; Brown, Matthew R G; Cortese, Filmeno; Gaxiola, Ismael; Goodyear, Bradley; Greenshaw, Andrew J; Dursun, Serdar M; Greiner, Russell

    2016-01-01

    Growing evidence documents the potential of machine learning for developing brain based diagnostic methods for major depressive disorder (MDD). As symptom severity may influence brain activity, we investigated whether the severity of MDD affected the accuracies of machine learned MDD-vs-Control diagnostic classifiers. Forty-five medication-free patients with DSM-IV defined MDD and 19 healthy controls participated in the study. Based on depression severity as determined by the Hamilton Rating Scale for Depression (HRSD), MDD patients were sorted into three groups: mild to moderate depression (HRSD 14-19), severe depression (HRSD 20-23), and very severe depression (HRSD ≥ 24). We collected functional magnetic resonance imaging (fMRI) data during both resting-state and an emotional-face matching task. Patients in each of the three severity groups were compared against controls in separate analyses, using either the resting-state or task-based fMRI data. We use each of these six datasets with linear support vector machine (SVM) binary classifiers for identifying individuals as patients or controls. The resting-state fMRI data showed statistically significant classification accuracy only for the very severe depression group (accuracy 66%, p = 0.012 corrected), while mild to moderate (accuracy 58%, p = 1.0 corrected) and severe depression (accuracy 52%, p = 1.0 corrected) were only at chance. With task-based fMRI data, the automated classifier performed at chance in all three severity groups. Binary linear SVM classifiers achieved significant classification of very severe depression with resting-state fMRI, but the contribution of brain measurements may have limited potential in differentiating patients with less severe depression from healthy controls.

  9. A Weighted Block Dictionary Learning Algorithm for Classification

    OpenAIRE

    Shi, Zhongrong

    2016-01-01

    Discriminative dictionary learning, playing a critical role in sparse representation based classification, has led to state-of-the-art classification results. Among the existing discriminative dictionary learning methods, two different approaches, shared dictionary and class-specific dictionary, which associate each dictionary atom to all classes or a single class, have been studied. The shared dictionary is a compact method but with lack of discriminative information; the class-specific dict...

  10. Tree mortality based fire severity classification for forest inventories: A Pacific Northwest national forests example

    Science.gov (United States)

    Thomas R. Whittier; Andrew N. Gray

    2016-01-01

    Determining how the frequency, severity, and extent of forest fires are changing in response to changes in management and climate is a key concern in many regions where fire is an important natural disturbance. In the USA the only national-scale fire severity classification uses satellite image changedetection to produce maps for large (>400 ha) fires, and is...

  11. Definitions for warning signs and signs of severe dengue according to the WHO 2009 classification: Systematic review of literature.

    Science.gov (United States)

    Morra, Mostafa Ebraheem; Altibi, Ahmed M A; Iqtadar, Somia; Minh, Le Huu Nhat; Elawady, Sameh Samir; Hallab, Asma; Elshafay, Abdelrahman; Omer, Omer Abedlbagi; Iraqi, Ahmed; Adhikari, Purushottam; Labib, Jonair Hussein; Elhusseiny, Khaled Mosaad; Elgebaly, Ahmed; Yacoub, Sophie; Huong, Le Thi Minh; Hirayama, Kenji; Huy, Nguyen Tien

    2018-04-24

    Since warning signs and signs of severe dengue are defined differently between studies, we conducted a systematic review on how researchers defined these signs. We conducted an electronic search in Scopus to identify relevant articles, using key words including dengue, "warning signs," "severe dengue," and "classification." A total of 491 articles were identified through this search strategy and were subsequently screened by 2 independent reviewers for definitions of any of the warning or severe signs in the 2009 WHO dengue classification. We included all original articles published in English after 2009, classifying dengue by the 2009 WHO classification or providing the additional definition or criterion of warning signs and severity (besides the information of 2009 WHO). Analysis of the extracted data from 44 articles showed wide variations among definitions and cutoff values used by physicians to classify patients diagnosed with dengue infection. The establishment of clear definitions for warning signs and severity is essential to prevent unnecessary hospitalization and harmonizing the interpretation and comparability of epidemiological studies dedicated to dengue infection. Copyright © 2018 John Wiley & Sons, Ltd.

  12. Non-invasive classification of severe sepsis and systemic inflammatory response syndrome using a nonlinear support vector machine: a preliminary study

    International Nuclear Information System (INIS)

    Tang, Collin H H; Savkin, Andrey V; Chan, Gregory S H; Middleton, Paul M; Bishop, Sarah; Lovell, Nigel H

    2010-01-01

    Sepsis has been defined as the systemic response to infection in critically ill patients, with severe sepsis and septic shock representing increasingly severe stages of the same disease. Based on the non-invasive cardiovascular spectrum analysis, this paper presents a pilot study on the potential use of the nonlinear support vector machine (SVM) in the classification of the sepsis continuum into severe sepsis and systemic inflammatory response syndrome (SIRS) groups. 28 consecutive eligible patients attending the emergency department with presumptive diagnoses of sepsis syndrome have participated in this study. Through principal component analysis (PCA), the first three principal components were used to construct the SVM feature space. The SVM classifier with a fourth-order polynomial kernel was found to have a better overall performance compared with the other SVM classifiers, showing the following classification results: sensitivity = 94.44%, specificity = 62.50%, positive predictive value = 85.00%, negative predictive value = 83.33% and accuracy = 84.62%. Our classification results suggested that the combinatory use of cardiovascular spectrum analysis and the proposed SVM classification of autonomic neural activity is a potentially useful clinical tool to classify the sepsis continuum into two distinct pathological groups of varying sepsis severity

  13. COMPARISON OF OXYGEN UPTAKE KINETICS AND OXYGEN DEFICIT IN SEVERELY OVERWEIGHT AND NORMAL WEIGHT ADOLESCENT FEMALES

    Directory of Open Access Journals (Sweden)

    Mark Loftin

    2005-12-01

    Full Text Available The purpose of this study was to determine if differences in oxygen uptake kinetics and oxygen deficit existed between normal weight and severely overweight adolescent girls. Subjects included 10 normal weight and 8 severely overweight girls. The participants performed a leg cycling VO2 peak test and a constant load leg cycling test at 80% of the ventilatory threshold (T-vent. In the constant workload test O2 kinetics as indicated by Phase I (VO2 L at 20 sec and Phase II time constants (t were determined. Also, the O2 deficit (VO2 L was measured. As expected significant differences were noted in body composition and VO2 peak relative to mass with normal weight body mass averaging 55.3 ± 7.0 kg, severely overweight 90.5 ± 18.0 kg, % fat normal weight 27.3 ± 3.9%, severely overweight 49.7 ± 4.9% and VO2 peak (ml·kg-1·min-1 normal weight 32.0 ± 2.7 and severely overweight 22.0 ± 5.3. VO2 peak (l·min-1 and T-vent (%VO2 max were similar between groups. Results revealed similar O2 kinetic responses between groups; phase I kinetics normal weight 0.72 ± 0.15 L; severely overweight 0.75 ± 0.13L, phase II (t normal weight 41.5 ± 21.3 sec; severely overweight 33.9 ± 22.7 sec. However, the O2 deficit was significantly higher in the severely overweight (0.75 ± 0.15L when compared to the normal weight group (0.34 ± 0.13L. Correlations ranged from r = -0.15 to 0.51 between VO2 peak (L·min-1 or fat weight and phase I, t and O2 deficit. These data generally support previous research concerning the independence of O2 uptake response and body size

  14. Postnatal weight gain modifies severity and functional outcome of oxygen-induced proliferative retinopathy.

    Science.gov (United States)

    Stahl, Andreas; Chen, Jing; Sapieha, Przemyslaw; Seaward, Molly R; Krah, Nathan M; Dennison, Roberta J; Favazza, Tara; Bucher, Felicitas; Löfqvist, Chatarina; Ong, Huy; Hellström, Ann; Chemtob, Sylvain; Akula, James D; Smith, Lois E H

    2010-12-01

    In clinical studies, postnatal weight gain is strongly associated with retinopathy of prematurity (ROP). However, animal studies are needed to investigate the pathophysiological mechanisms of how postnatal weight gain affects the severity of ROP. In the present study, we identify nutritional supply as one potent parameter that affects the extent of retinopathy in mice with identical birth weights and the same genetic background. Wild-type pups with poor postnatal nutrition and poor weight gain (PWG) exhibit a remarkably prolonged phase of retinopathy compared to medium weight gain or extensive weight gain pups. A high (r(2) = 0.83) parabolic association between postnatal weight gain and oxygen-induced retinopathy severity is observed, as is a significantly prolonged phase of proliferative retinopathy in PWG pups (20 days) compared with extensive weight gain pups (6 days). The extended retinopathy is concomitant with prolonged overexpression of retinal vascular endothelial growth factor in PWG pups. Importantly, PWG pups show low serum levels of nonfasting glucose, insulin, and insulin-like growth factor-1 as well as high levels of ghrelin in the early postoxygen-induced retinopathy phase, a combination indicative of poor metabolic supply. These differences translate into visual deficits in adult PWG mice, as demonstrated by impaired bipolar and proximal neuronal function. Together, these results provide evidence for a pathophysiological correlation between poor postnatal nutritional supply, slow weight gain, prolonged retinal vascular endothelial growth factor overexpression, protracted retinopathy, and reduced final visual outcome.

  15. Dengue disease severity in Indonesian children: An evaluation of the World Health Organization classification system

    NARCIS (Netherlands)

    T.E. Setiati (Tatty); A.T.A. Mairuhu; P. Koraka (Penelope); M. Supriatna (Mohamad); M.R. Mac Gillavry (Melvin); D.P.M. Brandjes (Dees); A.D.M.E. Osterhaus (Albert); J.W.M. van der Meer (Jos); E.C.M. van Gorp (Eric); A. Soemantri (Augustinus)

    2007-01-01

    textabstractBackground: Dengue disease severity is usually classified using criteria set up by the World Health Organization (WHO). We aimed to assess the diagnostic accuracy of the WHO classification system and modifications to this system, and evaluated their potential practical usefulness.

  16. Attribute Weighting Based K-Nearest Neighbor Using Gain Ratio

    Science.gov (United States)

    Nababan, A. A.; Sitompul, O. S.; Tulus

    2018-04-01

    K- Nearest Neighbor (KNN) is a good classifier, but from several studies, the result performance accuracy of KNN still lower than other methods. One of the causes of the low accuracy produced, because each attribute has the same effect on the classification process, while some less relevant characteristics lead to miss-classification of the class assignment for new data. In this research, we proposed Attribute Weighting Based K-Nearest Neighbor Using Gain Ratio as a parameter to see the correlation between each attribute in the data and the Gain Ratio also will be used as the basis for weighting each attribute of the dataset. The accuracy of results is compared to the accuracy acquired from the original KNN method using 10-fold Cross-Validation with several datasets from the UCI Machine Learning repository and KEEL-Dataset Repository, such as abalone, glass identification, haberman, hayes-roth and water quality status. Based on the result of the test, the proposed method was able to increase the classification accuracy of KNN, where the highest difference of accuracy obtained hayes-roth dataset is worth 12.73%, and the lowest difference of accuracy obtained in the abalone dataset of 0.07%. The average result of the accuracy of all dataset increases the accuracy by 5.33%.

  17. A supervised learning rule for classification of spatiotemporal spike patterns.

    Science.gov (United States)

    Lilin Guo; Zhenzhong Wang; Adjouadi, Malek

    2016-08-01

    This study introduces a novel supervised algorithm for spiking neurons that take into consideration synapse delays and axonal delays associated with weights. It can be utilized for both classification and association and uses several biologically influenced properties, such as axonal and synaptic delays. This algorithm also takes into consideration spike-timing-dependent plasticity as in Remote Supervised Method (ReSuMe). This paper focuses on the classification aspect alone. Spiked neurons trained according to this proposed learning rule are capable of classifying different categories by the associated sequences of precisely timed spikes. Simulation results have shown that the proposed learning method greatly improves classification accuracy when compared to the Spike Pattern Association Neuron (SPAN) and the Tempotron learning rule.

  18. Meal replacements as a weight loss tool in a population with severe mental illness.

    Science.gov (United States)

    Gelberg, Hollie A; Kwan, Crystal L; Mena, Shirley J; Erickson, Zachary D; Baker, Matthew R; Chamberlin, Valery; Nguyen, Charles; Rosen, Jennifer A; Shah, Chandresh; Ames, Donna

    2015-12-01

    Weight gain and worsening metabolic parameters are often side effects of antipsychotic medications used by individuals with severe mental illness. To address this, a randomized, controlled research study of a behavioral weight management program for individuals with severe mental illness was undertaken to assess its efficacy. Patients unable to meet weight loss goals during the first portion of the year-long study were given the option of using meal replacement shakes in an effort to assist with weight loss. Specific requirements for use of meal replacement shakes were specified in the study protocol; only five patients were able to use the shakes in accordance with the protocol and lose weight while improving metabolic parameters. Case studies of two subjects are presented, illustrating the challenges and obstacles they faced, as well as their successes. Taking responsibility for their own weight loss, remaining motivated through the end of the study, and incorporating the meal replacement shakes into a daily routine were factors found in common with these patients. Use of meal replacements shakes with this population may be effective. Published by Elsevier Ltd.

  19. Weight-Related Correlates of Psychological Dysregulation in Adolescent and Young Adult (AYA) Females with Severe Obesity

    Science.gov (United States)

    Gowey, Marissa A.; Reiter-Purtill, Jennifer; Becnel, Jennifer; Peugh, James; Mitchell, James E.; Zeller, Meg H.

    2016-01-01

    Objective Severe obesity is the fastest growing pediatric subgroup of excess weight levels. Psychological dysregulation (i.e., impairments in regulating cognitive, emotional, and/or behavioral processes) has been associated with obesity and poorer weight loss outcomes. The present study explored associations of dysregulation with weight-related variables among adolescent and young adult (AYA) females with severe obesity. Methods Fifty-four AYA females with severe obesity (MBMI=48.71 kg/m2; Mage=18.29, R=15–21 years; 59.3% White) completed self-report measures of psychological dysregulation and weight-related constructs including meal patterns, problematic eating behaviors, and body and weight dissatisfaction, as non-surgical comparison participants in a multi-site study of adolescent bariatric surgery outcomes. Pearson and bivariate correlations were conducted and stratified by age group to analyze associations between dysregulation subscales (affective, behavioral, cognitive) and weight-related variables. Results Breakfast was the most frequently skipped meal (consumed 3–4 times/week). Eating out was common (4–5 times/week) and mostly occurred at fast-food restaurants. Evening hyperphagia (61.11%) and eating in the absence of hunger (37.04%) were commonly endorsed, while unplanned eating (29.63%), a sense of loss of control over eating (22.22%), eating beyond satiety (22.22%), night eating (12.96%), and binge eating (11.11%) were less common. Almost half of the sample endorsed extreme weight dissatisfaction. Dysregulation was associated with most weight-related attitudes and behaviors of interest in young adults but select patterns emerged for adolescents. Conclusions Higher levels of psychological dysregulation are associated with greater BMI, problematic eating patterns and behaviors, and body dissatisfaction in AYA females with severe obesity. These findings have implications for developing novel intervention strategies for severe obesity in AYAs that may

  20. Weight classification does not influence the short-term endocrine or metabolic effects of high-fructose corn syrup-sweetened beverages.

    Science.gov (United States)

    Heden, Timothy D; Liu, Ying; Kearney, Monica L; Kanaley, Jill A

    2014-05-01

    Obesity and high-fructose corn syrup (HFCS)-sweetened beverages are associated with an increased risk of chronic disease, but it is not clear whether obese (Ob) individuals are more susceptible to the detrimental effects of HFCS-sweetened beverages. The purpose of this study was to examine the endocrine and metabolic effects of consuming HFCS-sweetened beverages, and whether weight classification (normal weight (NW) vs. Ob) influences these effects. Ten NW and 10 Ob men and women who habitually consumed ≤355 mL per day of sugar-sweetened beverages were included in this study. Initially, the participants underwent a 4-h mixed-meal test after a 12-h overnight fast to assess insulin sensitivity, pancreatic and gut endocrine responses, insulin secretion and clearance, and glucose, triacylglycerol, and cholesterol responses. Next, the participants consumed their normal diet ad libitum, with 1065 mL per day (117 g·day(-1)) of HFCS-sweetened beverages added for 2 weeks. After the intervention, the participants repeated the mixed-meal test. HFCS-sweetened beverages did not significantly alter body weight, insulin sensitivity, insulin secretion or clearance, or endocrine, glucose, lipid, or cholesterol responses in either NW or Ob individuals. Regardless of previous diet, Ob individuals, compared with NW individuals, had ∼28% lower physical activity levels, 6%-9% lower insulin sensitivity, 12%-16% lower fasting high-density-lipoprotein cholesterol concentrations, 84%-144% greater postprandial triacylglycerol concentrations, and 46%-79% greater postprandial insulin concentrations. Greater insulin responses were associated with reduced insulin clearance, and there were no differences in insulin secretion. These findings suggest that weight classification does not influence the short-term endocrine and metabolic effects of HFCS-sweetened beverages.

  1. Interpreting weightings of the peer assessment rating index and the discrepancy index across contexts on Chinese patients.

    Science.gov (United States)

    Liu, Siqi; Oh, Heesoo; Chambers, David William; Xu, Tianmin; Baumrind, Sheldon

    2018-04-06

    Determine optimal weightings of Peer Assessment Rating (PAR) index and Discrepancy Index (DI) for malocclusion severity assessment in Chinese orthodontic patients. Sixty-nine Chinese orthodontists assessed a full set of pre-treatment records from a stratified random sample of 120 subjects gathered from six university orthodontic centres. Using professional judgment as the outcome variable, multiple regression analyses were performed to derive customized weighting systems for the PAR index and DI, for all subjects and each Angle classification subgroup. Professional judgment was consistent, with an Intraclass Correlation Coefficient (ICC) of 0.995. The PAR index or DI can be reliably measured, with ICC = 0.959 and 0.990, respectively. The predictive accuracy of PAR index was greatly improved by the Chinese weighting process (from r = 0.431 to r = 0.788) with almost equal distribution in each Angle classification subgroup. The Chinese-weighted DI showed a higher predictive accuracy, at P = 0.01, compared with the PAR index (r = 0.851 versus r = 0.788). A better performance was found in the Class II group (r = 0.890) when compared to Class I (r = 0.736) and III (r = 0.785) groups. The Chinese-weighted PAR index and DI were capable of predicting 62 per cent and 73 per cent of total variance in the professional judgment of malocclusion severity in Chinese patients. Differential prediction across Angle classifications merits attention since different weighting formulas were found.

  2. Weight-related correlates of psychological dysregulation in adolescent and young adult (AYA) females with severe obesity.

    Science.gov (United States)

    Gowey, Marissa A; Reiter-Purtill, Jennifer; Becnel, Jennifer; Peugh, James; Mitchell, James E; Zeller, Meg H

    2016-04-01

    Severe obesity is the fastest growing pediatric subgroup of excess weight levels. Psychological dysregulation (i.e., impairments in regulating cognitive, emotional, and/or behavioral processes) has been associated with obesity and poorer weight loss outcomes. The present study explored associations of dysregulation with weight-related variables among adolescent and young adult (AYA) females with severe obesity. Fifty-four AYA females with severe obesity (MBMI = 48.71 kg/m(2); Mage = 18.29, R = 15-21 years; 59.3% White) completed self-report measures of psychological dysregulation and weight-related constructs including meal patterns, problematic eating behaviors, and body and weight dissatisfaction, as non-surgical comparison participants in a multi-site study of adolescent bariatric surgery outcomes. Pearson and bivariate correlations were conducted and stratified by age group to analyze associations between dysregulation subscales (affective, behavioral, cognitive) and weight-related variables. Breakfast was the most frequently skipped meal (consumed 3-4 times/week). Eating out was common (4-5 times/week) and mostly occurred at fast-food restaurants. Evening hyperphagia (61.11%) and eating in the absence of hunger (37.04%) were commonly endorsed, while unplanned eating (29.63%), a sense of loss of control over eating (22.22%), eating beyond satiety (22.22%), night eating (12.96%), and binge eating (11.11%) were less common. Almost half of the sample endorsed extreme weight dissatisfaction. Dysregulation was associated with most weight-related attitudes and behaviors of interest in young adults but select patterns emerged for adolescents. Higher levels of psychological dysregulation are associated with greater BMI, problematic eating patterns and behaviors, and body dissatisfaction in AYA females with severe obesity. These findings have implications for developing novel intervention strategies for severe obesity in AYAs that may have a multidimensional

  3. Weighted statistical parameters for irregularly sampled time series

    Science.gov (United States)

    Rimoldini, Lorenzo

    2014-01-01

    Unevenly spaced time series are common in astronomy because of the day-night cycle, weather conditions, dependence on the source position in the sky, allocated telescope time and corrupt measurements, for example, or inherent to the scanning law of satellites like Hipparcos and the forthcoming Gaia. Irregular sampling often causes clumps of measurements and gaps with no data which can severely disrupt the values of estimators. This paper aims at improving the accuracy of common statistical parameters when linear interpolation (in time or phase) can be considered an acceptable approximation of a deterministic signal. A pragmatic solution is formulated in terms of a simple weighting scheme, adapting to the sampling density and noise level, applicable to large data volumes at minimal computational cost. Tests on time series from the Hipparcos periodic catalogue led to significant improvements in the overall accuracy and precision of the estimators with respect to the unweighted counterparts and those weighted by inverse-squared uncertainties. Automated classification procedures employing statistical parameters weighted by the suggested scheme confirmed the benefits of the improved input attributes. The classification of eclipsing binaries, Mira, RR Lyrae, Delta Cephei and Alpha2 Canum Venaticorum stars employing exclusively weighted descriptive statistics achieved an overall accuracy of 92 per cent, about 6 per cent higher than with unweighted estimators.

  4. Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory.

    Science.gov (United States)

    Sacchet, Matthew D; Prasad, Gautam; Foland-Ross, Lara C; Thompson, Paul M; Gotlib, Ian H

    2015-01-01

    Recently, there has been considerable interest in understanding brain networks in major depressive disorder (MDD). Neural pathways can be tracked in the living brain using diffusion-weighted imaging (DWI); graph theory can then be used to study properties of the resulting fiber networks. To date, global abnormalities have not been reported in tractography-based graph metrics in MDD, so we used a machine learning approach based on "support vector machines" to differentiate depressed from healthy individuals based on multiple brain network properties. We also assessed how important specific graph metrics were for this differentiation. Finally, we conducted a local graph analysis to identify abnormal connectivity at specific nodes of the network. We were able to classify depression using whole-brain graph metrics. Small-worldness was the most useful graph metric for classification. The right pars orbitalis, right inferior parietal cortex, and left rostral anterior cingulate all showed abnormal network connectivity in MDD. This is the first use of structural global graph metrics to classify depressed individuals. These findings highlight the importance of future research to understand network properties in depression across imaging modalities, improve classification results, and relate network alterations to psychiatric symptoms, medication, and comorbidities.

  5. Apparent diffusion coefficient value of gastric cancer by diffusion-weighted imaging: Correlations with the histological differentiation and Lauren classification

    International Nuclear Information System (INIS)

    Liu, Song; Guan, Wenxian; Wang, Hao; Pan, Liang; Zhou, Zhuping; Yu, Haiping; Liu, Tian; Yang, Xiaofeng; He, Jian; Zhou, Zhengyang

    2014-01-01

    Highlights: • Gastric cancers’ ADC values were significantly lower than normal gastric wall. • Gastric adenocarcinomas with different differentiation had different ADC values. • Gastric adenocarcinomas’ ADC values correlated with histologic differentiations. • Gastric cancers’ ADC values correlated with Lauren classifications. • Mean ADC value was better than min ADC value in characterizing gastric cancers. - Abstract: Objective: The purpose of this study was to evaluate the correlations between histological differentiation and Lauren classification of gastric cancer and the apparent diffusion coefficient (ADC) value of diffusion weighted imaging (DWI). Materials and methods: Sixty-nine patients with gastric cancer lesions underwent preoperative magnetic resonance imaging (MRI) (3.0T) and surgical resection. DWI was obtained with a single-shot, echo-planar imaging sequence in the axial plane (b values: 0 and 1000 s/mm 2 ). Mean and minimum ADC values were obtained for each gastric cancer and normal gastric walls by two radiologists, who were blinded to the histological findings. Histological type, degree of differentiation and Lauren classification of each resected specimen were determined by one pathologist. Mean and minimum ADC values of gastric cancers with different histological types, degrees of differentiation and Lauren classifications were compared. Correlations between ADC values and histological differentiation and Lauren classification were analyzed. Results: The mean and minimum ADC values of gastric cancers, as a whole and separately, were significantly lower than those of normal gastric walls (all p values <0.001). There were significant differences in the mean and minimum ADC values among gastric cancers with different histological types, degrees of differentiation and Lauren classifications (p < 0.05). Mean and minimum ADC values correlated significantly (all p < 0.001) with histological differentiation (r = 0.564, 0.578) and Lauren

  6. Implementation of several mathematical algorithms to breast tissue density classification

    International Nuclear Information System (INIS)

    Quintana, C.; Redondo, M.; Tirao, G.

    2014-01-01

    The accuracy of mammographic abnormality detection methods is strongly dependent on breast tissue characteristics, where a dense breast tissue can hide lesions causing cancer to be detected at later stages. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. This paper presents the implementation and the performance of different mathematical algorithms designed to standardize the categorization of mammographic images, according to the American College of Radiology classifications. These mathematical techniques are based on intrinsic properties calculations and on comparison with an ideal homogeneous image (joint entropy, mutual information, normalized cross correlation and index Q) as categorization parameters. The algorithms evaluation was performed on 100 cases of the mammographic data sets provided by the Ministerio de Salud de la Provincia de Córdoba, Argentina—Programa de Prevención del Cáncer de Mama (Department of Public Health, Córdoba, Argentina, Breast Cancer Prevention Program). The obtained breast classifications were compared with the expert medical diagnostics, showing a good performance. The implemented algorithms revealed a high potentiality to classify breasts into tissue density categories. - Highlights: • Breast density classification can be obtained by suitable mathematical algorithms. • Mathematical processing help radiologists to obtain the BI-RADS classification. • The entropy and joint entropy show high performance for density classification

  7. Ancillary testing, diagnostic/classification criteria and severity grading in Behçet disease.

    Science.gov (United States)

    Okada, Annabelle A; Stanford, Miles; Tabbara, Khalid

    2012-12-01

    Since there is no pathognomonic clinical sign or laboratory test to distinguish Behçet disease from other uveitic entities, the diagnosis must be made based on characteristic ocular and systemic findings in the absence of evidence of other disease that can explain the findings. Ancillary tests, including ocular and brain imaging studies, are used to assess the severity of intraocular inflammation and systemic manifestations of Behçet disease, to identify latent infections and other medical conditions that might worsen with systemic treatment, and to monitor for adverse effects of drugs used. There are two diagnostic or classification criteria in general use by the uveitis community, one from Japan and one from an international group; both rely on a minimum number and/or combination of clinical findings to identify Behçet disease. Finally, several grading schemes have been proposed to assess severity of ocular disease and response to treatment.

  8. Identification and application of the valid wavelength bands for burnt area detection and fire severity classification using Landsat/TM data

    International Nuclear Information System (INIS)

    Maki, M.; Tamura, M.

    2003-01-01

    Firstly, by using Landsat Thematic Mapper (TM) imagery before and after forest fire, the valid wavelength bands for detecting burnt areas were examined and compared to NDVI. Secondly, by using the valid wavelength bands, mapping of burnt area and classification of fire severity were examined. The results show that (a) channel 4 and 7 were more sensitive than other channels for detecting burnt area, (b) BAI (Burnt Area Index) [(ch. 4-ch. 7)/(ch. 4+ch. 7)] was more useful than NDVI for detecting burnt areas, and (c) BAI imagery was more useful for classification of burn severity than NDVI imagery

  9. AN ADABOOST OPTIMIZED CCFIS BASED CLASSIFICATION MODEL FOR BREAST CANCER DETECTION

    Directory of Open Access Journals (Sweden)

    CHANDRASEKAR RAVI

    2017-06-01

    Full Text Available Classification is a Data Mining technique used for building a prototype of the data behaviour, using which an unseen data can be classified into one of the defined classes. Several researchers have proposed classification techniques but most of them did not emphasis much on the misclassified instances and storage space. In this paper, a classification model is proposed that takes into account the misclassified instances and storage space. The classification model is efficiently developed using a tree structure for reducing the storage complexity and uses single scan of the dataset. During the training phase, Class-based Closed Frequent ItemSets (CCFIS were mined from the training dataset in the form of a tree structure. The classification model has been developed using the CCFIS and a similarity measure based on Longest Common Subsequence (LCS. Further, the Particle Swarm Optimization algorithm is applied on the generated CCFIS, which assigns weights to the itemsets and their associated classes. Most of the classifiers are correctly classifying the common instances but they misclassify the rare instances. In view of that, AdaBoost algorithm has been used to boost the weights of the misclassified instances in the previous round so as to include them in the training phase to classify the rare instances. This improves the accuracy of the classification model. During the testing phase, the classification model is used to classify the instances of the test dataset. Breast Cancer dataset from UCI repository is used for experiment. Experimental analysis shows that the accuracy of the proposed classification model outperforms the PSOAdaBoost-Sequence classifier by 7% superior to other approaches like Naïve Bayes Classifier, Support Vector Machine Classifier, Instance Based Classifier, ID3 Classifier, J48 Classifier, etc.

  10. Support vector machine classification of Major Depressive Disorder using diffusion-weighted neuroimaging and graph theory

    Directory of Open Access Journals (Sweden)

    Matthew D Sacchet

    2015-02-01

    Full Text Available Recently there has been considerable interest in understanding brain networks in Major Depressive Disorder (MDD. Neural pathways can be tracked in the living brain using diffusion weighted imaging (DWI; graph theory can then be used to study properties of the resulting fiber networks. To date, global abnormalities have not been reported in tractography-based graph metrics in MDD, so we used a machine learning approach based on ‘support vector machines’ to differentiate depressed from healthy individuals based on multiple brain network properties. We also assessed how important specific graph metrics were for this differentiation. Finally, we conducted a local graph analysis to identify abnormal connectivity at specific nodes of the network. We were able to classify depression using whole-brain graph metrics. Small-worldness was the most useful graph metric for classification. The right pars orbitalis, right inferior parietal cortex, and left rostral anterior cingulate all showed abnormal network connectivity in MDD. This is the first use of structural global graph metrics to classify depressed individuals. These findings highlight the importance of future research to understand network properties in depression across imaging modalities, improve classification results, and relate network alterations to psychiatric symptoms, medication, and co-morbidities.

  11. Comparison of STIR turbo SE imaging and diffusion-weighted imaging of the lung: capability for detection and subtype classification of pulmonary adenocarcinomas

    Energy Technology Data Exchange (ETDEWEB)

    Koyama, Hisanobu; Ohno, Yoshiharu; Onishi, Yumiko; Matsumoto, Keiko; Nogami, Munenobu; Takenaka, Daisuke; Sugimura, Kazuro [Kobe University Graduate School of Medicine, Department of Radiology, Kobe, Hyogo (Japan); Aoyama, Nobukazu [Kobe University Hospital, Division of Radiology, Kobe (Japan); Nishio, Wataru [Kobe University Graduate School of Medicine, Division of Cardiovascular, Thoracic and Pediatric Surgery, Kobe (Japan); Ohbayashi, Chiho [Hyogo Cancer Center, Division of Pathology, Akashi (Japan)

    2010-04-15

    The aim of the study was to evaluate the diagnostic performance of diffusion-weighted imaging (DWI) for detection and subtype classification in pulmonary adenocarcinomas through comparison with short TI inversion recovery turbo spin-echo imaging sequence (STIR). Thirty-two patients (mean age, 65.2 years) with 33 adenocarcinomas (mean diameter, 27.6 mm) were enrolled in this study. The detection rates of both sequences were compared. The ADC values on DWI and the contrast ratio (CR) between cancer and muscle on STIR were measured and those were compared across subtype classifications. Finally, ROC-based positive tests were performed to differentiate subtype classifications, and differentiation capabilities were compared. The DWI detection rate [85% (28/33)] was significantly lower than that of STIR [100% (33/33), P < 0.05]. The ADC values showed no significant difference regarding subtype classification; however, the CRs of bronchio-alveolar carcinomas (BACs) were significantly lower than those of other types (P < 0.05). When threshold values for differentiating BACs from others were adapted, the sensitivity and accuracy of DWI were significantly lower than those of STIR (P < 0.05). For differentiating adenocarcinomas with mixed subtypes from those with no BA component, there were no significant differences between the two sequences. STIR is more sensitive for detection and subtype classification than DWI. (orig.)

  12. A new reliability allocation weight for reducing the occurrence of severe failure effects

    International Nuclear Information System (INIS)

    Kim, Kyungmee O.; Yang, Yoonjung; Zuo, Ming J.

    2013-01-01

    A reliability allocation weight is used during the early design stage of a system to apportion the system reliability requirement to its individual subsystems. Since some failures have serious effects on public safety, cost and environmental issues especially in a mission critical system, the failure effect must be considered as one of the important factors in determining the allocation weight. Previously, the risk priority number or the criticality number was used to consider the failure effect in the allocation weight. In this paper, we identify the limitations of the previous approach and propose a new allocation weight based on the subsystem failure severity and its relative frequency. An example is given to illustrate that the proposed method is more effective than the previous method for reducing the occurrence of the unacceptable failure effects in a newly designed system

  13. Boosted classification trees result in minor to modest improvement in the accuracy in classifying cardiovascular outcomes compared to conventional classification trees

    Science.gov (United States)

    Austin, Peter C; Lee, Douglas S

    2011-01-01

    Purpose: Classification trees are increasingly being used to classifying patients according to the presence or absence of a disease or health outcome. A limitation of classification trees is their limited predictive accuracy. In the data-mining and machine learning literature, boosting has been developed to improve classification. Boosting with classification trees iteratively grows classification trees in a sequence of reweighted datasets. In a given iteration, subjects that were misclassified in the previous iteration are weighted more highly than subjects that were correctly classified. Classifications from each of the classification trees in the sequence are combined through a weighted majority vote to produce a final classification. The authors' objective was to examine whether boosting improved the accuracy of classification trees for predicting outcomes in cardiovascular patients. Methods: We examined the utility of boosting classification trees for classifying 30-day mortality outcomes in patients hospitalized with either acute myocardial infarction or congestive heart failure. Results: Improvements in the misclassification rate using boosted classification trees were at best minor compared to when conventional classification trees were used. Minor to modest improvements to sensitivity were observed, with only a negligible reduction in specificity. For predicting cardiovascular mortality, boosted classification trees had high specificity, but low sensitivity. Conclusions: Gains in predictive accuracy for predicting cardiovascular outcomes were less impressive than gains in performance observed in the data mining literature. PMID:22254181

  14. Dairy cow disability weights.

    Science.gov (United States)

    McConnel, Craig S; McNeil, Ashleigh A; Hadrich, Joleen C; Lombard, Jason E; Garry, Franklyn B; Heller, Jane

    2017-08-01

    Over the past 175 years, data related to human disease and death have progressed to a summary measure of population health, the Disability-Adjusted Life Year (DALY). As dairies have intensified there has been no equivalent measure of the impact of disease on the productive life and well-being of animals. The development of a disease-adjusted metric requires a consistent set of disability weights that reflect the relative severity of important diseases. The objective of this study was to use an international survey of dairy authorities to derive disability weights for primary disease categories recorded on dairies. National and international dairy health and management authorities were contacted through professional organizations, dairy industry publications and conferences, and industry contacts. Estimates of minimum, most likely, and maximum disability weights were derived for 12 common dairy cow diseases. Survey participants were asked to estimate the impact of each disease on overall health and milk production. Diseases were classified from 1 (minimal adverse effects) to 10 (death). The data was modelled using BetaPERT distributions to demonstrate the variation in these dynamic disease processes, and to identify the most likely aggregated disability weights for each disease classification. A single disability weight was assigned to each disease using the average of the combined medians for the minimum, most likely, and maximum severity scores. A total of 96 respondents provided estimates of disability weights. The final disability weight values resulted in the following order from least to most severe: retained placenta, diarrhea, ketosis, metritis, mastitis, milk fever, lame (hoof only), calving trauma, left displaced abomasum, pneumonia, musculoskeletal injury (leg, hip, back), and right displaced abomasum. The peaks of the probability density functions indicated that for certain disease states such as retained placenta there was a relatively narrow range of

  15. Eighty Kilograms Weight Reduction in a Case of Obstructive Sleep Apnea with Several Comorbidities: Did the Conditions Improve?

    Directory of Open Access Journals (Sweden)

    Moein Foroughi

    2016-05-01

    Full Text Available Obstructive sleep apnea (OSA together with metabolic disorders is common in severely obese patients. Weight reduction is considered as a treatment modality in these cases while few of them can succeed in considerable weight loss. Here, we present a severely obese man with body mass index of 54 suffered from OSA, type 2 diabetes, hypothyroidism, and hypertension. He intentionally lost 80 kilograms weight during the 2-year follow-up. Diabetes and hypertension completely resolved with considerable improvement in OSA syndrome after this huge weight reduction.

  16. Severe obesity and comorbid condition impact on the weight-related quality of life of the adolescent patient.

    Science.gov (United States)

    Zeller, Meg H; Inge, Thomas H; Modi, Avani C; Jenkins, Todd M; Michalsky, Marc P; Helmrath, Michael; Courcoulas, Anita; Harmon, Carroll M; Rofey, Dana; Baughcum, Amy; Austin, Heather; Price, Karin; Xanthakos, Stavra A; Brandt, Mary L; Horlick, Mary; Buncher, Ralph

    2015-03-01

    To assess links between comorbid health status, severe excess weight, and weight-related quality of life (WRQOL) in adolescents with severe obesity and undergoing weight-loss surgery (WLS) to inform clinical care. Baseline (preoperative) data from Teen Longitudinal Assessment of Bariatric Surgery, a prospective multicenter observational study of 242 adolescents with severe obesity (MedianBMI = 50.5 kg/m(2); Meanage = 17.1; 75.6% female; 71.9% white) undergoing WLS, were used to examine the impact of demographics, body mass index (BMI), presence/absence of 16 comorbid conditions, and a cumulative comorbidity load (CLoad) index on WRQOL scores (Impact of Weight on Quality of Life-Kids). WRQOL was significantly lower than reference samples of healthy weight, overweight, and obese samples. Of 16 comorbid conditions, the most prevalent were dyslipidemia (74.4%), chronic pain (58.3%), and obstructive sleep apnea (56.6%). Male subjects had a greater CLoad (P = .01) and BMI (P = .01), yet less impairment in total WRQOL (P conditions (eg, stress urinary incontinence) also emerged as contributors to lower WRQOL. WRQOL impairment is substantial for adolescents with severe obesity undergoing WLS, with predictors varying by sex. These patient-data highlight targets for education, support, and adjunctive care referrals before WLS. Furthermore, they provide a comprehensive empirical base for understanding heterogeneity in adolescent WRQOL outcomes after WLS, as weight and comorbidity profiles change over time. Copyright © 2015 Elsevier Inc. All rights reserved.

  17. Respiratory severity score and extubation readiness in very low birth weight infants

    Directory of Open Access Journals (Sweden)

    Maroun J. Mhanna

    2017-12-01

    Full Text Available Background: The respiratory severity score (RSS is a byproduct of mean airway pressure (MAP and fraction of inspired oxygen (FiO2. We sought to determine whether RSS could be used as a screening tool to predict extubation readiness in very low birth weight (VLBW infants. Methods: In a retrospective cohort study, medical records of all VLBW infants admitted to our unit (6/1/09–2/28/12 were reviewed for infants' demographics, prenatal characteristics, and medication use. Also, records were reviewed for unplanned vs. planned extubation, blood gas, ventilator parameters and signs of severe respiratory failure [RF, defined as partial pressure of carbon dioxide (pCO2 > 65, pH  50%, and MAP > 10 cm] on the day of extubation. Results: During the study period 31% (45/147 failed extubation. Overall, infants who failed extubation had a lower birth weight (BW and gestational age (GA, and on the day of extubation had a higher RSS and percentage of having one or more signs of severe RF. In a logistic regression model, adjusting for BW, GA, RSS and RF, RSS remained the only risk factor associated with extubation failure [adjusted OR 1.63 (95% CI: 1.10–2.40; p = 0.01]. RSS had a sensitivity of 0.86 (95% CI: 0.72–0.94 at a cutoff of 1.26 and a specificity of 0.88 (95% CI: 0.80–0.94 at a cutoff of 2.5. There was no difference in extubation failure between unplanned vs. planned extubation [41% (9/22 vs. 29% (36/125; p = 0.25]. Conclusion: An elevated RSS is associated with extubation failure. Successful unplanned extubation is common in VLBW infants. Key Words: very low birth weight, extubation, mechanical ventilation, respiratory severity score

  18. Occupancy classification of position weight matrix-inferred transcription factor binding sites.

    Directory of Open Access Journals (Sweden)

    Hollis Wright

    Full Text Available BACKGROUND: Computational prediction of Transcription Factor Binding Sites (TFBS from sequence data alone is difficult and error-prone. Machine learning techniques utilizing additional environmental information about a predicted binding site (such as distances from the site to particular chromatin features to determine its occupancy/functionality class show promise as methods to achieve more accurate prediction of true TFBS in silico. We evaluate the Bayesian Network (BN and Support Vector Machine (SVM machine learning techniques on four distinct TFBS data sets and analyze their performance. We describe the features that are most useful for classification and contrast and compare these feature sets between the factors. RESULTS: Our results demonstrate good performance of classifiers both on TFBS for transcription factors used for initial training and for TFBS for other factors in cross-classification experiments. We find that distances to chromatin modifications (specifically, histone modification islands as well as distances between such modifications to be effective predictors of TFBS occupancy, though the impact of individual predictors is largely TF specific. In our experiments, Bayesian network classifiers outperform SVM classifiers. CONCLUSIONS: Our results demonstrate good performance of machine learning techniques on the problem of occupancy classification, and demonstrate that effective classification can be achieved using distances to chromatin features. We additionally demonstrate that cross-classification of TFBS is possible, suggesting the possibility of constructing a generalizable occupancy classifier capable of handling TFBS for many different transcription factors.

  19. An Improved Rotation Forest for Multi-Feature Remote-Sensing Imagery Classification

    Directory of Open Access Journals (Sweden)

    Yingchang Xiu

    2017-11-01

    Full Text Available Multi-feature, especially multi-temporal, remote-sensing data have the potential to improve land cover classification accuracy. However, sometimes it is difficult to utilize all the features efficiently. To enhance classification performance based on multi-feature imagery, an improved rotation forest, combining Principal Component Analysis (PCA and a boosting naïve Bayesian tree (NBTree, is proposed. First, feature extraction was carried out with PCA. The feature set was randomly split into several disjoint subsets; then, PCA was applied to each subset, and new training data for linear extracted features based on original training data were obtained. These steps were repeated several times. Second, based on the new training data, a boosting naïve Bayesian tree was constructed as the base classifier, which aims to achieve lower prediction error than a decision tree in the original rotation forest. At the classification phase, the improved rotation forest has two-layer voting. It first obtains several predictions through weighted voting in a boosting naïve Bayesian tree; then, the first-layer vote predicts by majority to obtain the final result. To examine the classification performance, the improved rotation forest was applied to multi-feature remote-sensing images, including MODIS Enhanced Vegetation Index (EVI imagery time series, MODIS Surface Reflectance products and ancillary data in Shandong Province for 2013. The EVI imagery time series was preprocessed using harmonic analysis of time series (HANTS to reduce the noise effects. The overall accuracy of the final classification result was 89.17%, and the Kappa coefficient was 0.71, which outperforms the original rotation forest and other classifier ensemble results, as well as the NASA land cover product. However, this new algorithm requires more computational time, meaning the efficiency needs to be further improved. Generally, the improved rotation forest has a potential advantage in

  20. Memristive Perceptron for Combinational Logic Classification

    Directory of Open Access Journals (Sweden)

    Lidan Wang

    2013-01-01

    Full Text Available The resistance of the memristor depends upon the past history of the input current or voltage; so it can function as synapse in neural networks. In this paper, a novel perceptron combined with the memristor is proposed to implement the combinational logic classification. The relationship between the memristive conductance change and the synapse weight update is deduced, and the memristive perceptron model and its synaptic weight update rule are explored. The feasibility of the novel memristive perceptron for implementing the combinational logic classification (NAND, NOR, XOR, and NXOR is confirmed by MATLAB simulation.

  1. One-Year Behavioral Treatment of Obesity: Comparison of Moderate and Severe Caloric Restriction and the Effects of Weight Maintenance Therapy.

    Science.gov (United States)

    Wadden, Thomas A.; And Others

    1994-01-01

    Compared weight losses of 49 obese women assigned to 52-week behavioral program combined with moderate or severe caloric restriction. Those in severe caloric restriction group lost significantly more weight during first 26 weeks but regained some weight. Reports of binge eating declined in both groups, and no relationship was observed between…

  2. [Classification of severely injured patients in the G-DRG System 2008].

    Science.gov (United States)

    Juhra, C; Franz, D; Roeder, N; Vordemvenne, T; Raschke, M J

    2009-05-01

    Since the introduction of a per-case reimbursement system in Germany (German Diagnosis-Related Groups, G-DRG), the correct reimbursement for the treatment of severely injured patients has been much debated. While the classification of a patient in a polytrauma DRG follows different rules than the usual clinical definition, leading to a high number of patients not grouped as severely injured by the system, the system was also criticized in 2005 for its shortcomings in financing the treatment of severely injured patients. The development of financial reimbursement will be discussed in this paper. 167 patients treated in 2006 and 2007 due to a severe injury at the University-Hospital Münster and grouped into a polytrauma-DRG were included in this study. For each patient, cost-equivalents were estimated. For those patients treated in 2007 (n=110), exact costs were calculated following the InEK cost-calculation method. The reimbursement was calculated using the G-DRG-Systems of 2007, 2008 and 2009. Cost-equivalents/costs and clinical parameters were correlated. A total of 167 patients treated in 2006 and 2007 for a severe injury at the Münster University Hospital and grouped into a polytrauma DRG were included in this study. Cost equivalents were estimated for each patient. For those patients treated in 2007 (n=110), exact costs were calculated following the InEK (Institute for the Hospital Remuneration System) cost calculation method. Reimbursement was calculated using the G-DRG systems of 2007, 2008 and 2009. Cost equivalents/costs and clinical parameters were correlated. With the ongoing development of the G-DRG system, reimbursement for the treatment of severely injured patient has improved, but the amount of underfinancing remains substantial. As treatment of severely injured patients must be reimbursed using the G-DRG system, this system must be further adapted to better meet the needs of severely injured patients. Parameters such as total surgery time, injury

  3. Web Page Classification Method Using Neural Networks

    Science.gov (United States)

    Selamat, Ali; Omatu, Sigeru; Yanagimoto, Hidekazu; Fujinaka, Toru; Yoshioka, Michifumi

    Automatic categorization is the only viable method to deal with the scaling problem of the World Wide Web (WWW). In this paper, we propose a news web page classification method (WPCM). The WPCM uses a neural network with inputs obtained by both the principal components and class profile-based features (CPBF). Each news web page is represented by the term-weighting scheme. As the number of unique words in the collection set is big, the principal component analysis (PCA) has been used to select the most relevant features for the classification. Then the final output of the PCA is combined with the feature vectors from the class-profile which contains the most regular words in each class before feeding them to the neural networks. We have manually selected the most regular words that exist in each class and weighted them using an entropy weighting scheme. The fixed number of regular words from each class will be used as a feature vectors together with the reduced principal components from the PCA. These feature vectors are then used as the input to the neural networks for classification. The experimental evaluation demonstrates that the WPCM method provides acceptable classification accuracy with the sports news datasets.

  4. Comparison of severity classification in Japanese patients with antineutrophil cytoplasmic antibody-associated vasculitis in a nationwide, prospective, inception cohort study.

    Science.gov (United States)

    Sada, Ken-Ei; Harigai, Masayoshi; Amano, Koichi; Atsumi, Tatsuya; Fujimoto, Shouichi; Yuzawa, Yukio; Takasaki, Yoshinari; Banno, Shogo; Sugihara, Takahiko; Kobayashi, Masaki; Usui, Joichi; Yamagata, Kunihiro; Homma, Sakae; Dobashi, Hiroaki; Tsuboi, Naotake; Ishizu, Akihiro; Sugiyama, Hitoshi; Okada, Yasunori; Arimura, Yoshihiro; Matsuo, Seiichi; Makino, Hirofumi

    2016-09-01

    To compare disease severity classification systems for six-month outcome prediction in patients with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV). Patients with newly diagnosed AAV from 53 tertiary institutions were enrolled. Six-month remission, overall survival, and end-stage renal disease (ESRD)-free survival were evaluated. According to the European Vasculitis Study Group (EUVAS)-defined disease severity, the 321 enrolled patients were classified as follows: 14, localized; 71, early systemic; 170, generalized; and 66, severe disease. According to the rapidly progressive glomerulonephritis (RPGN) clinical grading system, the patients were divided as follows: 60, grade I; 178, grade II; 66, grade III; and 12, grade IV. According to the Five-Factor Score (FFS) 2009, 103, 109, and 109 patients had ≤1, 2, and ≥3 points, respectively. No significant difference in remission rates was found in any severity classification. The overall and ESRD-free survival rates significantly differed between grades I/II, III, and IV, regardless of renal involvement. Severe disease was a good predictor of six-month overall and ESRD-free survival. The FFS 2009 was useful to predict six-month ESRD-free survival but not overall survival. The RPGN grading system was more useful to predict six-month overall and ESRD-free survival than the EUVAS-defined severity or FFS 2009.

  5. Bosniak Classification system

    DEFF Research Database (Denmark)

    Graumann, Ole; Osther, Susanne Sloth; Karstoft, Jens

    2014-01-01

    Background: The Bosniak classification is a diagnostic tool for the differentiation of cystic changes in the kidney. The process of categorizing renal cysts may be challenging, involving a series of decisions that may affect the final diagnosis and clinical outcome such as surgical management....... Purpose: To investigate the inter- and intra-observer agreement among experienced uroradiologists when categorizing complex renal cysts according to the Bosniak classification. Material and Methods: The original categories of 100 cystic renal masses were chosen as “Gold Standard” (GS), established...... to the calculated weighted κ all readers performed “very good” for both inter-observer and intra-observer variation. Most variation was seen in cysts catagorized as Bosniak II, IIF, and III. These results show that radiologists who evaluate complex renal cysts routinely may apply the Bosniak classification...

  6. Fuzzy One-Class Classification Model Using Contamination Neighborhoods

    Directory of Open Access Journals (Sweden)

    Lev V. Utkin

    2012-01-01

    Full Text Available A fuzzy classification model is studied in the paper. It is based on the contaminated (robust model which produces fuzzy expected risk measures characterizing classification errors. Optimal classification parameters of the models are derived by minimizing the fuzzy expected risk. It is shown that an algorithm for computing the classification parameters is reduced to a set of standard support vector machine tasks with weighted data points. Experimental results with synthetic data illustrate the proposed fuzzy model.

  7. A New Weighted Injury Severity Scoring System: Better Predictive Power for Pediatric Trauma Mortality.

    Science.gov (United States)

    Shi, Junxin; Shen, Jiabin; Caupp, Sarah; Wang, Angela; Nuss, Kathryn E; Kenney, Brian; Wheeler, Krista K; Lu, Bo; Xiang, Henry

    2018-05-02

    An accurate injury severity measurement is essential for the evaluation of pediatric trauma care and outcome research. The traditional Injury Severity Score (ISS) does not consider the differential risks of the Abbreviated Injury Scale (AIS) from different body regions nor is it pediatric specific. The objective of this study was to develop a weighted injury severity scoring (wISS) system for pediatric blunt trauma patients with better predictive power than ISS. Based on the association between mortality and AIS from each of the six ISS body regions, we generated different weights for the component AIS scores used in the calculation of ISS. The weights and wISS were generated using the National Trauma Data Bank (NTDB). The Nationwide Emergency Department Sample (NEDS) was used to validate our main results. Pediatric blunt trauma patients less than 16 years were included, and mortality was the outcome. Discrimination (areas under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, concordance) and calibration (Hosmer-Lemeshow statistic) were compared between the wISS and ISS. The areas under the receiver operating characteristic curves from the wISS and ISS are 0.88 vs. 0.86 in ISS=1-74 and 0.77 vs. 0.64 in ISS=25-74 (ppredictive value, negative predictive value, and concordance when they were compared at similar levels of sensitivity. The wISS had better calibration (smaller Hosmer-Lemeshow statistic) than the ISS (11.6 versus 19.7 for ISS=1-74 and 10.9 versus 12.6 for ISS= 25-74). The wISS showed even better discrimination with the NEDS. By weighting the AIS from different body regions, the wISS had significantly better predictive power for mortality than the ISS, especially in critically injured children.Level of Evidence and study typeLevel IV Prognostic/Epidemiological.

  8. A Population Survey in Italy Based on the ICF Classification: Recognizing Persons with Severe Disability

    Directory of Open Access Journals (Sweden)

    Matilde Leonardi

    2012-01-01

    Full Text Available Aim of this paper is to describe functioning of subjects with “severe disability” collected with a protocol based on the International Classification of Functioning, Disability, and Health. It included sections on body functions and structures (BF and BS, activities and participation (A&P, and environmental factors (EF. In A&P, performance without personal support (WPS was added to standard capacity and performance. Persons with severe disability were those reporting a number of very severe/complete problems in BF or in A&P-capacity superior to mean + 1SD. Correlations between BF and A&P and differences between capacity, performance-WPS, and performance were assessed with Spearman's coefficient. Out of 1051, 200 subjects were considered as severely disabled. Mild to moderate correlations between BF and A&P were reported (between 0.148 and 0.394 when the full range of impairments/limitations was taken into account; between 0.198 and 0.285 when only the severe impairments/limitations were taken into account; performance-WPS was less similar to performance than to capacity. Our approach enabled identifying subjects with “severe disability” and separating the effect of personal support from that of devices, policies, and service provision.

  9. Spectroscopic classification of transients

    DEFF Research Database (Denmark)

    Stritzinger, M. D.; Fraser, M.; Hummelmose, N. N.

    2017-01-01

    We report the spectroscopic classification of several transients based on observations taken with the Nordic Optical Telescope (NOT) equipped with ALFOSC, over the nights 23-25 August 2017.......We report the spectroscopic classification of several transients based on observations taken with the Nordic Optical Telescope (NOT) equipped with ALFOSC, over the nights 23-25 August 2017....

  10. Mild, moderate, meaningful? Examining the psychological and functioning correlates of DSM-5 eating disorder severity specifiers.

    Science.gov (United States)

    Gianini, Loren; Roberto, Christina A; Attia, Evelyn; Walsh, B Timothy; Thomas, Jennifer J; Eddy, Kamryn T; Grilo, Carlos M; Weigel, Thomas; Sysko, Robyn

    2017-08-01

    This study evaluated the DSM-5 severity specifiers for treatment-seeking groups of participants with anorexia nervosa (AN), the purging form of bulimia nervosa (BN), and binge-eating disorder (BED). Hundred and sixty-two participants with AN, 93 participants with BN, and 343 participants with BED were diagnosed using semi-structured interviews, sub-categorized using DSM-5 severity specifiers and compared on demographic and cross-sectional clinical measures. In AN, the number of previous hospitalizations and the duration of illness increased with severity, but there was no difference across severity groups on measures of eating pathology, depression, or measures of self-reported physical or emotional functioning. In BN, the level of eating concerns increased across the severity groups, but the groups did not differ on measures of depression, self-esteem, and most eating pathology variables. In BN, support was also found for an alternative severity classification scheme based upon number of methods of purging. In BED, levels of several measures of eating pathology and self-reported physical and emotional functioning increased across the severity groups. For BED, however, support was also found for an alternative severity classification scheme based upon overvaluation of shape and weight. Preliminary evidence was also found for a transdiagnostic severity index based upon overvaluation of shape and weight. Overall, these data show limited support for the DSM-5 severity specifiers for BN and modest support for the DSM-5 severity specifiers for AN and BED. © 2017 Wiley Periodicals, Inc.

  11. 42 CFR 412.513 - Patient classification system.

    Science.gov (United States)

    2010-10-01

    ... LTC-DRG classification system provides a LTC-DRG, and an appropriate weighting factor, for those cases... intermediary decides that a different LTC-DRG should be assigned, the case will be reviewed by the appropriate... 42 Public Health 2 2010-10-01 2010-10-01 false Patient classification system. 412.513 Section 412...

  12. APPLICATION OF MULTIPLE LOGISTIC REGRESSION, BAYESIAN LOGISTIC AND CLASSIFICATION TREE TO IDENTIFY THE SIGNIFICANT FACTORS INFLUENCING CRASH SEVERITY

    Directory of Open Access Journals (Sweden)

    MILAD TAZIK

    2017-11-01

    Full Text Available Identifying cases in which road crashes result in fatality or injury of drivers may help improve their safety. In this study, datasets of crashes happened in TehranQom freeway, Iran, were examined by three models (multiple logistic regression, Bayesian logistic and classification tree to analyse the contribution of several variables to fatal accidents. For multiple logistic regression and Bayesian logistic models, the odds ratio was calculated for each variable. The model which best suited the identification of accident severity was determined based on AIC and DIC criteria. Based on the results of these two models, rollover crashes (OR = 14.58, %95 CI: 6.8-28.6, not using of seat belt (OR = 5.79, %95 CI: 3.1-9.9, exceeding speed limits (OR = 4.02, %95 CI: 1.8-7.9 and being female (OR = 2.91, %95 CI: 1.1-6.1 were the most important factors in fatalities of drivers. In addition, the results of the classification tree model have verified the findings of the other models.

  13. Gynecomastia Classification for Surgical Management: A Systematic Review and Novel Classification System.

    Science.gov (United States)

    Waltho, Daniel; Hatchell, Alexandra; Thoma, Achilleas

    2017-03-01

    Gynecomastia is a common deformity of the male breast, where certain cases warrant surgical management. There are several surgical options, which vary depending on the breast characteristics. To guide surgical management, several classification systems for gynecomastia have been proposed. A systematic review was performed to (1) identify all classification systems for the surgical management of gynecomastia, and (2) determine the adequacy of these classification systems to appropriately categorize the condition for surgical decision-making. The search yielded 1012 articles, and 11 articles were included in the review. Eleven classification systems in total were ascertained, and a total of 10 unique features were identified: (1) breast size, (2) skin redundancy, (3) breast ptosis, (4) tissue predominance, (5) upper abdominal laxity, (6) breast tuberosity, (7) nipple malposition, (8) chest shape, (9) absence of sternal notch, and (10) breast skin elasticity. On average, classification systems included two or three of these features. Breast size and ptosis were the most commonly included features. Based on their review of the current classification systems, the authors believe the ideal classification system should be universal and cater to all causes of gynecomastia; be surgically useful and easy to use; and should include a comprehensive set of clinically appropriate patient-related features, such as breast size, breast ptosis, tissue predominance, and skin redundancy. None of the current classification systems appears to fulfill these criteria.

  14. A comparison of perceived and calculated weight status classification congruence between pre- and post-bariatric surgery patients.

    Science.gov (United States)

    Ferriby, Megan; Pratt, Keeley; Noria, Sabrena; Needleman, Bradley

    2017-08-01

    One prominent reason patients undergo bariatric surgery is to reduce their excess weight and body mass index. Weight status classifications (WSCs), based on calculated body mass index, organize patients into distinct groups (underweight, healthy weight, overweight, class I obesity, class II obesity, and class III obesity) for treatment recommendations, including surgery for patients with obesity. Bariatric patients' perceptions of their WSC is important to consider, because patients who accurately perceive their WSC presurgery have greater motivation for health behavior changes; alternatively, incongruence between perceived and calculated WSC could deter motivation and ultimately their health changes postsurgery. This study seeks to understand the congruence between patients' perceived and calculated WSC, and to determine if there are differences in congruence between groups of pre- or postsurgery, male and female, and emerging/early adulthood and middle/late adulthood patients. University Hospital. Self-report measures. Results indicate the presurgery patients were more congruent in their perceptions of WSC compared with their postsurgery peers and emerging/early adulthood patients were more congruent in their perceptions of WSC compared with middle/late adulthood patients. No gender differences emerged in the full sample, but when divided by surgical status, presurgery females reported more congruent perceptions of WSC compared with their postsurgery peers. Males did not differ in their rates of congruence. These rates of incongruence may suggest a need for assessment of patients' perceived WSC, particularly postsurgery. Published by Elsevier Inc.

  15. Perceived Physician-informed Weight Status Predicts Accurate Weight Self-Perception and Weight Self-Regulation in Low-income, African American Women.

    Science.gov (United States)

    Harris, Charlie L; Strayhorn, Gregory; Moore, Sandra; Goldman, Brian; Martin, Michelle Y

    2016-01-01

    Obese African American women under-appraise their body mass index (BMI) classification and report fewer weight loss attempts than women who accurately appraise their weight status. This cross-sectional study examined whether physician-informed weight status could predict weight self-perception and weight self-regulation strategies in obese women. A convenience sample of 118 low-income women completed a survey assessing demographic characteristics, comorbidities, weight self-perception, and weight self-regulation strategies. BMI was calculated during nurse triage. Binary logistic regression models were performed to test hypotheses. The odds of obese accurate appraisers having been informed about their weight status were six times greater than those of under-appraisers. The odds of those using an "approach" self-regulation strategy having been physician-informed were four times greater compared with those using an "avoidance" strategy. Physicians are uniquely positioned to influence accurate weight self-perception and adaptive weight self-regulation strategies in underserved women, reducing their risk for obesity-related morbidity.

  16. Classification of hematopoietic regions in out-of-phase T1-weighted images. A quantitative comparison study with T1-weighted and STIR images

    International Nuclear Information System (INIS)

    Amano, Yasuo; Amano, Maki; Kijima, Tetsuji; Kumazaki, Tatsuo

    1995-01-01

    The hematopoietic regions were classified into two groups on the basis of out-of-phase T 1 -weighted images (op-TlWI): regions with lower intensity than that of muscle (LH) and regions with intensity equal to or higher than that of muscle (HH). We quantitatively evaluated the differences in signal intensity between LH and HH in order to examine this classification. Forty-two hematopoietic areas in aplastic anemia were classified into two groups of 23 LH and 19 HH. The signal ratios of hematopoietic areas to muscle on TlWI and STIR were calculated, and the differences between LH and HH were statistically evaluated. The signal ratios of LH were significantly higher on TlWI and lower on STIR than those of HH (unpaired t-test, p<0.05). This result indicated that LH consisted of more hypocellular marrow than HH. Op-TlWI were useful in differentiating between LH and HH and defining the degree of hematopoiesis in aplastic anemia. (author)

  17. Association of newborn diseases with weight/length ratio and the adequacy of weight for gestational age

    Directory of Open Access Journals (Sweden)

    José Ricardo Dias Bertagnon

    2011-09-01

    Full Text Available Objective: To compare the frequencies of newborn diseases in thosenewborns classified according to a weight/length rate and thoseclassified by the adequacy weight for gestational age. Methods: Aretrospective cross-sectional study by record assessment was carriedout enclosing all the live newborns at Hospital Geral do Grajaú, fromSeptember to December, 2009 (n =577 classified according to therate weight/length and also to the adequacy weight for gestationalage. The 10 and 90 percentiles of the weight/length distribution, nowdesignated as “indices” were calculated leading to the followingclassification: low index, for newborns below 54.8 g/cm; high index,for those over 75.8 g/cm; and average index, for the remainingnewborns. According to the adequacy weight for gestational age thenewborns were designated as pre-term for gestational age; term smallfor gestational age; appropriate term and large term. In this samplethere were no small and large pre-term or post-term newborns. Majordiseases were related to the index and adequacy extracts by the χ2test for a contingency table. Results: A significant association wasfound among low index, pre-term for gestational age newborns andterm small for gestational age; between average index and appropriatefor gestational age term newborns; and high index with large termappropriate for gestational age newborns (p< 0.001. Hypoglycemia(3.4% was associated to both low and high indices, to appropriatefor gestational age preterm newborns and to small for gestational ageterm newborns. Sepsis (3.1% was associated to both low index andpre-term appropriate for gestational age newborns. The respiratorydistress syndrome (1.3% was associated to low index and pre-termappropriate for gestational age newborns. Other respiratory distresssyndromes (3.8% were associated to low and high indices but notto the adequacy for gestational age classification. Jaundice (14.9%was not associated to the studied classifications

  18. Effect of inhaled corticosteroid use on weight (BMI) in pediatric patients with moderate-severe asthma.

    Science.gov (United States)

    Han, Jennifer; Nguyen, John; Kim, Yuna; Geng, Bob; Romanowski, Gale; Alejandro, Lawrence; Proudfoot, James; Xu, Ronghui; Leibel, Sydney

    2018-04-19

    Assess the relationship between inhaled corticosteroid use (ICS) and weight (BMI) in pediatric patients with moderate-severe asthma. Assess if the number of emergency department (ED) visits correlates with overall BMI trajectory. Assess the trend of prescribing biologic therapy in pediatric patients with moderate-severe asthma and determine its relationship with weight (BMI). A retrospective chart review was performed on 93 pediatric patients with moderate-severe asthma to determine the relationship between ICS use and weight (BMI), biologic therapy and BMI, and number of ED visits and BMI trajectory. A mixed effects model was employed with the correlation between repeated measures accounted for through the random effects. There is a statistically significant increase of 0.369 kg/m 2 in BMI trajectory per year in subjects on high-dose steroids compared to an increase of 0.195 kg/m 2 in the low dose group (p BMI of subjects initiated on biologic therapy (omalizumab or mepolizumab) had a statistically significant decrease in BMI trajectory of 0.818 kg/m 2 per year (p BMI trajectory (p BMI trajectory; the higher the dose, the greater the projected BMI increase per year. Initiation of biologic therapy decreased BMI trajectory over time. Lastly, those with frequent ED visits had a higher BMI trend. Future prospective studies are warranted that further evaluate the potential metabolic impacts of ICS and assess the effects of biologic therapy on BMI.

  19. LDA boost classification: boosting by topics

    Science.gov (United States)

    Lei, La; Qiao, Guo; Qimin, Cao; Qitao, Li

    2012-12-01

    AdaBoost is an efficacious classification algorithm especially in text categorization (TC) tasks. The methodology of setting up a classifier committee and voting on the documents for classification can achieve high categorization precision. However, traditional Vector Space Model can easily lead to the curse of dimensionality and feature sparsity problems; so it affects classification performance seriously. This article proposed a novel classification algorithm called LDABoost based on boosting ideology which uses Latent Dirichlet Allocation (LDA) to modeling the feature space. Instead of using words or phrase, LDABoost use latent topics as the features. In this way, the feature dimension is significantly reduced. Improved Naïve Bayes (NB) is designed as the weaker classifier which keeps the efficiency advantage of classic NB algorithm and has higher precision. Moreover, a two-stage iterative weighted method called Cute Integration in this article is proposed for improving the accuracy by integrating weak classifiers into strong classifier in a more rational way. Mutual Information is used as metrics of weights allocation. The voting information and the categorization decision made by basis classifiers are fully utilized for generating the strong classifier. Experimental results reveals LDABoost making categorization in a low-dimensional space, it has higher accuracy than traditional AdaBoost algorithms and many other classic classification algorithms. Moreover, its runtime consumption is lower than different versions of AdaBoost, TC algorithms based on support vector machine and Neural Networks.

  20. Physical Activity and Sedentary Time Associations with Metabolic Health Across Weight Statuses in Children and Adolescents

    DEFF Research Database (Denmark)

    Kuzik, Nicholas; Carson, Valerie; Andersen, Lars Bo

    2017-01-01

    classification compared with metabolically healthy (MH) classification for the NW group. More MVPA was associated with lower odds of MU classification than MH classification for NW and overweight groups. For multinomial logistic regressions, more MVPA was associated with lower odds of MH-obesity classification......, as well as MU-NW, -overweight, and -obesity classifications, compared with the MH-NW group. Furthermore, more sedentary time was associated with higher odds of MU-NW classification compared with the MH-NW group. CONCLUSIONS: More MVPA was beneficial for metabolic health and weight status, whereas lower......OBJECTIVE: The aim of this study was to examine the prevalence of metabolic health across weight statuses and the associations of physical activity and sedentary time within and across metabolic health-weight status groups. METHODS: Six studies (n = 4,581) from the International Children...

  1. General regression and representation model for classification.

    Directory of Open Access Journals (Sweden)

    Jianjun Qian

    Full Text Available Recently, the regularized coding-based classification methods (e.g. SRC and CRC show a great potential for pattern classification. However, most existing coding methods assume that the representation residuals are uncorrelated. In real-world applications, this assumption does not hold. In this paper, we take account of the correlations of the representation residuals and develop a general regression and representation model (GRR for classification. GRR not only has advantages of CRC, but also takes full use of the prior information (e.g. the correlations between representation residuals and representation coefficients and the specific information (weight matrix of image pixels to enhance the classification performance. GRR uses the generalized Tikhonov regularization and K Nearest Neighbors to learn the prior information from the training data. Meanwhile, the specific information is obtained by using an iterative algorithm to update the feature (or image pixel weights of the test sample. With the proposed model as a platform, we design two classifiers: basic general regression and representation classifier (B-GRR and robust general regression and representation classifier (R-GRR. The experimental results demonstrate the performance advantages of proposed methods over state-of-the-art algorithms.

  2. Customer and performance rating in QFD using SVM classification

    Science.gov (United States)

    Dzulkifli, Syarizul Amri; Salleh, Mohd Najib Mohd; Leman, A. M.

    2017-09-01

    In a classification problem, where each input is associated to one output. Training data is used to create a model which predicts values to the true function. SVM is a popular method for binary classification due to their theoretical foundation and good generalization performance. However, when trained with noisy data, the decision hyperplane might deviate from optimal position because of the sum of misclassification errors in the objective function. In this paper, we introduce fuzzy in weighted learning approach for improving the accuracy of Support Vector Machine (SVM) classification. The main aim of this work is to determine appropriate weighted for SVM to adjust the parameters of learning method from a given set of noisy input to output data. The performance and customer rating in Quality Function Deployment (QFD) is used as our case study to determine implementing fuzzy SVM is highly scalable for very large data sets and generating high classification accuracy.

  3. Birth weight classification in gestational diabetes: is there an ideal chart?

    Directory of Open Access Journals (Sweden)

    Livia Silveira Mastella

    2017-01-01

    Full Text Available Introduction: Gestational diabetes mellitus (GDM is associated to increased rates of large for gestational age newborns and macrosomia. Several charts are used to classify birth weight. Is there an ideal chart to classify newborns of GDM mothers? Methods: We evaluated adequacy of birth weight of 332 neonates born to GDM mothers at Hospital de Clínicas de Porto Alegre, Brazil. Newborns were classified according to gestational age as small (SGA, adequate or large (LGA based on four charts: Alexander, Pedreira, INTERGROWTH 21st Project and SINASC-2012. The latter was built using data from a large national registry of 2012, the Born Alive National Surveillance System (Sistema de Informações de Nascidos Vivos – SINASC, which included 2.905,789 birth certificates. Frequencies of SGA and LGA and Kappa agreement were calculated. Results: In non-gender adjusted curves, SGA rates (95% confidence interval varied from 8% (5-11 to 9% (6-13; LGA rates, from 11% (8-15 to 17% (13-21. For males, SGA rates varied from 3% (1-6% to 6% (3-11%, and LGA rates, from 18% (13-24% to 31% (24-38%; for female, SGA rates were from 3% (1-7% to 10% (6-16% and LGA rates, from 11% (6-16% to 19% (13-26%. Kappa results were: ALEXANDER vs. SINASC-2012: 0.80 (0.73-0.88; INTERGROWTH 21st vs. SINASC-2012 (adjusted by sex: 0.62 (0.53-0.71; INTERGROWTH 21st vs. PEDREIRA: 0.71 (0.62-0.79; SINASC-2012 (by sex vs. PEDREIRA: 0.86 (0.79-0.93. Conclusions: Misclassification has to be taken into account when evaluating newborns of GDM mothers, as LGA rates can almost double depending on the chart used to classify birth weight.

  4. Injury severity in ice skating: an epidemiologic analysis using a standardised injury classification system.

    Science.gov (United States)

    Ostermann, Roman C; Hofbauer, Marcus; Tiefenböck, Thomas M; Pumberger, Matthias; Tiefenböck, Michael; Platzer, Patrick; Aldrian, Silke

    2015-01-01

    Although injuries sustained during ice skating have been reported to be more serious than other forms of skating, the potential injury risks are often underestimated by skating participants. The purpose of this study was to give a descriptive overview of injury patterns occurring during ice skating. Special emphasis was put on injury severity by using a standardised injury classification system. Over a six month period, all patients treated with ice-skating-related injuries at Europe's largest hospital were included. Patient demographics were collected and all injuries categorised according to the Abbreviated Injury Scale (AIS) 2005. A descriptive statistic and logistic regression analysis was performed. Three hundred and forty-one patients (134 M, 207 F) were included in this study. Statistical analysis revealed that age had a significant influence on injury severity. People > 50 years had a higher risk of sustaining a more severe injury according to the AIS compared with younger skaters. Furthermore, the risk of head injury was significantly lower for people aged between 18 and 50 years than for people  50 years than for people aged between 18 and 50 years (p = 0.04). The severity of ice-skating injuries is associated with the patient's age, showing more severe injuries in older patients. Awareness should be raised among the public and physicians about the risks associated with this activity in order to promote further educational interventions and the use of protective gear.

  5. Non-severe hypoglycaemia is associated with weight gain in patients with type 1 diabetes: Results from the Diabetes Control and Complication Trial.

    Science.gov (United States)

    Bumbu, Anisoara; Moutairou, Abdul; Matar, Odette; Fumeron, Frédéric; Velho, Gilberto; Riveline, Jean-Pierre; Gautier, Jean-François; Marre, Michel; Roussel, Ronan; Potier, Louis

    2018-05-01

    It is unclear whether the frequent non-severe episodes of hypoglycaemia observed during intensive glucose control in individuals with type 1 diabetes (T1D) are associated with subsequent weight gain. We analysed the association between non-severe hypoglycaemia and weight gain in 1441 Diabetes Control and Complication Trial (DCCT) participants. Non-severe hypoglycaemia was assessed by hypo-score (ie, number of blood glucose values gain. The annual weight gain by hypo-score tertiles was 0.8 ± 1.2 (T1), 1.3 ± 1.5 (T2) and 1.4 ± 1.3 kg/y (T3), P gain of 1.8 kg/y was 2.14 (95% CI, 1.56-2.93) for T2, and 2.53 (95%CI, 1.85-3.45) for T3 vs T1. These differences in weight gain and in risk of weight gain remained significant after adjustment for sex, age, duration of diabetes, HbA1c at baseline and treatment arms. In conclusion, our analysis shows a significant association between non-severe hypoglycaemia and weight gain in individuals with T1D from the DCCT. © 2017 John Wiley & Sons Ltd.

  6. Relationships between World Health Organization "International Classification of Functioning, Disability and Health" Constructs and Participation in Adults with Severe Mental Illness

    Science.gov (United States)

    Sánchez, Jennifer; Rosenthal, David A.; Chan, Fong; Brooks, Jessica; Bezyak, Jill L.

    2016-01-01

    Purpose: To examine the World Health Organization "International Classification of Functioning, Disability and Health" (ICF) constructs as correlates of community participation of people with severe mental illnesses (SMI). Methods: Quantitative descriptive research design using multiple regression and correlational techniques was used to…

  7. Gender classification under extended operating conditions

    Science.gov (United States)

    Rude, Howard N.; Rizki, Mateen

    2014-06-01

    Gender classification is a critical component of a robust image security system. Many techniques exist to perform gender classification using facial features. In contrast, this paper explores gender classification using body features extracted from clothed subjects. Several of the most effective types of features for gender classification identified in literature were implemented and applied to the newly developed Seasonal Weather And Gender (SWAG) dataset. SWAG contains video clips of approximately 2000 samples of human subjects captured over a period of several months. The subjects are wearing casual business attire and outer garments appropriate for the specific weather conditions observed in the Midwest. The results from a series of experiments are presented that compare the classification accuracy of systems that incorporate various types and combinations of features applied to multiple looks at subjects at different image resolutions to determine a baseline performance for gender classification.

  8. Comparison of Danish dichotomous and BI-RADS classifications of mammographic density.

    Science.gov (United States)

    Hodge, Rebecca; Hellmann, Sophie Sell; von Euler-Chelpin, My; Vejborg, Ilse; Andersen, Zorana Jovanovic

    2014-06-01

    In the Copenhagen mammography screening program from 1991 to 2001, mammographic density was classified either as fatty or mixed/dense. This dichotomous mammographic density classification system is unique internationally, and has not been validated before. To compare the Danish dichotomous mammographic density classification system from 1991 to 2001 with the density BI-RADS classifications, in an attempt to validate the Danish classification system. The study sample consisted of 120 mammograms taken in Copenhagen in 1991-2001, which tested false positive, and which were in 2012 re-assessed and classified according to the BI-RADS classification system. We calculated inter-rater agreement between the Danish dichotomous mammographic classification as fatty or mixed/dense and the four-level BI-RADS classification by the linear weighted Kappa statistic. Of the 120 women, 32 (26.7%) were classified as having fatty and 88 (73.3%) as mixed/dense mammographic density, according to Danish dichotomous classification. According to BI-RADS density classification, 12 (10.0%) women were classified as having predominantly fatty (BI-RADS code 1), 46 (38.3%) as having scattered fibroglandular (BI-RADS code 2), 57 (47.5%) as having heterogeneously dense (BI-RADS 3), and five (4.2%) as having extremely dense (BI-RADS code 4) mammographic density. The inter-rater variability assessed by weighted kappa statistic showed a substantial agreement (0.75). The dichotomous mammographic density classification system utilized in early years of Copenhagen's mammographic screening program (1991-2001) agreed well with the BI-RADS density classification system.

  9. Support for linguistic macrofamilies from weighted sequence alignment

    Science.gov (United States)

    Jäger, Gerhard

    2015-01-01

    Computational phylogenetics is in the process of revolutionizing historical linguistics. Recent applications have shed new light on controversial issues, such as the location and time depth of language families and the dynamics of their spread. So far, these approaches have been limited to single-language families because they rely on a large body of expert cognacy judgments or grammatical classifications, which is currently unavailable for most language families. The present study pursues a different approach. Starting from raw phonetic transcription of core vocabulary items from very diverse languages, it applies weighted string alignment to track both phonetic and lexical change. Applied to a collection of ∼1,000 Eurasian languages and dialects, this method, combined with phylogenetic inference, leads to a classification in excellent agreement with established findings of historical linguistics. Furthermore, it provides strong statistical support for several putative macrofamilies contested in current historical linguistics. In particular, there is a solid signal for the Nostratic/Eurasiatic macrofamily. PMID:26403857

  10. Standardizing foot-type classification using arch index values.

    Science.gov (United States)

    Wong, Christopher Kevin; Weil, Rich; de Boer, Emily

    2012-01-01

    The lack of a reliable classification standard for foot type makes drawing conclusions from existing research and clinical decisions difficult, since different foot types may move and respond to treatment differently. The purpose of this study was to determine interrater agreement for foot-type classification based on photo-box-derived arch index values. For this correlational study with two raters, a sample of 11 healthy volunteers with normal to obese body mass indices was recruited from both a community weight-loss programme and a programme in physical therapy. Arch index was calculated using AutoCAD software from footprint photographs obtained via mirrored photo-box. Classification as high-arched, normal, or low-arched foot type was based on arch index values. Reliability of the arch index was determined with intra-class correlations; agreement on foot-type classification was determined using quadratic weighted kappa (κw). Average arch index was 0.215 for one tester and 0.219 for the second tester, with an overall range of 0.017 to 0.370. Both testers classified 6 feet as low-arched, 9 feet as normal, and 7 feet as high-arched. Interrater reliability for the arch index was ICC=0.90; interrater agreement for foot-type classification was κw=0.923. Classification of foot type based on arch index values derived from plantar footprint photographs obtained via mirrored photo-box showed excellent reliability in people with varying BMI. Foot-type classification may help clinicians and researchers subdivide sample populations to better differentiate mobility, gait, or treatment effects among foot types.

  11. Standardizing Foot-Type Classification Using Arch Index Values

    Science.gov (United States)

    Weil, Rich; de Boer, Emily

    2012-01-01

    ABSTRACT Purpose: The lack of a reliable classification standard for foot type makes drawing conclusions from existing research and clinical decisions difficult, since different foot types may move and respond to treatment differently. The purpose of this study was to determine interrater agreement for foot-type classification based on photo-box-derived arch index values. Method: For this correlational study with two raters, a sample of 11 healthy volunteers with normal to obese body mass indices was recruited from both a community weight-loss programme and a programme in physical therapy. Arch index was calculated using AutoCAD software from footprint photographs obtained via mirrored photo-box. Classification as high-arched, normal, or low-arched foot type was based on arch index values. Reliability of the arch index was determined with intra-class correlations; agreement on foot-type classification was determined using quadratic weighted kappa (κw). Results: Average arch index was 0.215 for one tester and 0.219 for the second tester, with an overall range of 0.017 to 0.370. Both testers classified 6 feet as low-arched, 9 feet as normal, and 7 feet as high-arched. Interrater reliability for the arch index was ICC=0.90; interrater agreement for foot-type classification was κw=0.923. Conclusions: Classification of foot type based on arch index values derived from plantar footprint photographs obtained via mirrored photo-box showed excellent reliability in people with varying BMI. Foot-type classification may help clinicians and researchers subdivide sample populations to better differentiate mobility, gait, or treatment effects among foot types. PMID:23729964

  12. Direct Kernel Perceptron (DKP): ultra-fast kernel ELM-based classification with non-iterative closed-form weight calculation.

    Science.gov (United States)

    Fernández-Delgado, Manuel; Cernadas, Eva; Barro, Senén; Ribeiro, Jorge; Neves, José

    2014-02-01

    The Direct Kernel Perceptron (DKP) (Fernández-Delgado et al., 2010) is a very simple and fast kernel-based classifier, related to the Support Vector Machine (SVM) and to the Extreme Learning Machine (ELM) (Huang, Wang, & Lan, 2011), whose α-coefficients are calculated directly, without any iterative training, using an analytical closed-form expression which involves only the training patterns. The DKP, which is inspired by the Direct Parallel Perceptron, (Auer et al., 2008), uses a Gaussian kernel and a linear classifier (perceptron). The weight vector of this classifier in the feature space minimizes an error measure which combines the training error and the hyperplane margin, without any tunable regularization parameter. This weight vector can be translated, using a variable change, to the α-coefficients, and both are determined without iterative calculations. We calculate solutions using several error functions, achieving the best trade-off between accuracy and efficiency with the linear function. These solutions for the α coefficients can be considered alternatives to the ELM with a new physical meaning in terms of error and margin: in fact, the linear and quadratic DKP are special cases of the two-class ELM when the regularization parameter C takes the values C=0 and C=∞. The linear DKP is extremely efficient and much faster (over a vast collection of 42 benchmark and real-life data sets) than 12 very popular and accurate classifiers including SVM, Multi-Layer Perceptron, Adaboost, Random Forest and Bagging of RPART decision trees, Linear Discriminant Analysis, K-Nearest Neighbors, ELM, Probabilistic Neural Networks, Radial Basis Function neural networks and Generalized ART. Besides, despite its simplicity and extreme efficiency, DKP achieves higher accuracies than 7 out of 12 classifiers, exhibiting small differences with respect to the best ones (SVM, ELM, Adaboost and Random Forest), which are much slower. Thus, the DKP provides an easy and fast way

  13. Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.

    Science.gov (United States)

    Kim, Junghoe; Calhoun, Vince D; Shim, Eunsoo; Lee, Jong-Hwan

    2016-01-01

    Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was

  14. Intra- and interrater reliability of three different MRI grading and classification systems after acute hamstring injuries.

    Science.gov (United States)

    Wangensteen, Arnlaug; Tol, Johannes L; Roemer, Frank W; Bahr, Roald; Dijkstra, H Paul; Crema, Michel D; Farooq, Abdulaziz; Guermazi, Ali

    2017-04-01

    To assess and compare the intra- and interrater reliability of three different MRI grading and classification systems after acute hamstring injury. Male athletes (n=40) with clinical diagnosis of acute hamstring injury and MRI ≤5days were selected from a prospective cohort. Two radiologists independently evaluated the MRIs using standardised scoring form including the modified Peetrons grading system, the Chan acute muscle strain injury classification and the British Athletics Muscle Injury Classification. Intra-and interrater reliability was assessed with linear weighted kappa (κ) or unweighted Cohen's κ and percentage agreement was calculated. We observed 'substantial' to 'almost perfect' intra- (κ range 0.65-1.00) and interrater reliability (κ range 0.77-1.00) with percentage agreement 83-100% and 88-100%, respectively, for severity gradings, overall anatomical sites and overall classifications for the three MRI systems. We observed substantial variability (κ range -0.05 to 1.00) for subcategories within the Chan classification and the British Athletics Muscle Injury Classification, however, the prevalence of positive scorings was low for some subcategories. The modified Peetrons grading system, overall Chan classification and overall British Athletics Muscle Injury Classification demonstrated 'substantial' to 'almost perfect' intra- and interrater reliability when scored by experienced radiologists. The intra- and interrater reliability for the anatomical subcategories within the classifications remains unclear. Copyright © 2017 Elsevier B.V. All rights reserved.

  15. Effects of supervised Self Organising Maps parameters on classification performance.

    Science.gov (United States)

    Ballabio, Davide; Vasighi, Mahdi; Filzmoser, Peter

    2013-02-26

    Self Organising Maps (SOMs) are one of the most powerful learning strategies among neural networks algorithms. SOMs have several adaptable parameters and the selection of appropriate network architectures is required in order to make accurate predictions. The major disadvantage of SOMs is probably due to the network optimisation, since this procedure can be often time-expensive. Effects of network size, training epochs and learning rate on the classification performance of SOMs are known, whereas the effect of other parameters (type of SOMs, weights initialisation, training algorithm, topology and boundary conditions) are not so obvious. This study was addressed to analyse the effect of SOMs parameters on the network classification performance, as well as on their computational times, taking into consideration a significant number of real datasets, in order to achieve a comprehensive statistical comparison. Parameters were contemporaneously evaluated by means of an approach based on the design of experiments, which enabled the investigation of their interaction effects. Results highlighted the most important parameters which influence the classification performance and enabled the identification of the optimal settings, as well as the optimal architectures to reduce the computational time of SOMs. Copyright © 2012 Elsevier B.V. All rights reserved.

  16. Grading Dysphagia as a Toxicity of Head and Neck Cancer: Differences in Severity Classification Based on MBS DIGEST and Clinical CTCAE Grades.

    Science.gov (United States)

    Goepfert, Ryan P; Lewin, Jan S; Barrow, Martha P; Warneke, Carla L; Fuller, Clifton D; Lai, Stephen Y; Weber, Randal S; Hutcheson, Katherine A

    2018-04-01

    Clinician-reported toxicity grading through common terminology criteria for adverse events (CTCAE) stages dysphagia based on symptoms, diet, and tube dependence. The new dynamic imaging grade of swallowing toxicity (DIGEST) tool offers a similarly scaled five-point ordinal summary grade of pharyngeal swallowing as determined through results of a modified barium swallow (MBS) study. This study aims to inform clinicians on the similarities and differences between dysphagia severity according to clinical CTCAE and MBS-derived DIGEST grading. A cross-sectional sample of 95 MBS studies was randomly selected from a prospectively-acquired MBS database among patients treated with organ preservation strategies for head and neck cancer. MBS DIGEST and clinical CTCAE dysphagia grades were compared. DIGEST and CTCAE dysphagia grades had "fair" agreement per weighted κ of 0.358 (95% CI .231-.485). Using a threshold of DIGEST ≥ 3 as reference, CTCAE had an overall sensitivity of 0.50, specificity of 0.84, and area under the curve (AUC) of 0.67 to identify severe MBS-detected dysphagia. At less than 6 months, sensitivity was 0.72, specificity was 0.76, and AUC was 0.75 while at greater than 6 months, sensitivity was 0.22, specificity was 0.90, and AUC was 0.56 for CTCAE to detect dysphagia as determined by DIGEST. Classification of pharyngeal dysphagia on MBS using DIGEST augments our understanding of dysphagia severity according to the clinically-derived CTCAE while maintaining the simplicity of an ordinal scale. DIGEST likely complements CTCAE toxicity grading through improved specificity for physiologic dysphagia in the acute phase and improved sensitivity for dysphagia in the late-phase.

  17. Video genre classification using multimodal features

    Science.gov (United States)

    Jin, Sung Ho; Bae, Tae Meon; Choo, Jin Ho; Ro, Yong Man

    2003-12-01

    We propose a video genre classification method using multimodal features. The proposed method is applied for the preprocessing of automatic video summarization or the retrieval and classification of broadcasting video contents. Through a statistical analysis of low-level and middle-level audio-visual features in video, the proposed method can achieve good performance in classifying several broadcasting genres such as cartoon, drama, music video, news, and sports. In this paper, we adopt MPEG-7 audio-visual descriptors as multimodal features of video contents and evaluate the performance of the classification by feeding the features into a decision tree-based classifier which is trained by CART. The experimental results show that the proposed method can recognize several broadcasting video genres with a high accuracy and the classification performance with multimodal features is superior to the one with unimodal features in the genre classification.

  18. Five-way Smoking Status Classification Using Text Hot-Spot Identification and Error-correcting Output Codes

    OpenAIRE

    Cohen, Aaron M.

    2008-01-01

    We participated in the i2b2 smoking status classification challenge task. The purpose of this task was to evaluate the ability of systems to automatically identify patient smoking status from discharge summaries. Our submission included several techniques that we compared and studied, including hot-spot identification, zero-vector filtering, inverse class frequency weighting, error-correcting output codes, and post-processing rules. We evaluated our approaches using the same methods as the i2...

  19. Impact of using the new GOLD classification on the distribution of COPD severity in clinical practice

    Directory of Open Access Journals (Sweden)

    Hernández M

    2018-01-01

    Full Text Available Marcos Hernández, Gabriel García, Jimena Falco, Agustín R García, Vanina Martín, Manuel Ibarrola, Silvia Quadrelli Department of Respiratory Medicine, Güemes Foundation, Buenos Aires, Argentina Objective: The objective of this study was to examine how COPD patients were classified by the Global Initiative for Chronic Obstructive Lung Disease (GOLD spirometry-based severity system and the distribution of COPD severity using the new GOLD 2011 assessment framework.Materials and methods: This was an observational, retrospective cohort study conducted in a single tertiary center on a prospective database, which aimed to evaluate the prevalence, incidence, severity, and comorbidities of COPD. Inclusion criteria were age ≥40 years and COPD diagnosis according to GOLD 2007 classification. Clinical factors were compared between the categories in GOLD 2007 and 2011 groups by using the χ2 test for categorical data and the analysis of variance for continuous data.Results: In total, 420 COPD patients were included in the analysis. The distribution of patients into GOLD 2007 categories was as follows: 6.4% (n=27 of them were classified into subgroup I, 42.1% (n=177 into subgroup II, 37.9% (n=159 into subgroup III, and 13.6% (n=57 into subgroup IV. The distribution of patients into GOLD 2011 categories was as follows: 16.4% (n=69 of them were classified into subgroup A (low risk and fewer symptoms, 32.1% (n=135 into subgroup B (low risk and more symptoms, 21.6% (n=91 into subgroup C (high risk and fewer symptoms, and 29.7% (n=125 into subgroup D (high risk and more symptoms. After the application of the new GOLD 2011 (modified Medical Research Council [mMRC] system, 22% (n=94 of patients were upgraded to a higher level than their spirometry level, and 16.2% (n=68 of them were downgraded in their severity category, meaning that almost 40% of patients changed their severity assessment category. In total, 22% of patients in stage I were allocated to

  20. Applying inventory classification to a large inventory management system

    Directory of Open Access Journals (Sweden)

    Benjamin Isaac May

    2017-06-01

    Full Text Available Inventory classification aims to ensure that business-driving inventory items are efficiently managed in spite of constrained resources. There are numerous single- and multiple-criteria approaches to it. Our objective is to improve resource allocation to focus on items that can lead to high equipment availability. This concern is typical of many service industries such as military logistics, airlines, amusement parks and public works. Our study tests several inventory prioritization techniques and finds that a modified multi-criterion weighted non-linear optimization (WNO technique is a powerful approach for classifying inventory, outperforming traditional techniques of inventory prioritization such as ABC analysis in a variety of performance objectives.

  1. Classification of hematopoietic regions in out-of-phase T{sub 1}-weighted images. A quantitative comparison study with T{sub 1}-weighted and STIR images

    Energy Technology Data Exchange (ETDEWEB)

    Amano, Yasuo; Amano, Maki; Kijima, Tetsuji; Kumazaki, Tatsuo [Nippon Medical School, Tokyo (Japan)

    1995-07-01

    The hematopoietic regions were classified into two groups on the basis of out-of-phase T{sub 1}-weighted images (op-TlWI): regions with lower intensity than that of muscle (LH) and regions with intensity equal to or higher than that of muscle (HH). We quantitatively evaluated the differences in signal intensity between LH and HH in order to examine this classification. Forty-two hematopoietic areas in aplastic anemia were classified into two groups of 23 LH and 19 HH. The signal ratios of hematopoietic areas to muscle on TlWI and STIR were calculated, and the differences between LH and HH were statistically evaluated. The signal ratios of LH were significantly higher on TlWI and lower on STIR than those of HH (unpaired t-test, p<0.05). This result indicated that LH consisted of more hypocellular marrow than HH. Op-TlWI were useful in differentiating between LH and HH and defining the degree of hematopoiesis in aplastic anemia. (author).

  2. Classification of feeding and eating disorders: review of evidence and proposals for ICD-11

    Science.gov (United States)

    UHER, RUDOLF; RUTTER, MICHAEL

    2012-01-01

    Current classification of eating disorders is failing to classify most clinical presentations; ignores continuities between child, adolescent and adult manifestations; and requires frequent changes of diagnosis to accommodate the natural course of these disorders. The classification is divorced from clinical practice, and investigators of clinical trials have felt compelled to introduce unsystematic modifications. Classification of feeding and eating disorders in ICD-11 requires substantial changes to remediate the shortcomings. We review evidence on the developmental and cross-cultural differences and continuities, course and distinctive features of feeding and eating disorders. We make the following recommendations: a) feeding and eating disorders should be merged into a single grouping with categories applicable across age groups; b) the category of anorexia nervosa should be broadened through dropping the requirement for amenorrhoea, extending the weight criterion to any significant underweight, and extending the cognitive criterion to include developmentally and culturally relevant presentations; c) a severity qualifier “with dangerously low body weight” should distinguish the severe cases of anorexia nervosa that carry the riskiest prognosis; d) bulimia nervosa should be extended to include subjective binge eating; e) binge eating disorder should be included as a specific category defined by subjective or objective binge eating in the absence of regular compensatory behaviour; f) combined eating disorder should classify subjects who sequentially or concurrently fulfil criteria for both anorexia and bulimia nervosa; g) avoidant/restrictive food intake disorder should classify restricted food intake in children or adults that is not accompanied by body weight and shape related psychopathology; h) a uniform minimum duration criterion of four weeks should apply. PMID:22654933

  3. A Study on SVM Based on the Weighted Elitist Teaching-Learning-Based Optimization and Application in the Fault Diagnosis of Chemical Process

    Directory of Open Access Journals (Sweden)

    Cao Junxiang

    2015-01-01

    Full Text Available Teaching-Learning-Based Optimization (TLBO is a new swarm intelligence optimization algorithm that simulates the class learning process. According to such problems of the traditional TLBO as low optimizing efficiency and poor stability, this paper proposes an improved TLBO algorithm mainly by introducing the elite thought in TLBO and adopting different inertia weight decreasing strategies for elite and ordinary individuals of the teacher stage and the student stage. In this paper, the validity of the improved TLBO is verified by the optimizations of several typical test functions and the SVM optimized by the weighted elitist TLBO is used in the diagnosis and classification of common failure data of the TE chemical process. Compared with the SVM combining other traditional optimizing methods, the SVM optimized by the weighted elitist TLBO has a certain improvement in the accuracy of fault diagnosis and classification.

  4. The long-term outcome after severe trauma of children in Flanders (Belgium): A population-based cohort study using the International Classification of Functioning-related outcome score

    NARCIS (Netherlands)

    P. van de Voorde (Patrick); M. Sabbe (Marc); R. Tsonaka (Roula); D. Rizopoulos (Dimitris); P. Calle (Paul); A. de De Jaeger (Annick); E.M.E.H. Lesaffre (Emmanuel); D. Matthys (Dirk)

    2011-01-01

    textabstractImportant long-term health problems have been described after severe paediatric trauma. The International Classification of Functioning (ICF) was developed as a universal framework to describe that health. We evaluated outcome in children after 'severe' trauma (defined as: hospitalised

  5. TFM classification and staging of oral submucous fibrosis: A new proposal.

    Science.gov (United States)

    Arakeri, Gururaj; Thomas, Deepak; Aljabab, Abdulsalam S; Hunasgi, Santosh; Rai, Kirthi Kumar; Hale, Beverley; Fonseca, Felipe Paiva; Gomez, Ricardo Santiago; Rahimi, Siavash; Merkx, Matthias A W; Brennan, Peter A

    2018-04-01

    We have evaluated the rationale of existing grading and staging schemes of oral submucous fibrosis (OSMF) based on how they are categorized. A novel classification and staging scheme is proposed. A total of 300 OSMF patients were evaluated for agreement between functional, clinical, and histopathological staging. Bilateral biopsies were assessed in 25 patients to evaluate for any differences in histopathological staging of OSMF in the same mouth. Extent of clinician agreement for categorized staging data was evaluated using Cohen's weighted kappa analysis. Cross-tabulation was performed on categorical grading data to understand the intercorrelation, and the unweighted kappa analysis was used to assess the bilateral grade agreement. Probabilities of less than 0.05 were considered significant. Data were analyzed using SPSS Statistics (version 25.0, IBM, USA). A low agreement was found between all the stages depicting the independent nature of trismus, clinical features, and histopathological components (K = 0.312, 0.167, 0.152) in OSMF. Following analysis, a three-component classification scheme (TFM classification) was developed that describes the severity of each independently, grouping them using a novel three-tier staging scheme as a guide to the treatment plan. The proposed classification and staging could be useful for effective communication, categorization, and for recording data and prognosis, and for guiding treatment plans. Furthermore, the classification considers OSMF malignant transformation in detail. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  6. Latent class models for classification

    NARCIS (Netherlands)

    Vermunt, J.K.; Magidson, J.

    2003-01-01

    An overview is provided of recent developments in the use of latent class (LC) and other types of finite mixture models for classification purposes. Several extensions of existing models are presented. Two basic types of LC models for classification are defined: supervised and unsupervised

  7. Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values.

    Directory of Open Access Journals (Sweden)

    Talayeh Razzaghi

    Full Text Available This work is motivated by the needs of predictive analytics on healthcare data as represented by Electronic Medical Records. Such data is invariably problematic: noisy, with missing entries, with imbalance in classes of interests, leading to serious bias in predictive modeling. Since standard data mining methods often produce poor performance measures, we argue for development of specialized techniques of data-preprocessing and classification. In this paper, we propose a new method to simultaneously classify large datasets and reduce the effects of missing values. It is based on a multilevel framework of the cost-sensitive SVM and the expected maximization imputation method for missing values, which relies on iterated regression analyses. We compare classification results of multilevel SVM-based algorithms on public benchmark datasets with imbalanced classes and missing values as well as real data in health applications, and show that our multilevel SVM-based method produces fast, and more accurate and robust classification results.

  8. The present status of xeroderma pigmentosum in Japan and a tentative severity classification scale.

    Science.gov (United States)

    Nakano, Eiji; Masaki, Taro; Kanda, Fumio; Ono, Ryusuke; Takeuchi, Seiji; Moriwaki, Shinichi; Nishigori, Chikako

    2016-08-01

    Xeroderma pigmentosum (XP) is a rare autosomal recessive hereditary disease. Patients with XP have severe hypersensitivity to sunlight, resulting in skin cancers, and some patients have neurological symptoms. In Japan, XP complementation group A (XP-A) is the most common form, and it is associated with severe neurological symptoms. We performed a nationwide survey on XP to determine the present status of XP in Japan. The distribution of complementation groups in Japan was considerably different from that in other countries, but there was a higher frequency in group A and the variant type, which is similar to previous reports in Japan. Basal cell carcinoma was the most frequent skin cancer that patients with XP developed, followed by squamous cell carcinoma and malignant melanoma. The frequency of these skin cancers in patients with XP-A has decreased, and these skin cancers have been occurring in much older people than those previously observed. Diagnosing XP in patients at younger ages seems to encourage patients and their parents to use sun protection, which helps prevent skin cancer. We also created a tentative scale for classifying the severity of XP, and we evaluated the neurological symptoms of XP-A using this severity scale. Our classification correlated well with patients' age, suggesting that it may be useful and feasible in clinical practice to assess the progression of symptoms of each patient with XP and evaluate the effects of treatment in the future. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  9. Fitness Tracker for Weight Lifting Style Workouts

    Energy Technology Data Exchange (ETDEWEB)

    Wihl, B. M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2017-02-01

    This document proposes an early, high level design for a fitness tracking system which can automatically log weight lifting style workouts. The system will provide an easy to use interface both physically through the use of several wireless wristband style motion trackers worn on the limbs, and graphically through a smartphone application. Exercise classification will be accomplished by calibration of the user’s specific motions. The system will accurately track a user’s workout, miscounting no more than one repetition in every 20, have sufficient battery life to last several hours, work with existing smartphones and have a cost similar to those of current fitness tracking devices. This document presents the mission background, current state-of-theart, stakeholders and their expectations, the proposed system’s context and concepts, implementation concepts, system requirements, first sublevel function decomposition, possible risks for the system, and a reflection on the design process.

  10. Implementation of several mathematical algorithms to breast tissue density classification

    Science.gov (United States)

    Quintana, C.; Redondo, M.; Tirao, G.

    2014-02-01

    The accuracy of mammographic abnormality detection methods is strongly dependent on breast tissue characteristics, where a dense breast tissue can hide lesions causing cancer to be detected at later stages. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. This paper presents the implementation and the performance of different mathematical algorithms designed to standardize the categorization of mammographic images, according to the American College of Radiology classifications. These mathematical techniques are based on intrinsic properties calculations and on comparison with an ideal homogeneous image (joint entropy, mutual information, normalized cross correlation and index Q) as categorization parameters. The algorithms evaluation was performed on 100 cases of the mammographic data sets provided by the Ministerio de Salud de la Provincia de Córdoba, Argentina—Programa de Prevención del Cáncer de Mama (Department of Public Health, Córdoba, Argentina, Breast Cancer Prevention Program). The obtained breast classifications were compared with the expert medical diagnostics, showing a good performance. The implemented algorithms revealed a high potentiality to classify breasts into tissue density categories.

  11. Evolutionary Algorithms For Neural Networks Binary And Real Data Classification

    Directory of Open Access Journals (Sweden)

    Dr. Hanan A.R. Akkar

    2015-08-01

    Full Text Available Artificial neural networks are complex networks emulating the way human rational neurons process data. They have been widely used generally in prediction clustering classification and association. The training algorithms that used to determine the network weights are almost the most important factor that influence the neural networks performance. Recently many meta-heuristic and Evolutionary algorithms are employed to optimize neural networks weights to achieve better neural performance. This paper aims to use recently proposed algorithms for optimizing neural networks weights comparing these algorithms performance with other classical meta-heuristic algorithms used for the same purpose. However to evaluate the performance of such algorithms for training neural networks we examine such algorithms to classify four opposite binary XOR clusters and classification of continuous real data sets such as Iris and Ecoli.

  12. Analysis of Influence of Different Relations Types on the Quality of Thesaurus Application to Text Classification Problems

    Directory of Open Access Journals (Sweden)

    Nadezhda S. Lagutina

    2017-01-01

    Full Text Available The main purpose of the article is to analyze how effectively different types of thesaurus relations can be used for solutions of text classification tasks. The basis of the study is an automatically generated thesaurus of a subject area, that contains three types of relations: synonymous, hierarchical and associative. To generate the thesaurus the authors use a hybrid method based on several linguistic and statistical algorithms for extraction of semantic relations. The method allows to create a thesaurus with a sufficiently large number of terms and relations among them. The authors consider two problems: topical text classification and sentiment classification of large newspaper articles. To solve them, the authors developed two approaches that complement standard algorithms with a procedure that take into account thesaurus relations to determine semantic features of texts. The approach to topical classification includes the standard unsupervised BM25 algorithm and the procedure, that take into account synonymous and hierarchical relations of the thesaurus of the subject area. The approach to sentiment classification consists of two steps. At the first step, a thesaurus is created, whose terms weight polarities are calculated depending on the term occurrences in the training set or on the weights of related thesaurus terms. At the second step, the thesaurus is used to compute the features of words from texts and to classify texts by the algorithm SVM or Naive Bayes. In experiments with text corpora BBCSport, Reuters, PubMed and the corpus of articles about American immigrants, the authors varied the types of thesaurus relations that are involved in the classification and the degree of their use. The results of the experiments make it possible to evaluate the efficiency of the application of thesaurus relations for classification of raw texts and to determine under what conditions certain relationships affect more or less. In particular, the

  13. Unsupervised Classification Using Immune Algorithm

    OpenAIRE

    Al-Muallim, M. T.; El-Kouatly, R.

    2012-01-01

    Unsupervised classification algorithm based on clonal selection principle named Unsupervised Clonal Selection Classification (UCSC) is proposed in this paper. The new proposed algorithm is data driven and self-adaptive, it adjusts its parameters to the data to make the classification operation as fast as possible. The performance of UCSC is evaluated by comparing it with the well known K-means algorithm using several artificial and real-life data sets. The experiments show that the proposed U...

  14. Psychological Outcomes and Predictors of Initial Weight Loss Outcomes among Severely Obese Adolescents Receiving Laparoscopic Adjustable Gastric Banding

    Science.gov (United States)

    Sysko, Robyn; Devlin, Michael J.; Hildebrandt, Tom B.; Brewer, Stephanie K.; Zitsman, Jeffrey L.; Walsh, B. Timothy

    2013-01-01

    Objective Elevated rates of psychopathology are noted among severely obese youth presenting for weight loss surgery. The role of mental health providers in this population is not well defined, and the selection of candidates is often the result of clinical judgment alone. The purpose of this study was to comprehensively evaluate psychiatric symptoms among a large sample of adolescents receiving laparoscopic adjustable gastric banding (LAGB) by: (1) examining changes in depressive symptoms and quality of life in the year following surgery, (2) evaluating the interaction between patterns of change in depression, quality of life, and weight post-surgery, and (3) identifying pre-surgical psychological predictors of initial weight change. Method Participants were 101 severely obese adolescents aged 14 to 18. Measures of height, weight, depressive symptoms, and quality of life were obtained in the first year following surgery. Changes in the Beck Depression Inventory (BDI), Pediatric Quality of Life Inventory (PedsQL), and body mass index were analyzed using latent growth curve modeling. Results Significant changes in total BDI [βslope=−0.885 SE=0.279, psurgery (pAdolescents experienced notable improvements in initial depressive symptoms and quality of life after LAGB, and measures of pre-operative binge eating and family conflict affected post-surgery body mass index among youth. PMID:23140654

  15. EPA`s program for risk assessment guidelines: Cancer classification issues

    Energy Technology Data Exchange (ETDEWEB)

    Wiltse, J. [Environmental Protection Agency, Washington, DC (United States)

    1990-12-31

    Issues presented are related to classification of weight of evidence in cancer risk assessments. The focus in this paper is on lines of evidence used in constructing a conclusion about potential human carcinogenicity. The paper also discusses issues that are mistakenly addressed as classification issues but are really part of the risk assessment process. 2 figs.

  16. SEVERITY CLASSIFICATION OF MICROANEURYSMS USING NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    Shree Divya R

    2014-01-01

    Full Text Available Diabetic Retinopathy is one of the most common causes of blindness that leads to the loss of vision to the human eye. Several methods have been proposed to detect several defects of the human eye like hemorrhages, exudates etc. which are to be considered as the major symptoms. Among them, Microaneurysms should be considered as one of the severe condition for the early blindness. Several techniques have been proposed based on this, but they have certain drawbacks. A new technique called neural network taken for presentation, helps to detect and determine the severity of Microaneurysms which would be able to give a better performance than the existing techniques.

  17. A risk prediction model for severe intraventricular hemorrhage in very low birth weight infants and the effect of prophylactic indomethacin.

    Science.gov (United States)

    Luque, M J; Tapia, J L; Villarroel, L; Marshall, G; Musante, G; Carlo, W; Kattan, J

    2014-01-01

    Develop a risk prediction model for severe intraventricular hemorrhage (IVH) in very low birth weight infants (VLBWI). Prospectively collected data of infants with birth weight 500 to 1249 g born between 2001 and 2010 in centers from the Neocosur Network were used. Forward stepwise logistic regression model was employed. The model was tested in the 2011 cohort and then applied to the population of VLBWI that received prophylactic indomethacin to analyze its effect in the risk of severe IVH. Data from 6538 VLBWI were analyzed. The area under ROC curve for the model was 0.79 and 0.76 when tested in the 2011 cohort. The prophylactic indomethacin group had lower incidence of severe IVH, especially in the highest-risk groups. A model for early severe IVH prediction was developed and tested in our population. Prophylactic indomethacin was associated with a lower risk-adjusted incidence of severe IVH.

  18. Early corticosteroid treatment does not affect severity of unconjugated hyperbilirubinemia in extreme low birth weight preterm infants

    NARCIS (Netherlands)

    Hulzebos, Christian V.; Bos, Arend F.; Anttila, Eija; Hallman, Mikko; Verkade, Henkjan J.

    Aim: To determine the relationship between early postnatal dexamethasone (DXM) treatment and the severity of hyperbilirubinemia in extreme low birth weight (ELBW) preterm infants. Methods: In 54 ELBW preterm infants, total serum bilirubin concentrations (TSB) and phototherapy (PT) data during the

  19. High incidence of rickets in extremely low birth weight infants with severe parenteral nutrition-associated cholestasis and bronchopulmonary dysplasia.

    Science.gov (United States)

    Lee, Soon Min; Namgung, Ran; Park, Min Soo; Eun, Ho Sun; Park, Kook In; Lee, Chul

    2012-12-01

    Risk factors for rickets of prematurity have not been re-examined since introduction of high mineral formula, particularly in ELBW infants. We analyzed the incidence and the risk factors of rickets in extremely low birth weight (ELBW) infants. As a retrospective case-control study from 2004 to 2008, risk factors were analyzed in 24 patients with rickets versus 31 patients without. The frequency of rickets in ELBW infants was 24/55 (44%). Infants with rickets were diagnosed at 48.2 ± 16.1 days of age, and improved by 85.3 ± 25.3 days. By radiologic evaluation, 29% were grade 1 rickets, 58% grade 2 and 13% grade 3. In univariate analysis, infants with rickets had significantly higher incidence of patent ductus arteriosus, parenteral nutrition associated cholestasis (PNAC), severe PNAC and moderate/severe bronchopulmonary dysplasia (BPD). In multiple regression analysis, after adjustment for gestation and birth weight, rickets significantly correlated with severe PNAC and with moderate/severe BPD. Serum peak alkaline phosphatase levels were significantly elevated in rickets (P rickets of prematurity remains high and the incidence of severe PNAC and moderate/severe BPD was significantly increased 18 and 3 times, respectively.

  20. Comparison of Danish dichotomous and BI-RADS classifications of mammographic density

    DEFF Research Database (Denmark)

    Hodge, Rebecca; Hellmann, Sophie Sell; von Euler-Chelpin, My

    2014-01-01

    BACKGROUND: In the Copenhagen mammography screening program from 1991 to 2001, mammographic density was classified either as fatty or mixed/dense. This dichotomous mammographic density classification system is unique internationally, and has not been validated before. PURPOSE: To compare the Danish...... dichotomous mammographic density classification system from 1991 to 2001 with the density BI-RADS classifications, in an attempt to validate the Danish classification system. MATERIAL AND METHODS: The study sample consisted of 120 mammograms taken in Copenhagen in 1991-2001, which tested false positive......, and which were in 2012 re-assessed and classified according to the BI-RADS classification system. We calculated inter-rater agreement between the Danish dichotomous mammographic classification as fatty or mixed/dense and the four-level BI-RADS classification by the linear weighted Kappa statistic. RESULTS...

  1. Intra- and interrater reliability of three different MRI grading and classification systems after acute hamstring injuries

    Energy Technology Data Exchange (ETDEWEB)

    Wangensteen, Arnlaug, E-mail: arnlaug.wangensteen@nih.no [Aspetar, Orthopaedic and Sports Medicine Hospital, Doha (Qatar); Oslo Sports Trauma Research Center, Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo (Norway); Tol, Johannes L., E-mail: johannes.tol@aspetar.com [Aspetar, Orthopaedic and Sports Medicine Hospital, Doha (Qatar); Amsterdam Center for Evidence Sports Medicine, Academic Medical Center (Netherlands); The Sports Physician Group, OLVG, Amsterdam (Netherlands); Roemer, Frank W. [Quantitative Imaging Center, Department of Radiology, Boston University School of Medicine, Boston, MA (United States); Department of Radiology, University of Erlangen-Nuremberg, Erlangen (Germany); Bahr, Roald [Aspetar, Orthopaedic and Sports Medicine Hospital, Doha (Qatar); Oslo Sports Trauma Research Center, Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo (Norway); Dijkstra, H. Paul [Aspetar, Orthopaedic and Sports Medicine Hospital, Doha (Qatar); Crema, Michel D. [Quantitative Imaging Center, Department of Radiology, Boston University School of Medicine, Boston, MA (United States); Department of Radiology, Saint-Antoine Hospital, University Paris VI, Paris (France); Farooq, Abdulaziz [Aspetar, Orthopaedic and Sports Medicine Hospital, Doha (Qatar); Guermazi, Ali [Quantitative Imaging Center, Department of Radiology, Boston University School of Medicine, Boston, MA (United States)

    2017-04-15

    Highlights: • Three different MRI grading and classification systems for acute hamstring injuries are overall reliable. • Reliability for the subcategories within these MRI grading and classification systems remains, however, unclear. - Abstract: Objective: To assess and compare the intra- and interrater reliability of three different MRI grading and classification systems after acute hamstring injury. Methods: Male athletes (n = 40) with clinical diagnosis of acute hamstring injury and MRI ≤5 days were selected from a prospective cohort. Two radiologists independently evaluated the MRIs using standardised scoring form including the modified Peetrons grading system, the Chan acute muscle strain injury classification and the British Athletics Muscle Injury Classification. Intra-and interrater reliability was assessed with linear weighted kappa (κ) or unweighted Cohen's κ and percentage agreement was calculated. Results: We observed ‘substantial’ to ‘almost perfect’ intra- (κ range 0.65–1.00) and interrater reliability (κ range 0.77–1.00) with percentage agreement 83–100% and 88–100%, respectively, for severity gradings, overall anatomical sites and overall classifications for the three MRI systems. We observed substantial variability (κ range −0.05 to 1.00) for subcategories within the Chan classification and the British Athletics Muscle Injury Classification, however, the prevalence of positive scorings was low for some subcategories. Conclusions: The modified Peetrons grading system, overall Chan classification and overall British Athletics Muscle Injury Classification demonstrated ‘substantial' to ‘almost perfect' intra- and interrater reliability when scored by experienced radiologists. The intra- and interrater reliability for the anatomical subcategories within the classifications remains unclear.

  2. Intra- and interrater reliability of three different MRI grading and classification systems after acute hamstring injuries

    International Nuclear Information System (INIS)

    Wangensteen, Arnlaug; Tol, Johannes L.; Roemer, Frank W.; Bahr, Roald; Dijkstra, H. Paul; Crema, Michel D.; Farooq, Abdulaziz; Guermazi, Ali

    2017-01-01

    Highlights: • Three different MRI grading and classification systems for acute hamstring injuries are overall reliable. • Reliability for the subcategories within these MRI grading and classification systems remains, however, unclear. - Abstract: Objective: To assess and compare the intra- and interrater reliability of three different MRI grading and classification systems after acute hamstring injury. Methods: Male athletes (n = 40) with clinical diagnosis of acute hamstring injury and MRI ≤5 days were selected from a prospective cohort. Two radiologists independently evaluated the MRIs using standardised scoring form including the modified Peetrons grading system, the Chan acute muscle strain injury classification and the British Athletics Muscle Injury Classification. Intra-and interrater reliability was assessed with linear weighted kappa (κ) or unweighted Cohen's κ and percentage agreement was calculated. Results: We observed ‘substantial’ to ‘almost perfect’ intra- (κ range 0.65–1.00) and interrater reliability (κ range 0.77–1.00) with percentage agreement 83–100% and 88–100%, respectively, for severity gradings, overall anatomical sites and overall classifications for the three MRI systems. We observed substantial variability (κ range −0.05 to 1.00) for subcategories within the Chan classification and the British Athletics Muscle Injury Classification, however, the prevalence of positive scorings was low for some subcategories. Conclusions: The modified Peetrons grading system, overall Chan classification and overall British Athletics Muscle Injury Classification demonstrated ‘substantial' to ‘almost perfect' intra- and interrater reliability when scored by experienced radiologists. The intra- and interrater reliability for the anatomical subcategories within the classifications remains unclear.

  3. Object-Based Classification of Grasslands from High Resolution Satellite Image Time Series Using Gaussian Mean Map Kernels

    Directory of Open Access Journals (Sweden)

    Mailys Lopes

    2017-07-01

    Full Text Available This paper deals with the classification of grasslands using high resolution satellite image time series. Grasslands considered in this work are semi-natural elements in fragmented landscapes, i.e., they are heterogeneous and small elements. The first contribution of this study is to account for grassland heterogeneity while working at the object level by modeling its pixels distributions by a Gaussian distribution. To measure the similarity between two grasslands, a new kernel is proposed as a second contribution: the α -Gaussian mean kernel. It allows one to weight the influence of the covariance matrix when comparing two Gaussian distributions. This kernel is introduced in support vector machines for the supervised classification of grasslands from southwest France. A dense intra-annual multispectral time series of the Formosat-2 satellite is used for the classification of grasslands’ management practices, while an inter-annual NDVI time series of Formosat-2 is used for old and young grasslands’ discrimination. Results are compared to other existing pixel- and object-based approaches in terms of classification accuracy and processing time. The proposed method is shown to be a good compromise between processing speed and classification accuracy. It can adapt to the classification constraints, and it encompasses several similarity measures known in the literature. It is appropriate for the classification of small and heterogeneous objects such as grasslands.

  4. Weighted K-means support vector machine for cancer prediction.

    Science.gov (United States)

    Kim, SungHwan

    2016-01-01

    To date, the support vector machine (SVM) has been widely applied to diverse bio-medical fields to address disease subtype identification and pathogenicity of genetic variants. In this paper, I propose the weighted K-means support vector machine (wKM-SVM) and weighted support vector machine (wSVM), for which I allow the SVM to impose weights to the loss term. Besides, I demonstrate the numerical relations between the objective function of the SVM and weights. Motivated by general ensemble techniques, which are known to improve accuracy, I directly adopt the boosting algorithm to the newly proposed weighted KM-SVM (and wSVM). For predictive performance, a range of simulation studies demonstrate that the weighted KM-SVM (and wSVM) with boosting outperforms the standard KM-SVM (and SVM) including but not limited to many popular classification rules. I applied the proposed methods to simulated data and two large-scale real applications in the TCGA pan-cancer methylation data of breast and kidney cancer. In conclusion, the weighted KM-SVM (and wSVM) increases accuracy of the classification model, and will facilitate disease diagnosis and clinical treatment decisions to benefit patients. A software package (wSVM) is publicly available at the R-project webpage (https://www.r-project.org).

  5. Association between the Family Nutrition and Physical Activity Screening Tool and obesity severity in youth referred to weight management.

    Science.gov (United States)

    Tucker, Jared M; Howard, Kathleen; Guseman, Emily H; Yee, Kimbo E; Saturley, Heather; Eisenmann, Joey C

    The Family Nutrition and Physical Activity Screening Tool (FNPA) evaluates family behavioural and environmental factors associated with pediatric obesity, but it is unknown if FNPA scores differ among youth across obesity severities. Our aim was to determine the association between the FNPA and obesity severity in youth referred to weight management. Upon initiating treatment, height, weight, and the FNPA were collected according to standard procedures. Cut-points for overweight/obesity, severe obesity (SO) class 2, and SO class 3 were calculated. FNPA scores were compared across weight status groups using analysis of covariance, and odds of SO across FNPA quartiles were evaluated with multiple logistic regression. Participants included 564 5-18year old who initiated treatment and completed the FNPA. After adjustment, FNPA scores differed by weight status with higher/healthier scores in youth with overweight/obesity (56.6±8.5) when compared to those with SO class 2 (55.0±7.1; p=0.015) or SO class 3 (53.6±9.0; p<0.001). Compared to those in the highest FNPA quartile, youth in the 2nd quartile had 1.8 (95% CI: 1.1, 2.9) times higher odds of SO, and those in the lowest FNPA quartile had 2.1 (95% CI: 1.3, 3.4) times higher odds of SO. Youth with SO had unhealthier subscale scores among 6 of 10 constructs, including nutritional, physical activity, sedentary, and sleep behaviours. Results suggest a consistent inverse relationship between the FNPA and adiposity among youth presenting for weight management. The FNPA is a useful metric for programs and clinicians targeting family behaviours and the home environment to combat obesity. Copyright © 2016 Asia Oceania Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.

  6. 26 CFR 48.4071-2 - Determination of weight.

    Science.gov (United States)

    2010-04-01

    ... EXCISE TAXES MANUFACTURERS AND RETAILERS EXCISE TAXES Motor Vehicles, Tires, Tubes, Tread Rubber, and... each type, size, grade, and classification. The average weights must be established in accordance with...

  7. Ghrelin and PYY levels in adolescents with severe obesity: effects of weight loss induced by long-term exercise training and modified food habits.

    Science.gov (United States)

    Gueugnon, Carine; Mougin, Fabienne; Nguyen, Nhu Uyen; Bouhaddi, Malika; Nicolet-Guénat, Marie; Dumoulin, Gilles

    2012-05-01

    This study investigated (a) changes in ghrelin and peptide YY (PYY) concentrations during a weight reduction programme and (b) baseline ghrelin and PYY levels as predictors of weight loss in 32 severely obese adolescents (BMI z score = 4.1). Subjects spent an academic year in an institution for childhood obesity. Fasting ghrelin and PYY, leptin, insulin levels and insulin resistance were measured at baseline (month 0) and during the programme (months 3, 6, 9). In addition, 15 normal-weight teenagers served as reference for the baseline assessments. At baseline, obese teenagers had lower ghrelin and PYY concentrations than normal-weight adolescents (P modification, there was a significant decrease in body weight among obese teenagers, associated with an increase in ghrelin (apparent from month 6; P modification. However, higher baseline PYY tended to correlate with greater anthropometrical changes (P < 0.1). In adolescents with severe obesity, a long-term combination of supervised aerobic exercises and a balanced diet led to weight reduction and increased ghrelin concentrations, without any change in PYY concentrations. Moreover, baseline PYY concentrations might be considered as predictors of weight loss.

  8. Disorder-specific predictive classification of adolescents with attention deficit hyperactivity disorder (ADHD relative to autism using structural magnetic resonance imaging.

    Directory of Open Access Journals (Sweden)

    Lena Lim

    Full Text Available Attention Deficit Hyperactivity Disorder (ADHD is a neurodevelopmental disorder, but diagnosed by subjective clinical and rating measures. The study's aim was to apply Gaussian process classification (GPC to grey matter (GM volumetric data, to assess whether individual ADHD adolescents can be accurately differentiated from healthy controls based on objective, brain structure measures and whether this is disorder-specific relative to autism spectrum disorder (ASD.Twenty-nine adolescent ADHD boys and 29 age-matched healthy and 19 boys with ASD were scanned. GPC was applied to make disorder-specific predictions of ADHD diagnostic status based on individual brain structure patterns. In addition, voxel-based morphometry (VBM analysis tested for traditional univariate group level differences in GM.The pattern of GM correctly classified 75.9% of patients and 82.8% of controls, achieving an overall classification accuracy of 79.3%. Furthermore, classification was disorder-specific relative to ASD. The discriminating GM patterns showed higher classification weights for ADHD in earlier developing ventrolateral/premotor fronto-temporo-limbic and stronger classification weights for healthy controls in later developing dorsolateral fronto-striato-parieto-cerebellar networks. Several regions were also decreased in GM in ADHD relative to healthy controls in the univariate VBM analysis, suggesting they are GM deficit areas.The study provides evidence that pattern recognition analysis can provide significant individual diagnostic classification of ADHD patients and healthy controls based on distributed GM patterns with 79.3% accuracy and that this is disorder-specific relative to ASD. Findings are a promising first step towards finding an objective differential diagnostic tool based on brain imaging measures to aid with the subjective clinical diagnosis of ADHD.

  9. Weight Management

    Science.gov (United States)

    ... Health Information Weight Management English English Español Weight Management Obesity is a chronic condition that affects more ... Liver (NASH) Heart Disease & Stroke Sleep Apnea Weight Management Topics About Food Portions Bariatric Surgery for Severe ...

  10. PATTERN CLASSIFICATION APPROACHES TO MATCHING BUILDING POLYGONS AT MULTIPLE SCALES

    Directory of Open Access Journals (Sweden)

    X. Zhang

    2012-07-01

    Full Text Available Matching of building polygons with different levels of detail is crucial in the maintenance and quality assessment of multi-representation databases. Two general problems need to be addressed in the matching process: (1 Which criteria are suitable? (2 How to effectively combine different criteria to make decisions? This paper mainly focuses on the second issue and views data matching as a supervised pattern classification. Several classifiers (i.e. decision trees, Naive Bayes and support vector machines are evaluated for the matching task. Four criteria (i.e. position, size, shape and orientation are used to extract information for these classifiers. Evidence shows that these classifiers outperformed the weighted average approach.

  11. Support Vector Machine Classification of Drunk Driving Behaviour.

    Science.gov (United States)

    Chen, Huiqin; Chen, Lei

    2017-01-23

    Alcohol is the root cause of numerous traffic accidents due to its pharmacological action on the human central nervous system. This study conducted a detection process to distinguish drunk driving from normal driving under simulated driving conditions. The classification was performed by a support vector machine (SVM) classifier trained to distinguish between these two classes by integrating both driving performance and physiological measurements. In addition, principal component analysis was conducted to rank the weights of the features. The standard deviation of R-R intervals (SDNN), the root mean square value of the difference of the adjacent R-R interval series (RMSSD), low frequency (LF), high frequency (HF), the ratio of the low and high frequencies (LF/HF), and average blink duration were the highest weighted features in the study. The results show that SVM classification can successfully distinguish drunk driving from normal driving with an accuracy of 70%. The driving performance data and the physiological measurements reported by this paper combined with air-alcohol concentration could be integrated using the support vector regression classification method to establish a better early warning model, thereby improving vehicle safety.

  12. Support Vector Machine Classification of Drunk Driving Behaviour

    Directory of Open Access Journals (Sweden)

    Huiqin Chen

    2017-01-01

    Full Text Available Alcohol is the root cause of numerous traffic accidents due to its pharmacological action on the human central nervous system. This study conducted a detection process to distinguish drunk driving from normal driving under simulated driving conditions. The classification was performed by a support vector machine (SVM classifier trained to distinguish between these two classes by integrating both driving performance and physiological measurements. In addition, principal component analysis was conducted to rank the weights of the features. The standard deviation of R–R intervals (SDNN, the root mean square value of the difference of the adjacent R–R interval series (RMSSD, low frequency (LF, high frequency (HF, the ratio of the low and high frequencies (LF/HF, and average blink duration were the highest weighted features in the study. The results show that SVM classification can successfully distinguish drunk driving from normal driving with an accuracy of 70%. The driving performance data and the physiological measurements reported by this paper combined with air-alcohol concentration could be integrated using the support vector regression classification method to establish a better early warning model, thereby improving vehicle safety.

  13. Training strategy for convolutional neural networks in pedestrian gender classification

    Science.gov (United States)

    Ng, Choon-Boon; Tay, Yong-Haur; Goi, Bok-Min

    2017-06-01

    In this work, we studied a strategy for training a convolutional neural network in pedestrian gender classification with limited amount of labeled training data. Unsupervised learning by k-means clustering on pedestrian images was used to learn the filters to initialize the first layer of the network. As a form of pre-training, supervised learning for the related task of pedestrian classification was performed. Finally, the network was fine-tuned for gender classification. We found that this strategy improved the network's generalization ability in gender classification, achieving better test results when compared to random weights initialization and slightly more beneficial than merely initializing the first layer filters by unsupervised learning. This shows that unsupervised learning followed by pre-training with pedestrian images is an effective strategy to learn useful features for pedestrian gender classification.

  14. BMI-for-age graphs with severe obesity percentile curves: tools for plotting cross-sectional and longitudinal youth BMI data.

    Science.gov (United States)

    Racette, Susan B; Yu, Liyang; DuPont, Nicholas C; Clark, B Ruth

    2017-05-24

    Severe obesity is an important and distinct weight status classification that is associated with disease risk and is increasing in prevalence among youth. The ability to graphically present population weight status data, ranging from underweight through severe obesity class 3, is novel and applicable to epidemiologic research, intervention studies, case reports, and clinical care. The aim was to create body mass index (BMI) graphing tools to generate sex-specific BMI-for-age graphs that include severe obesity percentile curves. We used the Centers for Disease Control and Prevention youth reference data sets and weight status criteria to generate the percentile curves. The statistical software environments SAS and R were used to create two different graphing options. This article provides graphing tools for creating sex-specific BMI-for-age graphs for males and females ages 2 to obesity classes 2 and 3, the ability to plot individual data for thousands of children and adolescents on a single graph, and the ability to generate cross-sectional and longitudinal graphs. These new BMI graphing tools will enable investigators, public health professionals, and clinicians to view and present youth weight status data in novel and meaningful ways.

  15. Two year reduction in sleep apnea symptoms and associated diabetes incidence after weight loss in severe obesity.

    Science.gov (United States)

    Grunstein, Ronald R; Stenlöf, Kaj; Hedner, Jan A; Peltonen, Markku; Karason, Kristjan; Sjöström, Lars

    2007-06-01

    To evaluate the effect of bariatric surgery on sleep apnea symptoms and obesity-associated morbidity in patients with severe obesity. Prospective study. University hospitals and community centers in Sweden. We investigated the influence of weight loss surgery (n=1729) on sleep apnea symptoms and obesity-related morbidity using a conservatively treated group (n=1748) as a control. Baseline BMI in surgical group (42.2+/-4.4 kg/m(2)) and control group (40.1+/-4.6 kg/m(2)) changed -9.7+/-5 kg/m(2) and 0+/-3 kg/m(2), respectively, at 2-year follow-up. In the surgery group, there was a marked improvement in all obstructive sleep apnea (OSA) symptoms compared with the control group (P sleep apnea symptoms at 2 years. Despite adjustment for weight change and baseline central obesity, subjects reporting loss of OSA symptoms had a lower 2-year incidence of diabetes and hypertriglyceridemia. Improvement in OSA in patients losing weight may provide health benefits in addition to weight loss alone.

  16. Classification of Several Optically Complex Waters in China Using in Situ Remote Sensing Reflectance

    Directory of Open Access Journals (Sweden)

    Qian Shen

    2015-11-01

    Full Text Available Determining the dominant optically active substances in water bodies via classification can improve the accuracy of bio-optical and water quality parameters estimated by remote sensing. This study provides four robust centroid sets from in situ remote sensing reflectance (Rrs (λ data presenting typical optical types obtained by plugging different similarity measures into fuzzy c-means (FCM clustering. Four typical types of waters were studied: (1 highly mixed eutrophic waters, with the proportion of absorption of colored dissolved organic matter (CDOM, phytoplankton, and non-living particulate matter at approximately 20%, 20%, and 60% respectively; (2 CDOM-dominated relatively clear waters, with approximately 45% by proportion of CDOM absorption; (3 nonliving solids-dominated waters, with approximately 88% by proportion of absorption of nonliving particulate matter; and (4 cyanobacteria-composed scum. We also simulated spectra from seven ocean color satellite sensors to assess their classification ability. POLarization and Directionality of the Earth's Reflectances (POLDER, Sentinel-2A, and MEdium Resolution Imaging Spectrometer (MERIS were found to perform better than the rest. Further, a classification tree for MERIS, in which the characteristics of Rrs (709/Rrs (681, Rrs (560/Rrs (709, Rrs (560/Rrs (620, and Rrs (709/Rrs (761 are integrated, is also proposed in this paper. The overall accuracy and Kappa coefficient of the proposed classification tree are 76.2% and 0.632, respectively.

  17. Classification of the severe trauma patient with the Abbreviated Injury Scale: degree of correlation between versions 98 and 2005 (2008 update).

    Science.gov (United States)

    Abajas Bustillo, Rebeca; Leal Costa, César; Ortego Mate, María Del Carmen; Zonfrillo, Mark R; Seguí Gómez, María; Durá Ros, María Jesús

    2018-02-01

    To explore differences in severity classifications according to 2 versions of the Abbreviated Injury Scale (AIS): version 2005 (the 2008 update) and the earlier version 98. To determine whether possible differences might have an impact on identifying severe trauma patients. Descriptive study and cross-sectional analysis of a case series of patients admitted to two spanish hospitals with out-of-hospital injuries between February 2012 and February 2013. For each patient we calculated the Injury Severity Score (ISS), the New Injury Severity Score (NISS), and the AIS scores according to versions 98 and 2005. The sample included 699 cases. The mean Severity (SD) age of patients was 52.7 (29.2) years, and 388 (55.5%) were males. Version 98 of the AIS correlated more strongly with both the ISS (2.6%) and the NISS (2.9%). The 2008 update of the AIS (version 2005) classified fewer trauma patients than version 98 at the severity levels indicated by the ISS and NISS.

  18. Inter and intra-observer reliability in assessment of the position of the lateral sesamoid in determining the severity of hallux valgus.

    Science.gov (United States)

    Panchani, Sunil; Reading, Jonathan; Mehta, Jaysheel

    2016-06-01

    The position of the lateral sesamoid on standard dorso-plantar weight bearing radiographs, with respect to the lateral cortex of the first metatarsal, has been shown to correlate well with the degree of the hallux valgus angle. This study aimed to assess the inter- and intra-observer error of this new classification system. Five orthopaedic consultants and five trainee orthopaedic surgeons were recruited to assess and document the degree of displacement of the lateral sesamoid on 144 weight-bearing dorso-plantar radiographs on two separate occasions. The severity of hallux valgus was defined as normal (0%), mild (≤50%), moderate (51-≤99%) or severe (≥100%) depending on the percentage displacement of the lateral sesamoid body from the lateral cortical border of the first metatarsal. Consultant intra-observer variability showed good agreement between repeated assessment of the radiographs (mean Kappa=0.75). Intra-observer variability for trainee orthopaedic surgeons also showed good agreement with a mean Kappa=0.73. Intraclass correlations for consultants and trainee surgeons was also high. The new classification system of assessing the severity of hallux valgus shows high inter- and intra-observer variability with good agreement and reproducibility between surgeons of consultant and trainee grades. Copyright © 2015 Elsevier Ltd. All rights reserved.

  19. Patterns of brain structural connectivity differentiate normal weight from overweight subjects.

    Science.gov (United States)

    Gupta, Arpana; Mayer, Emeran A; Sanmiguel, Claudia P; Van Horn, John D; Woodworth, Davis; Ellingson, Benjamin M; Fling, Connor; Love, Aubrey; Tillisch, Kirsten; Labus, Jennifer S

    2015-01-01

    Alterations in the hedonic component of ingestive behaviors have been implicated as a possible risk factor in the pathophysiology of overweight and obese individuals. Neuroimaging evidence from individuals with increasing body mass index suggests structural, functional, and neurochemical alterations in the extended reward network and associated networks. To apply a multivariate pattern analysis to distinguish normal weight and overweight subjects based on gray and white-matter measurements. Structural images (N = 120, overweight N = 63) and diffusion tensor images (DTI) (N = 60, overweight N = 30) were obtained from healthy control subjects. For the total sample the mean age for the overweight group (females = 32, males = 31) was 28.77 years (SD = 9.76) and for the normal weight group (females = 32, males = 25) was 27.13 years (SD = 9.62). Regional segmentation and parcellation of the brain images was performed using Freesurfer. Deterministic tractography was performed to measure the normalized fiber density between regions. A multivariate pattern analysis approach was used to examine whether brain measures can distinguish overweight from normal weight individuals. 1. White-matter classification: The classification algorithm, based on 2 signatures with 17 regional connections, achieved 97% accuracy in discriminating overweight individuals from normal weight individuals. For both brain signatures, greater connectivity as indexed by increased fiber density was observed in overweight compared to normal weight between the reward network regions and regions of the executive control, emotional arousal, and somatosensory networks. In contrast, the opposite pattern (decreased fiber density) was found between ventromedial prefrontal cortex and the anterior insula, and between thalamus and executive control network regions. 2. Gray-matter classification: The classification algorithm, based on 2 signatures with 42 morphological features, achieved 69

  20. Patterns of brain structural connectivity differentiate normal weight from overweight subjects

    Science.gov (United States)

    Gupta, Arpana; Mayer, Emeran A.; Sanmiguel, Claudia P.; Van Horn, John D.; Woodworth, Davis; Ellingson, Benjamin M.; Fling, Connor; Love, Aubrey; Tillisch, Kirsten; Labus, Jennifer S.

    2015-01-01

    Background Alterations in the hedonic component of ingestive behaviors have been implicated as a possible risk factor in the pathophysiology of overweight and obese individuals. Neuroimaging evidence from individuals with increasing body mass index suggests structural, functional, and neurochemical alterations in the extended reward network and associated networks. Aim To apply a multivariate pattern analysis to distinguish normal weight and overweight subjects based on gray and white-matter measurements. Methods Structural images (N = 120, overweight N = 63) and diffusion tensor images (DTI) (N = 60, overweight N = 30) were obtained from healthy control subjects. For the total sample the mean age for the overweight group (females = 32, males = 31) was 28.77 years (SD = 9.76) and for the normal weight group (females = 32, males = 25) was 27.13 years (SD = 9.62). Regional segmentation and parcellation of the brain images was performed using Freesurfer. Deterministic tractography was performed to measure the normalized fiber density between regions. A multivariate pattern analysis approach was used to examine whether brain measures can distinguish overweight from normal weight individuals. Results 1. White-matter classification: The classification algorithm, based on 2 signatures with 17 regional connections, achieved 97% accuracy in discriminating overweight individuals from normal weight individuals. For both brain signatures, greater connectivity as indexed by increased fiber density was observed in overweight compared to normal weight between the reward network regions and regions of the executive control, emotional arousal, and somatosensory networks. In contrast, the opposite pattern (decreased fiber density) was found between ventromedial prefrontal cortex and the anterior insula, and between thalamus and executive control network regions. 2. Gray-matter classification: The classification algorithm, based on 2 signatures with 42

  1. Treatment Options for Severe Obesity in the Pediatric Population: Current Limitations and Future Opportunities.

    Science.gov (United States)

    Ryder, Justin R; Fox, Claudia K; Kelly, Aaron S

    2018-06-01

    Severe obesity is the only obesity classification increasing in prevalence among children and adolescents. Treatment options that produce meaningful and sustained weight loss and comorbidity resolution are urgently needed. The purpose of this review is to provide a brief overview of the current treatment options for pediatric severe obesity and offer suggestions regarding future opportunities for accelerating the development and evaluation of innovative treatment strategies. At present, there are three treatment options for youth with severe obesity: lifestyle modification therapy, pharmacotherapy, and bariatric surgery. Lifestyle modification therapy can be useful for improving many chronic disease risk factors and comorbid conditions but often fails to achieve clinically meaningful and sustainable weight loss. Pharmacotherapy holds promise as an effective adjunctive treatment but remains in the primordial stages of development in the pediatric population. Bariatric surgery provides robust weight loss and risk factor/comorbidity improvements but is accompanied by higher risks and lower uptake compared to lifestyle modification therapy and pharmacotherapy. New areas worth pursuing include combination pharmacotherapy, device therapy, identification of predictors of response aimed at precision treatment, and interventions in the postbariatric surgical setting to improve long-term outcomes. Treating pediatric severe obesity effectively and safely is extremely challenging. Some progress has been made, but substantially more effort and innovation are needed in the future to combat this serious and ongoing medical and public health issue. © 2018 The Obesity Society.

  2. Chinese parents' perceptions of their children's weights and their relationship to parenting behaviours.

    Science.gov (United States)

    Wen, X; Hui, S S C

    2011-05-01

    The purpose of this study is to examine Chinese parents' perceptions of their children's weights and explore the parenting behaviours associated with these perceptions. A total of 2143 adolescents and 1869 parents were recruited from secondary schools in Ganzhou and Shantou in China. The adolescents' actual weights and heights were measured by trained testers. The self-reported parents' weights and heights, parental perception of the adolescents' weights, adolescents' perception of their own weights, parenting behaviours and demographic information were collected through the questionnaires distributed to the respondents. The results based on Kappa statistics show only a slight agreement between parental perception of their children's weights and the adolescents' actual weights (Kappa = 0.221). The results from the logistic regression show that the parents' gender [odds ratio (OR) = 0.80, 95% confidence interval (CI): 0.64-1.00], adolescents' gender (OR = 1.61, 95% CI: 1.29-2.01) and perception of their own weights (OR = 0.30, 95% CI: 0.24-0.38) are associated with the parents' perception of their children's weights. Statistically significant difference in several parenting behaviours was found between the parents with correct and incorrect perceptions of their children's weight. Misconceptions about their children's weights are prevalent among Chinese parents. The association between parents' perception of their children's weight and parenting behaviours suggests that the accurate classification of children's weights could help prevent childhood obesity. © 2010 Blackwell Publishing Ltd.

  3. Psychometric properties of the Spanish version of the Body Weight, Image and Self-Esteem Evaluation Questionnaire in patients with severe mental disorders.

    Science.gov (United States)

    Al-Halabi, Susana; Garcia-Portilla, Maria Paz; Saiz, Pilar Alejandra; Fonseca, Eduardo; Bobes-Bascaran, Maria Teresa; Galván, Gonzalo; Iglesias, Celso; Arrojo, Manuel; Benabarre, Antoni; Goikolea, José Manuel; Sanchez, Emilio; Sarramea, Fernando; Bobes, Julio

    2012-11-01

    Clinicians need brief and valid instruments to monitor the psychosocial impact of weight gain in persons with psychiatric disorders. We examined the psychometric properties of the Spanish version of the Body Weight, Image and Self-Esteem Evaluation (B-WISE) questionnaire in patients with severe mental disorders. The data come from a naturalistic, cross-sectional, validation study conducted at 6 centres in Spain. A total of 211 outpatients with severe mental disorders, 118 with schizophrenia and 93 with bipolar disorder, were evaluated using the B-WISE, the Visual Analogue Scale for Weight and Body Image, and the Clinical Global Impression-Severity (CGI-S). The body mass index was also obtained. The principal component analysis confirms 3 components explaining 50.93% of the variance. The Cronbach α values for B-WISE scales ranged between .55 and .73. Significant Pearson correlations were found between B-WISE total score and CGI-S (r = -0.25; P Body Image (r = 0.47; P Body mass index categorization significantly influenced total B-WISE scores (F = 3.586, P < .050). The B-WISE score corresponding to the 5th and 10th percentiles was 22. We were able to demonstrate that the Spanish version of the B-WISE is a valid instrument for assessing psychosocial impact of weight gain in patients with severe mental disorders in daily clinical practice. Copyright © 2012 Elsevier Inc. All rights reserved.

  4. [Classification of Colombian children with malnutrition according to NCHS reference or WHO standard].

    Science.gov (United States)

    Velásquez, Claudia; Bermúdez, Juliana; Echeverri, Claudia; Estrada, Alejandro

    2011-12-01

    A descriptive study was conducted to evaluate the concordance of National Center for Health Statistics reference (NCHS) used to classify undernourished children from Colombia with the WHO Child Growth Standards. We used data from children aged 6 to 59 months with acute malnutrition (Z Infantil" nutrition program in Colombia. Indicators height-for-age, weight for-height were analyzed when they were admitted to the hospital and weight for-height leaving the hospital. A statistical method used to compare means was T-student. Correlation coefficient intraclass (CCI) and Kappa index evaluated the concordance between NCHS and OMS; McNemar method evaluated the changes on the nutritional classification for children according to growth devices used. Of the total number of children classified as normal by NCHS, 10.4% were classified as stunted by WHO. 64% of the children admitted to the hospital presented acute malnutrition according to NCHS, of these 44,8% presented severe emaciation according to OMS, indeed severe emaciation increased of 36,0% to 63,3% using OMS. 5% of children leaving the hospital could need to stay more days if they had been evaluated with OMS. Growth devices shown high concordance in height-for-age (CCI = 0,988; k= 0,866) and weight for-height (CCI = 0,901; k = 0,578). Concluded that OMS growth standards classified more malnourished children and more severe states, in addition more malnourished children could be hospitalized and they could stay more days.

  5. Credit Risk Evaluation Using a C-Variable Least Squares Support Vector Classification Model

    Science.gov (United States)

    Yu, Lean; Wang, Shouyang; Lai, K. K.

    Credit risk evaluation is one of the most important issues in financial risk management. In this paper, a C-variable least squares support vector classification (C-VLSSVC) model is proposed for credit risk analysis. The main idea of this model is based on the prior knowledge that different classes may have different importance for modeling and more weights should be given to those classes with more importance. The C-VLSSVC model can be constructed by a simple modification of the regularization parameter in LSSVC, whereby more weights are given to the lease squares classification errors with important classes than the lease squares classification errors with unimportant classes while keeping the regularized terms in its original form. For illustration purpose, a real-world credit dataset is used to test the effectiveness of the C-VLSSVC model.

  6. Optimal ABC inventory classification using interval programming

    NARCIS (Netherlands)

    Rezaei, J.; Salimi, N.

    2015-01-01

    Inventory classification is one of the most important activities in inventory management, whereby inventories are classified into three or more classes. Several inventory classifications have been proposed in the literature, almost all of which have two main shortcomings in common. That is, the

  7. Association of previous severe low birth weight with adverse perinatal outcomes in a subsequent pregnancy among HIV-prevalent urban African women.

    Science.gov (United States)

    Smid, Marcela C; Ahmed, Yusuf; Stoner, Marie C D; Vwalika, Bellington; Stringer, Elizabeth M; Stringer, Jeffrey S A

    2017-02-01

    To evaluate the association between severity of prior low birth weight (LBW) delivery and adverse perinatal outcomes in the subsequent delivery among an HIV-prevalent urban African population. A retrospective cohort study was conducted among 41 109 women who had undergone two deliveries in Lusaka, Zambia, between February 1, 2006, and May 31, 2013. The relationship between prior LBW delivery (<2500 g) and a composite measure of adverse perinatal outcome in the second pregnancy was assessed using multivariate logistic regression. Women with prior LBW delivery (n=4259) had an increased risk of LBW in the second delivery versus those without prior LBW delivery (n=37 642). Such risk correlated with the severity of first delivery LBW. The adjusted odds ratio (AOR) was 2.89 (95% confidence interval [CI] 2.05-4.09) for a birth weight of 1000-1499 g, 3.05 (95% CI 2.42-3.86) for a birth weight of 1500-1999 g, and 2.02 (95% CI 1.81-2.27) for a birth weight of 2000-2499 g. Previous LBW delivery also increased the risk of adverse perinatal outcome, with an AOR of 1.4 (95% CI 1.2-1.7). Severe prior LBW delivery conferred substantial risk for adverse perinatal outcomes in a subsequent pregnancy. © 2016 International Federation of Gynecology and Obstetrics.

  8. Combining anatomical, diffusion, and resting state functional magnetic resonance imaging for individual classification of mild and moderate Alzheimer's disease

    Directory of Open Access Journals (Sweden)

    Tijn M. Schouten

    2016-01-01

    Full Text Available Magnetic resonance imaging (MRI is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD, and can therefore be used to help in diagnosing the disease. Improving classification of AD patients based on MRI scans might help to identify AD earlier in the disease's progress, which may be key in developing treatments for AD. In this study we used an elastic net classifier based on several measures derived from the MRI scans of mild to moderate AD patients (N=77 from the prospective registry on dementia study and controls (N=173 from the Austrian Stroke Prevention Family Study. We based our classification on measures from anatomical MRI, diffusion weighted MRI and resting state functional MRI. Our unimodal classification performance ranged from an area under the curve (AUC of 0.760 (full correlations between functional networks to 0.909 (grey matter density. When combining measures from multiple modalities in a stepwise manner, the classification performance improved to an AUC of 0.952. This optimal combination consisted of grey matter density, white matter density, fractional anisotropy, mean diffusivity, and sparse partial correlations between functional networks. Classification performance for mild AD as well as moderate AD also improved when using this multimodal combination. We conclude that different MRI modalities provide complementary information for classifying AD. Moreover, combining multiple modalities can substantially improve classification performance over unimodal classification.

  9. [New International Classification of Chronic Pancreatitis (M-ANNHEIM multifactor classification system, 2007): principles, merits, and demerits].

    Science.gov (United States)

    Tsimmerman, Ia S

    2008-01-01

    The new International Classification of Chronic Pancreatitis (designated as M-ANNHEIM) proposed by a group of German specialists in late 2007 is reviewed. All its sections are subjected to analysis (risk group categories, clinical stages and phases, variants of clinical course, diagnostic criteria for "established" and "suspected" pancreatitis, instrumental methods and functional tests used in the diagnosis, evaluation of the severity of the disease using a scoring system, stages of elimination of pain syndrome). The new classification is compared with the earlier classification proposed by the author. Its merits and demerits are discussed.

  10. 42 CFR 412.620 - Patient classification system.

    Science.gov (United States)

    2010-10-01

    ...-mix group classifications and weighting factors. We may periodically adjust the case-mix groups and... rehabilitation facilities into mutually exclusive case-mix groups. (2) For purposes of this subpart, case-mix... assessments under § 412.610(c)(1) are used to classify a Medicare patient into an appropriate case-mix group...

  11. Clustering and classification of email contents

    Directory of Open Access Journals (Sweden)

    Izzat Alsmadi

    2015-01-01

    Full Text Available Information users depend heavily on emails’ system as one of the major sources of communication. Its importance and usage are continuously growing despite the evolution of mobile applications, social networks, etc. Emails are used on both the personal and professional levels. They can be considered as official documents in communication among users. Emails’ data mining and analysis can be conducted for several purposes such as: Spam detection and classification, subject classification, etc. In this paper, a large set of personal emails is used for the purpose of folder and subject classifications. Algorithms are developed to perform clustering and classification for this large text collection. Classification based on NGram is shown to be the best for such large text collection especially as text is Bi-language (i.e. with English and Arabic content.

  12. Optimal Decision Fusion for Urban Land-Use/Land-Cover Classification Based on Adaptive Differential Evolution Using Hyperspectral and LiDAR Data

    Directory of Open Access Journals (Sweden)

    Yanfei Zhong

    2017-08-01

    Full Text Available Hyperspectral images and light detection and ranging (LiDAR data have, respectively, the high spectral resolution and accurate elevation information required for urban land-use/land-cover (LULC classification. To combine the respective advantages of hyperspectral and LiDAR data, this paper proposes an optimal decision fusion method based on adaptive differential evolution, namely ODF-ADE, for urban LULC classification. In the ODF-ADE framework the normalized difference vegetation index (NDVI, gray-level co-occurrence matrix (GLCM and digital surface model (DSM are extracted to form the feature map. The three different classifiers of the maximum likelihood classifier (MLC, support vector machine (SVM and multinomial logistic regression (MLR are used to classify the extracted features. To find the optimal weights for the different classification maps, weighted voting is used to obtain the classification result and the weights of each classification map are optimized by the differential evolution algorithm which uses a self-adaptive strategy to obtain the parameter adaptively. The final classification map is obtained after post-processing based on conditional random fields (CRF. The experimental results confirm that the proposed algorithm is very effective in urban LULC classification.

  13. Weight loss in a patient with polycystic kidney disease: when liver cysts are no longer innocent bystanders.

    Science.gov (United States)

    Cecere, N; Hakem, S; Demoulin, N; Hubert, C; Jabbour, N; Goffette, P; Pirson, Y; Morelle, J

    2015-10-01

    Autosomal dominant polycystic kidney disease (ADPKD) is the most frequent inherited kidney disorder, and liver involvement represents one of its major extra-renal manifestations. Although asymptomatic in most patients, polycystic liver disease (PLD) can lead to organ compression, severe disability and even become life-threatening, thereby warranting early recognition and appropriate management. We report the case of a 56-year-old woman with ADPKD and severe weight loss secondary to a giant hepatic cyst compressing the pylorus. Partial hepatectomy was required after failure of cyst aspiration and sclerotherapy, and patient's condition improved rapidly. We discuss the presentation and classification of compressing liver cysts, and the available therapeutic alternatives for this potentially severe complication of ADPKD.

  14. Vietnamese Document Representation and Classification

    Science.gov (United States)

    Nguyen, Giang-Son; Gao, Xiaoying; Andreae, Peter

    Vietnamese is very different from English and little research has been done on Vietnamese document classification, or indeed, on any kind of Vietnamese language processing, and only a few small corpora are available for research. We created a large Vietnamese text corpus with about 18000 documents, and manually classified them based on different criteria such as topics and styles, giving several classification tasks of different difficulty levels. This paper introduces a new syllable-based document representation at the morphological level of the language for efficient classification. We tested the representation on our corpus with different classification tasks using six classification algorithms and two feature selection techniques. Our experiments show that the new representation is effective for Vietnamese categorization, and suggest that best performance can be achieved using syllable-pair document representation, an SVM with a polynomial kernel as the learning algorithm, and using Information gain and an external dictionary for feature selection.

  15. Increasing illness severity in very low birth weight infants over a 9-year period

    Directory of Open Access Journals (Sweden)

    Locke Robert G

    2006-02-01

    Full Text Available Abstract Background Recent reports have documented a leveling-off of survival rates in preterm infants through the 1990's. The objective of this study was to determine temporal changes in illness severity in very low birth weight (VLBW infants in relationship to the outcomes of death and/or severe IVH. Methods Cohort study of 1414 VLBW infants cared for in a single level III neonatal intensive care unit in Delaware from 1993–2002. Infants were divided into consecutive 3-year cohorts. Illness severity was measured by two objective methods: the Score for Neonatal Acute Physiology (SNAP, based on data from the 1st day of life, and total thyroxine (T4, measured on the 5th day of life. Death before hospital discharge and severe intraventricular hemorrhage (IVH were investigated in the study sample in relation to illness severity. The fetal death rate was also investigated. Statistical analyses included both univariate and multivariate analysis. Results Illness severity, as measured by SNAP and T4, increased steadily over the 9-year study period with an associated increase in severe IVH and the combined outcome of death and/or severe IVH. During the final 3 years of the study, the observed increase in illness severity accounted for 86% (95% CI 57–116% of the variability in the increase in death and/or severe IVH. The fetal death rate dropped from 7.8/1000 (1993–1996 to 5.3/1000 (1999–2002, p = .01 over the course of the study. Conclusion These data demonstrate a progressive increase in illness in VLBW infants over time, associated with an increase in death and/or severe IVH. We speculate that the observed decrease in fetal death, and the increase in neonatal illness, mortality and/or severe IVH over time represent a shift of severely compromised patients that now survive the fetal time period and are presented for care in the neonatal unit.

  16. Burke-Fahn-Marsden dystonia severity, Gross Motor, Manual Ability, and Communication Function Classification scales in childhood hyperkinetic movement disorders including cerebral palsy: a 'Rosetta Stone' study.

    Science.gov (United States)

    Elze, Markus C; Gimeno, Hortensia; Tustin, Kylee; Baker, Lesley; Lumsden, Daniel E; Hutton, Jane L; Lin, Jean-Pierre S-M

    2016-02-01

    Hyperkinetic movement disorders (HMDs) can be assessed using impairment-based scales or functional classifications. The Burke-Fahn-Marsden Dystonia Rating Scale-movement (BFM-M) evaluates dystonia impairment, but may not reflect functional ability. The Gross Motor Function Classification System (GMFCS), Manual Ability Classification System (MACS), and Communication Function Classification System (CFCS) are widely used in the literature on cerebral palsy to classify functional ability, but not in childhood movement disorders. We explore the concordance of these three functional scales in a large sample of paediatric HMDs and the impact of dystonia severity on these scales. Children with HMDs (n=161; median age 10y 3mo, range 2y 6mo-21y) were assessed using the BFM-M, GMFCS, MACS, and CFCS from 2007 to 2013. This cross-sectional study contrasts the information provided by these scales. All four scales were strongly associated (all Spearman's rank correlation coefficient rs >0.72, pdisorders including cerebral palsy can be effectively evaluated using these scales. © 2015 Mac Keith Press.

  17. Low-back electromyography (EMG data-driven load classification for dynamic lifting tasks.

    Directory of Open Access Journals (Sweden)

    Deema Totah

    Full Text Available Numerous devices have been designed to support the back during lifting tasks. To improve the utility of such devices, this research explores the use of preparatory muscle activity to classify muscle loading and initiate appropriate device activation. The goal of this study was to determine the earliest time window that enabled accurate load classification during a dynamic lifting task.Nine subjects performed thirty symmetrical lifts, split evenly across three weight conditions (no-weight, 10-lbs and 24-lbs, while low-back muscle activity data was collected. Seven descriptive statistics features were extracted from 100 ms windows of data. A multinomial logistic regression (MLR classifier was trained and tested, employing leave-one subject out cross-validation, to classify lifted load values. Dimensionality reduction was achieved through feature cross-correlation analysis and greedy feedforward selection. The time of full load support by the subject was defined as load-onset.Regions of highest average classification accuracy started at 200 ms before until 200 ms after load-onset with average accuracies ranging from 80% (±10% to 81% (±7%. The average recall for each class ranged from 69-92%.These inter-subject classification results indicate that preparatory muscle activity can be leveraged to identify the intent to lift a weight up to 100 ms prior to load-onset. The high accuracies shown indicate the potential to utilize intent classification for assistive device applications.Active assistive devices, e.g. exoskeletons, could prevent back injury by off-loading low-back muscles. Early intent classification allows more time for actuators to respond and integrate seamlessly with the user.

  18. Couinaud's classification v.s. Cho's classification. Their feasibility in the right hepatic lobe

    International Nuclear Information System (INIS)

    Shioyama, Yasukazu; Ikeda, Hiroaki; Sato, Motohito; Yoshimi, Fuyo; Kishi, Kazushi; Sato, Morio; Kimura, Masashi

    2008-01-01

    The objective of this study was to investigate if the new classification system proposed by Cho is feasible to clinical usage comparing with the classical Couinaud's one. One hundred consecutive cases of abdominal CT were studied using a 64 or an 8 slice multislice CT and created three dimensional portal vein images for analysis by the Workstation. We applied both Cho's classification and the classical Couinaud's one for each cases according to their definitions. Three diagnostic radiologists assessed their feasibility as category one (unable to classify) to five (clear to classify with total suit with the original classification criteria). And in each cases, we tried to judge whether Cho's or the classical Couinaud' classification could more easily transmit anatomical information. Analyzers could classified portal veins clearly (category 5) in 77 to 80% of cases and clearly (category 5) or almost clearly (category 4) in 86-93% along with both classifications. In the feasibility of classification, there was no statistically significant difference between two classifications. In 15 cases we felt that using Couinaud's classification is more convenient for us to transmit anatomical information to physicians than using Cho's one, because in these cases we noticed two large portal veins ramify from right main portal vein cranialy and caudaly and then we could not classify P5 as a branch of antero-ventral segment (AVS). Conversely in 17 cases we felt Cho's classification is more convenient because we could not divide right posterior branch as P6 and P7 and in these cases the right posterior portal vein ramified to several small branches. The anterior fissure vein was clearly noticed in only 60 cases. Comparing the classical Couinaud's classification and Cho's one in feasility of classification, there was no statistically significant difference. We propose we routinely report hepatic anatomy with the classical Couinauds classification and in the preoperative cases we

  19. Granular loess classification based

    International Nuclear Information System (INIS)

    Browzin, B.S.

    1985-01-01

    This paper discusses how loess might be identified by two index properties: the granulometric composition and the dry unit weight. These two indices are necessary but not always sufficient for identification of loess. On the basis of analyses of samples from three continents, it was concluded that the 0.01-0.5-mm fraction deserves the name loessial fraction. Based on the loessial fraction concept, a granulometric classification of loess is proposed. A triangular chart is used to classify loess

  20. Assessing the Feasibility of a Social Media to Promote Weight Management Engagement in Adolescents with Severe Obesity: Pilot Study.

    Science.gov (United States)

    Prout Parks, Elizabeth; Moore, Reneé H; Li, Ziyi; Bishop-Gilyard, Chanelle T; Garrett, Andrew R; Hill, Douglas L; Bruton, Yasmeen P; Sarwer, David B

    2018-03-19

    Severe obesity in adolescents has deleterious physical and psychological complications necessitating frequent multi-disciplinary clinic visits. Greater treatment engagement has been equated with weight-loss. However, traditional medical weight-loss programs for adolescents have high attrition rates. Social media is widely used by adolescents and may enhance medical weight management engagement and success. The first objective was to examine the acceptability and feasibility of using a private social media group as an adjunct to medical weight management in youth ages 14 to 20 years with severe obesity [body mass index (BMI) ≥ 35 kg/m2]. The second objective was to pilot test the use of social media to improve treatment engagement and decrease attrition rates. In this single arm, 12 week pre-post study, participants attended individual clinic visits and participated in a moderated private social media group that received nutrition, exercise, and behavior change social media communications or "posts" 3 to 4 times/week. Youth commented and/or liked posts from the moderator and each other. Social media engagement was measured with the number of likes and comments on social media. Clinic attrition was compared, measuring clinic visit attendance 12 weeks prior, during, and after the intervention with mixed linear regression models. Correlations of social media engagement with changes from baseline for BMI, BMI-z score, and psychosocial measures were fit. All 13 enrolled youth completed the study and reported that the group was enjoyable, helpful, reinforced their weight management program, and would recommend using social media to support other youth. The pilot trial was acceptable and feasible. Youth mean weekly engagement (likes + comments) in social media was greater than once a day (8.6 ±3.6). Compared to 12 weeks prior to the intervention, there was no significant decrease in clinic visit attendance at the end of the intervention (M=.231, P=.69) or 12 weeks at

  1. Semi-supervised Probabilistic Distance Clustering and the Uncertainty of Classification

    Science.gov (United States)

    Iyigun, Cem; Ben-Israel, Adi

    Semi-supervised clustering is an attempt to reconcile clustering (unsupervised learning) and classification (supervised learning, using prior information on the data). These two modes of data analysis are combined in a parameterized model, the parameter θ ∈ [0, 1] is the weight attributed to the prior information, θ = 0 corresponding to clustering, and θ = 1 to classification. The results (cluster centers, classification rule) depend on the parameter θ, an insensitivity to θ indicates that the prior information is in agreement with the intrinsic cluster structure, and is otherwise redundant. This explains why some data sets (such as the Wisconsin breast cancer data, Merz and Murphy, UCI repository of machine learning databases, University of California, Irvine, CA) give good results for all reasonable classification methods. The uncertainty of classification is represented here by the geometric mean of the membership probabilities, shown to be an entropic distance related to the Kullback-Leibler divergence.

  2. The Dysexecutive Questionnaire advanced: item and test score characteristics, 4-factor solution, and severity classification.

    Science.gov (United States)

    Bodenburg, Sebastian; Dopslaff, Nina

    2008-01-01

    The Dysexecutive Questionnaire (DEX, , Behavioral assessment of the dysexecutive syndrome, 1996) is a standardized instrument to measure possible behavioral changes as a result of the dysexecutive syndrome. Although initially intended only as a qualitative instrument, the DEX has also been used increasingly to address quantitative problems. Until now there have not been more fundamental statistical analyses of the questionnaire's testing quality. The present study is based on an unselected sample of 191 patients with acquired brain injury and reports on the data relating to the quality of the items, the reliability and the factorial structure of the DEX. Item 3 displayed too great an item difficulty, whereas item 11 was not sufficiently discriminating. The DEX's reliability in self-rating is r = 0.85. In addition to presenting the statistical values of the tests, a clinical severity classification of the overall scores of the 4 found factors and of the questionnaire as a whole is carried out on the basis of quartile standards.

  3. Seizure classification in EEG signals utilizing Hilbert-Huang transform

    Directory of Open Access Journals (Sweden)

    Abdulhay Enas W

    2011-05-01

    Full Text Available Abstract Background Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG signals. Method Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering. Results The t-test results in a P-value Conclusion An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted instantaneous information of every IMF, but it would be most likely a hard task if only the average value is used. Extra benefits of this proposed system include low cost, and ease of interface. All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool.

  4. Seizure classification in EEG signals utilizing Hilbert-Huang transform.

    Science.gov (United States)

    Oweis, Rami J; Abdulhay, Enas W

    2011-05-24

    Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals. Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering. The t-test results in a P-value with respect to its fast response and ease to use. An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted instantaneous information of every IMF, but it would be most likely a hard task if only the average value is used. Extra benefits of this proposed system include low cost, and ease of interface. All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool.

  5. Diffusion-weighted MR and apparent diffusion coefficient in the evaluation of severe brain injury

    International Nuclear Information System (INIS)

    Nakahara, M.; Ericson, K.; Bellander, B.M.

    2001-01-01

    Purpose: To study apparent diffusion coefficient (ADC) maps in severely brain-injured patients. Material and Methods: Four deeply comatose patients with severe brain injury were investigated with single-shot, diffusion-weighted, spin-echo echo planar imaging. The tetrahedral diffusion gradient configuration and four iterations of a set of b-values (one time of 0 mm2/s, and four times of 1000 mm2/s) were used to create isotropic ADC maps with high signal-to-noise ratio. ADC values of gray and white matter were compared among patients and 4 reference subjects. Results: one patient was diagnosed as clinically brain dead after the MR examination. The patient's ADC values of gray and white matter were significantly lower than those of 3 other brain-injured patients. In addition the ADC value of white matter was significantly lower than that of gray matter. Conclusion: The patient with fatal outcome shortly after MR examination differed significantly from other patients with severe brain injury but non-fatal outcome, with regard to ADC values in gray and white matter. This might indicate a prognostic value of ADC maps in the evaluation of traumatic brain injury

  6. Modification of Hidden Layer Weight in Extreme Learning Machine Using Gain Ratio

    Directory of Open Access Journals (Sweden)

    Anggraeny Fetty Tri

    2016-01-01

    Full Text Available Extreme Learning Machine (ELM is a method of learning feed forward neural network quickly and has a fairly good accuracy. This method is devoted to a feed forward neural network with one hidden layer where the parameters (i.e. weight and bias are adjusted one time randomly at the beginning of the learning process. In neural network, the input layer is connected to all characteristics/features, and the output layer is connected to all classes of species. This research used three datasets from UCI database, which were Iris, Breast Wisconsin, and Dermatology, with each dataset having several features. Each characteristic/feature of the data has a role in the process of classification levels, starting from the most influencing role to non-influencing at all. Gain ratio was used to extract each feature role on each datasets. Gain ratio is a method to extract feature role in order to develop a decision tree structure. In this study, ELM structure has been modified, where the random weights of the hidden layer were adjusted to the level of each feature role in determining the species class, so as to improve the level of training and testing accuracy. The proposed method has higher classification accuracy rate than basic ELM on all three datasets, which were 99%, 96%, and 82%, respectively.

  7. 75 FR 78246 - Medicare Program; Re-Chartering of the Advisory Panel on Ambulatory Payment Classification (APC...

    Science.gov (United States)

    2010-12-15

    ...] Medicare Program; Re-Chartering of the Advisory Panel on Ambulatory Payment Classification (APC) Groups... announces the re-chartering of the Advisory Panel on Ambulatory Payment Classification (APC) Groups (the... (APC) groups and their associated weights established under the Medicare hospital Outpatient...

  8. High Adherence to CPAP Treatment Does Not Prevent the Continuation of Weight Gain among Severely Obese OSAS Patients

    Science.gov (United States)

    Myllylä, Minna; Kurki, Samu; Anttalainen, Ulla; Saaresranta, Tarja; Laitinen, Tarja

    2016-01-01

    Study Objectives: Obstructive sleep apnea syndrome (OSAS) patients benefit from continuous positive airway pressure (CPAP) treatment in a dose-response manner. We determined adherence and weight control, as well as their predictors, among long-term CPAP users. Methods: Cohort of 1,023 OSAS patients had used CPAP on average of 6.6 ± 1.2 years. BMI was determined at baseline and at follow-up visits. There were 7.4 ± 1.7 BMI and 6.5 ± 1.8 CPAP usage measurements per patient on average. Using the Bayesian hierarchical model, we determined the patients' individual trends of BMI and adherence development. Patients with significantly increasing or decreasing trends were identified at the posterior probability level of > 90%. Results: The mean age in the cohort was 55.6 ± 9.8 years, BMI 33.5 ± 6.4 kg/m2, apnea-hypopnea index 33.7 ± 23.1, and CPAP usage 6.0 ± 1.8 h/day. The majority of patients had no significant change in BMI (mean annual weight gain 0.04 ± 0.29 kg/m2) or CPAP adherence (mean annual increase 11.4 ± 7.0 min/day). However, at the individual level, 10% of the patients showed significant annual weight gain (0.63 ± 0.35 kg/m2) during the 5-year follow-up period. At baseline these patients were already more severely obese (mean BMI 40.0 ± 5.9 kg/m2) despite being younger (mean 50.9 ± 9.5 years) than the rest of the cohort. Conclusions: In the majority of CPAP-treated OSAS patients, weight did not significantly change but gained slightly slower than in age-matched population in general. However, in 10% of patients, high adherence to CPAP treatment did not prevent the continuation of weight gain. These patients present a high-risk group for OSAS-related multimorbidity later in life. Citation: Myllylä M, Kurki S, Anttalainen U, Saaresranta T, Laitinen T. High adherence to CPAP treatment does not prevent the continuation of weight gain among severely obese OSAS patients. J Clin Sleep Med 2016;12(4):519–528. PMID:26888588

  9. Mortality in severe trauma patients attended by emergency services in Navarre, Spain: validation of a new prediction model and comparison with the Revised Injury Severity Classification Score II.

    Science.gov (United States)

    Ali Ali, Bismil; Lefering, Rolf; Fortún Moral, Mariano; Belzunegui Otano, Tomás

    2018-01-01

    To validate the Mortality Prediction Model of Navarre (MPMN) to predict death after severe trauma and compare it to the Revised Injury Severity Classification Score II (RISCII). Retrospective analysis of a cohort of severe trauma patients (New Injury Severity Score >15) who were attended by emergency services in the Spanish autonomous community of Navarre between 2013 and 2015. The outcome variable was 30-day all-cause mortality. Risk was calculated with the MPMN and the RISCII. The performance of each model was assessed with the area under the receiver operating characteristic (ROC) curve and precision with respect to observed mortality. Calibration was assessed with the Hosmer-Lemeshow test. We included 516 patients. The mean (SD) age was 56 (23) years, and 363 (70%) were males. Ninety patients (17.4%) died within 30 days. The 30-day mortality rates predicted by the MPMN and RISCII were 16.4% and 15.4%, respectively. The areas under the ROC curves were 0.925 (95% CI, 0.902-0.952) for the MPMN and 0.941 (95% CI, 0.921-0.962) for the RISCII (P=0.269, DeLong test). Calibration statistics were 13.6 (P=.09) for the MPMN and 8.9 (P=.35) for the RISCII. Both the MPMN and the RISCII show good ability to discriminate risk and predict 30-day all-cause mortality in severe trauma patients.

  10. Shellfish Feeding Experiments, Filter Weight and Tissue Weight

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Particulate matter removal by shellfish was quantified in several geographic locations, across several years. Data include filter and shellfish tissue weights.

  11. Predicting fire severity using surface fuels and moisture

    Science.gov (United States)

    Pamela G. Sikkink; Robert E. Keane

    2012-01-01

    Fire severity classifications have been used extensively in fire management over the last 30 years to describe specific environmental or ecological impacts of fire on fuels, vegetation, wildlife, and soils in recently burned areas. New fire severity classifications need to be more objective, predictive, and ultimately more useful to fire management and planning. Our...

  12. A statistical approach to root system classification.

    Directory of Open Access Journals (Sweden)

    Gernot eBodner

    2013-08-01

    Full Text Available Plant root systems have a key role in ecology and agronomy. In spite of fast increase in root studies, still there is no classification that allows distinguishing among distinctive characteristics within the diversity of rooting strategies. Our hypothesis is that a multivariate approach for plant functional type identification in ecology can be applied to the classification of root systems. We demonstrate that combining principal component and cluster analysis yields a meaningful classification of rooting types based on morphological traits. The classification method presented is based on a data-defined statistical procedure without a priori decision on the classifiers. Biplot inspection is used to determine key traits and to ensure stability in cluster based grouping. The classification method is exemplified with simulated root architectures and morphological field data. Simulated root architectures showed that morphological attributes with spatial distribution parameters capture most distinctive features within root system diversity. While developmental type (tap vs. shoot-borne systems is a strong, but coarse classifier, topological traits provide the most detailed differentiation among distinctive groups. Adequacy of commonly available morphologic traits for classification is supported by field data. Three rooting types emerged from measured data, distinguished by diameter/weight, density and spatial distribution respectively. Similarity of root systems within distinctive groups was the joint result of phylogenetic relation and environmental as well as human selection pressure. We concluded that the data-define classification is appropriate for integration of knowledge obtained with different root measurement methods and at various scales. Currently root morphology is the most promising basis for classification due to widely used common measurement protocols. To capture details of root diversity efforts in architectural measurement

  13. Validity of the American Board of Orthodontics Discrepancy Index and the Peer Assessment Rating Index for comprehensive evaluation of malocclusion severity.

    Science.gov (United States)

    Liu, S; Oh, H; Chambers, D W; Baumrind, S; Xu, T

    2017-08-01

    To assess the validity of the American Board of Orthodontics Discrepancy Index (ABO-DI) and Peer Assessment Rating (PAR) Index in evaluating malocclusion severity in Chinese orthodontic patients. A stratified random sample of 120 orthodontic patients based on Angle classification was collected from six university orthodontic centres. Sixty-nine orthodontists rated malocclusion severity on a five-point scale by assessing a full set of pre-treatment records for each case and listed reasons for their decision. Their judgement was then compared with ABO-DI and PAR scores determined by three calibrated examiners. Excellent interexaminer reliability of clinician judgement, ABO-DI and PAR index was demonstrated by the Intraclass Correlation Coefficient (rho= 0.995, 0.990 and 0.964, respectively). Both the ABO-DI and US-PAR index showed good correlation with clinician judgement (r=.700 and r=.707, respectively). There was variability among the different Angle classifications: the ABO-DI showed the highest correlation with clinician judgement in Class II patients (r=.780), whereas the US-PAR index showed the highest correlation with clinician judgement in Class III patients (r=.710). Both indices demonstrated the lowest correlations with clinician judgement in Class I patients. With strong interexaminer agreement, the panel consensus was used for validating the ABO-DI and US-PAR index for malocclusion severity. Overall, the ABO-DI and US-PAR index were reliable for measuring malocclusion severity with significantly variable weightings for different Angle classifications. Further modification of the indices for different Angle classification may be indicated. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  14. Characterizing severe obesity in children and youth referred for weight management.

    Science.gov (United States)

    Salawi, Hebah A; Ambler, Kathryn A; Padwal, Rajdeep S; Mager, Diana R; Chan, Catherine B; Ball, Geoff D C

    2014-06-19

    Severe obesity (SO) in pediatrics has become increasing prevalent in recent decades.The objective of our study was to examine differences in demographic, anthropometric, cardiometabolic, and lifestyle variables in children and youth with SO versus their less overweight/obese (OW/OB) peers. A retrospective medical record review of 6-19 year old participants enrolled in an outpatient pediatric weight management clinic was conducted. SO (body mass index [BMI] ≥99(th) percentile) and OW/OB (BMI ≥85(th) and $50,000/year (65.7%). The SO group (n = 161) had lower HDL-cholesterol and higher liver enzymes, insulin resistance and blood pressure than the OW/OB group (n = 184; all p youth in the SO group failed to meet moderate-to-vigorous physical activity (48.4% vs 31.9%) and leisure-time-screen-time recommendations (43.4% vs 28.3%; both p youth with SO have a worse cardiometabolic profile and less favorable lifestyle habits than their OW/OB peers. These differences emphasize the heightened obesity-related health risks associated with SO in the pediatric years.

  15. Discussion on the safety classification of nuclear safety mechanical equipment

    International Nuclear Information System (INIS)

    Shen Wei

    2010-01-01

    The purpose and definition of the equipment safety classification in nuclear plant are introduced. The differences of several safety classification criterions are compared, and the object of safety classification is determined. According to the regulation, the definition and category of the safety functions are represented. The safety classification method, safety classification process, safety class interface, and the requirement for the safety class mechanical equipment are explored. At last, the relation of the safety classification between the mechanical and electrical equipment is presented, and the relation of the safety classification between mechanical equipment and system is also presented. (author)

  16. Classification and valuation of postoperative complications in a randomized trial of open versus laparoscopic ventral herniorrhaphy.

    Science.gov (United States)

    Kaafarani, H M A; Hur, K; Campasano, M; Reda, D J; Itani, K M F

    2010-06-01

    Generic instruments used for the valuation of health states (e.g., EuroQol) often lack sensitivity to notable differences that are relevant to particular diseases or interventions. We developed a valuation methodology specifically for complications following ventral incisional herniorrhaphy (VIH). Between 2004 and 2006, 146 patients were prospectively randomized to undergo laparoscopic (n = 73) or open (n = 73) VIH. The primary outcome of the trial was complications at 8 weeks. A three-step methodology was used to assign severity weights to complications. First, each complication was graded using the Clavien classification. Second, five reviewers were asked to independently and directly rate their perception of the severity of each class using a non-categorized visual analog scale. Zero represented an uncomplicated postoperative course, while 100 represented postoperative death. Third, the median, lowest, and highest values assigned to each class of complications were used to derive weighted complication scores for open and laparoscopic VIH. Open VIH had more complications than laparoscopic VIH (47.9 vs. 31.5%, respectively; P = 0.026). However, complications of laparoscopic VIH were more severe than those of open VIH. Non-parametric analysis revealed a statistically higher weighted complication score for open VIH (interquartile range: 0-20 for open vs. 0-10 for laparoscopic; P = 0.049). In the sensitivity analysis, similar results were obtained using the median, highest, and lowest weights. We describe a new methodology for the valuation of complications following VIH that allows a direct outcome comparison of procedures with different complication profiles. Further testing of the validity, reliability, and generalizability of this method is warranted.

  17. Disjoint hypercyclicity of weighted composition operators

    Indian Academy of Sciences (India)

    Hypercyclicity; supercyclicity; disjoint hypercyclicity; disjoint super- cyclicity; weighted composition operators; Hilbert space. 2010 Mathematics Subject Classification. 47A16, 47B33, 47B38. 1. Introduction. Let X be a topological vector space and T a bounded linear operator on X. The T-orbit of a vector x ∈ X is the set. O(x,T) ...

  18. Automatic detection and classification of breast tumors in ultrasonic images using texture and morphological features.

    Science.gov (United States)

    Su, Yanni; Wang, Yuanyuan; Jiao, Jing; Guo, Yi

    2011-01-01

    Due to severe presence of speckle noise, poor image contrast and irregular lesion shape, it is challenging to build a fully automatic detection and classification system for breast ultrasonic images. In this paper, a novel and effective computer-aided method including generation of a region of interest (ROI), segmentation and classification of breast tumor is proposed without any manual intervention. By incorporating local features of texture and position, a ROI is firstly detected using a self-organizing map neural network. Then a modified Normalized Cut approach considering the weighted neighborhood gray values is proposed to partition the ROI into clusters and get the initial boundary. In addition, a regional-fitting active contour model is used to adjust the few inaccurate initial boundaries for the final segmentation. Finally, three textures and five morphologic features are extracted from each breast tumor; whereby a highly efficient Affinity Propagation clustering is used to fulfill the malignancy and benign classification for an existing database without any training process. The proposed system is validated by 132 cases (67 benignancies and 65 malignancies) with its performance compared to traditional methods such as level set segmentation, artificial neural network classifiers, and so forth. Experiment results show that the proposed system, which needs no training procedure or manual interference, performs best in detection and classification of ultrasonic breast tumors, while having the lowest computation complexity.

  19. A classification system for tableting behaviors of binary powder mixtures

    Directory of Open Access Journals (Sweden)

    Changquan Calvin Sun

    2016-08-01

    Full Text Available The ability to predict tableting properties of a powder mixture from individual components is of both fundamental and practical importance to the efficient formulation development of tablet products. A common tableting classification system (TCS of binary powder mixtures facilitates the systematic development of new knowledge in this direction. Based on the dependence of tablet tensile strength on weight fraction in a binary mixture, three main types of tableting behavior are identified. Each type is further divided to arrive at a total of 15 sub-classes. The proposed classification system lays a framework for a better understanding of powder interactions during compaction. Potential applications and limitations of this classification system are discussed.

  20. Body weight satisfaction and disordered eating among youth who are active in sport in Singapore

    Directory of Open Access Journals (Sweden)

    Michael Chia

    2015-04-01

    Full Text Available Purpose : The research examined the relationship between body weight satisfaction and disordered eating among youth who are active in sport in Singapore. Method : 137 youths (82 boys and 55 girls; age 12-13 enrolled in school sport completed two self-report questionnaires- SCOFF for disordered eating and body weight satisfaction- on two separate occasions that were six months apart (T1 vs. T2. Results : Body mass index for age classifications revealed that 5.1% were severely underweight; 1.5% underweight; 88.3% acceptable weight; 4.4% overweight and 0.7% were severely overweight. Conclusions : (i the prevalence of disordered eating was 46% at baseline measurement and this remained stable at 45.3% six months later; (ii there was no sex difference for disordered eating on the two measurement occasions (T1 vs. T2, p>0.05; (iii the prevalence of youths unsure of their bodyweight satisfaction was 26.6-21.2% which compared to 88.3% adjudged to be of healthy weight; across T1 and T2, more male subjects wanted to gain bodyweight while more female subjects wanted to lose bodyweight; and (iv subjects who were dissatisfied with their bodyweight had significantly greater odds of being at risk for developing DE. Holistic education programmes based upon body image and nutrition, are recommended.

  1. [Effect of Low Molecular Weight Heparin Calcium Combined Compound Danshen Injection on Perinatal Outcomes of Nephrotic Syndrome Patients with Early Onset Severe Pre-eclampsia].

    Science.gov (United States)

    Tong, Chong-xin; Xing, Xiao-fen; Qiao, Shu-hua; Liu, Lin; Shan, Ling

    2015-08-01

    To observe the effect of low molecular weight heparin calcium (LMWHC) combined Compound Danshen Injection (DI) on nephrotic syndrome patients with early onset severe preeclampsia. Totally 80 nephrotic syndrome patients with early onset severe pre-eclampsia were randomly assigned to four groups voluntarily, i.e., Group A (22 cases, treated by magnesium sulfate), B (19 cases, treated by magnesium sulfate plus LMWHC), C (21 cases, magnesium sulfate plus DI), D (18 cases, magnesium sulfate plus LMWHC and DI). Umbilical arterial S/D ratios, amniotic fluid index (AFI), prolonged gestational age, placenta weight, neonatal weight, and Apgar score were compared among the four groups. Compared with before treatment in the same group, umbilical arterial S/D ratios decreased in the four groups (P <0. 05). AFI decreased in Group A, while it increased in Group B, C, and D (P<0. 05). Compared with Group A at the same time point, umbilical arterial S/D ratios decreased, and AFI increased in Group B, C, and D (P <0. 01 , P <0. 05). Prolonged gestational age and neonatal weight were increased in Group B, C, and D (P <0. 01, P <0. 05). Placenta weight were increased in Group B and D (P <0. 05). Apgar scores at 1 and 5 min were improved in Group D (P <0. 05). Compared with Group B and C at the same time point, umbilical arterial S/D ratios decreased, and AFI increased in Group D (P<0. 05). Compared with Group B, prolonged gestational age and placenta weight were decreased in Group C, but prolonged gestational age and placenta weight were increased in Group D (P <0.05). Compared with Group C, prolonged gestational age, placenta weight, and neonatal weight were increased in Group D (P <0. 05). Treatment of nephrotic syndrome patients with early onset severe pre-eclampsia by LMWHC combined DI could prolong gestational ages, obviously improve prenatal outcomes, with better effect obtained than using any of them alone.

  2. Overweight and Obesity Prevalence Among School-Aged Nunavik Inuit Children According to Three Body Mass Index Classification Systems.

    Science.gov (United States)

    Medehouenou, Thierry Comlan Marc; Ayotte, Pierre; St-Jean, Audray; Meziou, Salma; Roy, Cynthia; Muckle, Gina; Lucas, Michel

    2015-07-01

    Little is known about the suitability of three commonly used body mass index (BMI) classification system for Indigenous children. This study aims to estimate overweight and obesity prevalence among school-aged Nunavik Inuit children according to International Obesity Task Force (IOTF), Centers for Disease Control and Prevention (CDC), and World Health Organization (WHO) BMI classification systems, to measure agreement between those classification systems, and to investigate whether BMI status as defined by these classification systems is associated with levels of metabolic and inflammatory biomarkers. Data were collected on 290 school-aged children (aged 8-14 years; 50.7% girls) from the Nunavik Child Development Study with data collected in 2005-2010. Anthropometric parameters were measured and blood sampled. Participants were classified as normal weight, overweight, and obese according to BMI classification systems. Weighted kappa (κw) statistics assessed agreement between different BMI classification systems, and multivariate analysis of variance ascertained their relationship with metabolic and inflammatory biomarkers. The combined prevalence rate of overweight/obesity was 26.9% (with 6.6% obesity) with IOTF, 24.1% (11.0%) with CDC, and 40.4% (12.8%) with WHO classification systems. Agreement was the highest between IOTF and CDC (κw = .87) classifications, and substantial for IOTF and WHO (κw = .69) and for CDC and WHO (κw = .73). Insulin and high-sensitivity C-reactive protein plasma levels were significantly higher from normal weight to obesity, regardless of classification system. Among obese subjects, higher insulin level was observed with IOTF. Compared with other systems, IOTF classification appears to be more specific to identify overweight and obesity in Inuit children. Copyright © 2015 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

  3. Monitoring Severe Accidents Using AI Techniques

    International Nuclear Information System (INIS)

    No, Young Gyu; Kim, Ju Hyun; Na, Man Gyun; Ahn, Kwang Il

    2011-01-01

    It is very difficult for nuclear power plant operators to monitor and identify the major severe accident scenarios following an initiating event by staring at temporal trends of important parameters. The objective of this study is to develop and verify the monitoring for severe accidents using artificial intelligence (AI) techniques such as support vector classification (SVC), probabilistic neural network (PNN), group method of data handling (GMDH) and fuzzy neural network (FNN). The SVC and PNN are used for event classification among the severe accidents. Also, GMDH and FNN are used to monitor for severe accidents. The inputs to AI techniques are initial time-integrated values obtained by integrating measurement signals during a short time interval after reactor scram. In this study, 3 types of initiating events such as the hot-leg LOCA, the cold-leg LOCA and SGTR are considered and it is verified how well the proposed scenario identification algorithm using the GMDH and FNN models identifies the timings when the reactor core will be uncovered, when CET will exceed 1200 .deg. F and when the reactor vessel will fail. In cases that an initiating event develops into a severe accident, the proposed algorithm showed accurate classification of initiating events. Also, it well predicted timings for important occurrences during severe accident progression scenarios, which is very helpful for operators to perform severe accident management

  4. Predictors of severe late radiotherapy-related toxicity after hyperfractionated radiotherapy with or without concomitant cisplatin in locally advanced head and neck cancer. Secondary retrospective analysis of a randomized phase III trial (SAKK 10/94)

    International Nuclear Information System (INIS)

    Ghadjar, Pirus; Simcock, Mathew; Zimmermann, Frank; Betz, Michael; Bodis, Stephan; Bernier, Jacques; Studer, Gabriela; Aebersold, Daniel M.

    2012-01-01

    Background and purpose: This secondary analysis was performed to identify predictive factors for severe late radiotherapy (RT)-related toxicity after treatment with hyperfractionated RT +/− concomitant cisplatin in locally advanced head and neck cancer. Materials and methods: Patients were retrospectively analyzed from the previously reported randomized phase III trial: SAKK 10/94. Severe late RT-related toxicity was defined as late RTOG ⩾ grade 3 toxicity starting 3 months after end of RT and/or potential treatment-related death within 3 years of randomization. Results: Two hundred and thirteen randomized patients were analyzed; 84 (39%) experienced severe late RT-related toxicity. With median follow-up of 9.7 years (range, 0.4–15.4 years), median time to severe late RT-related toxicity was 9.6 years. In the univariate Cox proportional hazards model the following variables were associated with severe late RT-related toxicity: advanced N-classification (p < 0.001); technically unresectable disease (p = 0.04); weight loss ratio (p = 0.003); supportive measures (p = 0.009) and severe acute dysphagia (p = 0.001). In the subsequent multivariate analysis all variables except use of supportive measures remained statistically significant. Conclusions: Chemotherapy did not appear to affect severe late RT-related toxicity, but advanced N-classification, technically unresectable disease, weight loss ratio, and severe acute dysphagia were independent predictive factors for severe late RT-related toxicity.

  5. Transport package response to severe thermal events, part 2: legal weight truck cask

    International Nuclear Information System (INIS)

    Greiner, M.; Faulkner, R.J.; Jin, Y.Y.

    1998-01-01

    The response of intact and damaged versions of the GA-4 Legal Weight Truck Cask to a range of severe thermal events is simulated using finite element computer analysis. The minimum fire durations that cause the containment seals and fuel cladding to reach their respective temperature limits are evaluated for a range of hydrocarbon fire temperatures. Containment seals reach their temperature limit in shorter duration fires as compared to the cladding, for both an undamaged package and a cask whose impact limiter is destroyed moments before the fire begins. However, if the neutron shield is destroyed, the cladding reaches its limit first in high temperature fires. A margin of safety exists between the conditions of the IAEA regulatory fire test and all of the performance envelopes calculated in this work. (author)

  6. [Nordic accident classification system used in the Danish National Hospital Registration System to register causes of severe traumatic brain injury].

    Science.gov (United States)

    Engberg, Aase Worsaa; Penninga, Elisabeth Irene; Teasdale, Thomas William

    2007-11-05

    The purpose was to illustrate the use of the accident classification system worked out by the Nordic Medico-Statistical Committee (NOMESCO). In particular, registration of causes of severe traumatic brain injury according to the system as part of the Danish National Hospital Registration System was studied. The study comprised 117 patients with very severe traumatic brain injury (TBI) admitted to the Brain Injury Unit of the University Hospital in Hvidovre, Copenhagen, from 1 October 2000 to 30 September 2002. Prospective NOMESCO coding at discharge was compared to independent retrospective coding based on hospital records, and to coding from other wards in the Danish National Hospital Registration System. Furthermore, sets of codes in the Danish National Hospital Registration System for consecutive admissions after a particular accident were compared. Identical results of prospective and independent retrospective coding were found for 65% of 588 single codes, and complete sets of codes for the same accident were identical only in 28% of cases. Sets of codes for the first admission in a hospital course corresponded to retrospective coding at the end of the course in only 17% of cases. Accident code sets from different wards, based on the same injury, were identical in only 7% of cases. Prospective coding by the NOMESCO accident classification system proved problematic, both with regard to correctness and completeness. The system--although logical--seems too complicated compared to the resources invested in the coding. The results of this investigation stress the need for better management and for better instruction to those who carry out the registration.

  7. Joint Concept Correlation and Feature-Concept Relevance Learning for Multilabel Classification.

    Science.gov (United States)

    Zhao, Xiaowei; Ma, Zhigang; Li, Zhi; Li, Zhihui

    2018-02-01

    In recent years, multilabel classification has attracted significant attention in multimedia annotation. However, most of the multilabel classification methods focus only on the inherent correlations existing among multiple labels and concepts and ignore the relevance between features and the target concepts. To obtain more robust multilabel classification results, we propose a new multilabel classification method aiming to capture the correlations among multiple concepts by leveraging hypergraph that is proved to be beneficial for relational learning. Moreover, we consider mining feature-concept relevance, which is often overlooked by many multilabel learning algorithms. To better show the feature-concept relevance, we impose a sparsity constraint on the proposed method. We compare the proposed method with several other multilabel classification methods and evaluate the classification performance by mean average precision on several data sets. The experimental results show that the proposed method outperforms the state-of-the-art methods.

  8. A review and analysis of neural networks for classification of remotely sensed multispectral imagery

    Science.gov (United States)

    Paola, Justin D.; Schowengerdt, Robert A.

    1993-01-01

    A literature survey and analysis of the use of neural networks for the classification of remotely sensed multispectral imagery is presented. As part of a brief mathematical review, the backpropagation algorithm, which is the most common method of training multi-layer networks, is discussed with an emphasis on its application to pattern recognition. The analysis is divided into five aspects of neural network classification: (1) input data preprocessing, structure, and encoding; (2) output encoding and extraction of classes; (3) network architecture, (4) training algorithms; and (5) comparisons to conventional classifiers. The advantages of the neural network method over traditional classifiers are its non-parametric nature, arbitrary decision boundary capabilities, easy adaptation to different types of data and input structures, fuzzy output values that can enhance classification, and good generalization for use with multiple images. The disadvantages of the method are slow training time, inconsistent results due to random initial weights, and the requirement of obscure initialization values (e.g., learning rate and hidden layer size). Possible techniques for ameliorating these problems are discussed. It is concluded that, although the neural network method has several unique capabilities, it will become a useful tool in remote sensing only if it is made faster, more predictable, and easier to use.

  9. A new classification scheme of plastic wastes based upon recycling labels.

    Science.gov (United States)

    Özkan, Kemal; Ergin, Semih; Işık, Şahin; Işıklı, Idil

    2015-01-01

    Since recycling of materials is widely assumed to be environmentally and economically beneficial, reliable sorting and processing of waste packaging materials such as plastics is very important for recycling with high efficiency. An automated system that can quickly categorize these materials is certainly needed for obtaining maximum classification while maintaining high throughput. In this paper, first of all, the photographs of the plastic bottles have been taken and several preprocessing steps were carried out. The first preprocessing step is to extract the plastic area of a bottle from the background. Then, the morphological image operations are implemented. These operations are edge detection, noise removal, hole removing, image enhancement, and image segmentation. These morphological operations can be generally defined in terms of the combinations of erosion and dilation. The effect of bottle color as well as label are eliminated using these operations. Secondly, the pixel-wise intensity values of the plastic bottle images have been used together with the most popular subspace and statistical feature extraction methods to construct the feature vectors in this study. Only three types of plastics are considered due to higher existence ratio of them than the other plastic types in the world. The decision mechanism consists of five different feature extraction methods including as Principal Component Analysis (PCA), Kernel PCA (KPCA), Fisher's Linear Discriminant Analysis (FLDA), Singular Value Decomposition (SVD) and Laplacian Eigenmaps (LEMAP) and uses a simple experimental setup with a camera and homogenous backlighting. Due to the giving global solution for a classification problem, Support Vector Machine (SVM) is selected to achieve the classification task and majority voting technique is used as the decision mechanism. This technique equally weights each classification result and assigns the given plastic object to the class that the most classification

  10. Classification Using Markov Blanket for Feature Selection

    DEFF Research Database (Denmark)

    Zeng, Yifeng; Luo, Jian

    2009-01-01

    Selecting relevant features is in demand when a large data set is of interest in a classification task. It produces a tractable number of features that are sufficient and possibly improve the classification performance. This paper studies a statistical method of Markov blanket induction algorithm...... for filtering features and then applies a classifier using the Markov blanket predictors. The Markov blanket contains a minimal subset of relevant features that yields optimal classification performance. We experimentally demonstrate the improved performance of several classifiers using a Markov blanket...... induction as a feature selection method. In addition, we point out an important assumption behind the Markov blanket induction algorithm and show its effect on the classification performance....

  11. (Dis-)solving the Weight Problem in Binge-Eating Disorder: Systemic Insights From Three Treatment Contexts With Weight Stability, Weight Loss, and Weight Acceptance.

    Science.gov (United States)

    Meyer, Lene Bomholt; Waaddegaard, Mette; Lau, Marianne Engelbrecht; Tjørnhøj-Thomsen, Tine

    2018-04-01

    Binge-eating disorder (BED) is a severe eating disorder strongly associated with obesity. Treatments struggle to provide safe and effective ways of addressing weight in a BED context. This study explored a two-phased treatment for BED developed at a major out-patient eating disorder service in Denmark. The study used interviews and participant observations to gain insight into experiences and processes related to weight and body issues in three treatment contexts that addressed weight stability, weight acceptance, and weight loss. Using systems theory, the study proposed a relational weight problem that embeds feelings of non-acceptance due to weight, a merge of weight and identity, and an internalized body- and weight-critical gaze of others. Contrary to critical claims that weight acceptance discourages people with obesity from engaging in weight loss efforts, this study suggests that acceptance and a disentanglement of weight and identity are prerequisites for weight loss for this group.

  12. The classification of the Ricci tensor in the general theory of relativity

    International Nuclear Information System (INIS)

    Cormack, W.J.

    1979-10-01

    A comprehensive classification of the Ricci tensor in General Relativity using several techniques is given and their connection with existing classification studied under the headings; canonical forms for the Ricci tensor, invariant 2-spaces in the classification of the Ricci tensor, Riemannian curvature and the classification of the Riemann and Ricci tensors, and spinor classifications of the Ricci tensor. (U.K.)

  13. Serum cortisol values, superior vena cava flow and illness severity scores in very low birth weight infants.

    LENUS (Irish Health Repository)

    Miletin, J

    2012-02-01

    OBJECTIVE: Recent evidence suggests that high cortisol concentrations are associated with increased morbidity and mortality in very low birth weight (VLBW) infants. Neonatal illness severity and mortality risk scores are reliable in predicting morbidity and mortality. The objectives were (i) to assess the correlation between serum cortisol levels and clinical assessment of multi-organ dysfunction\\/illness severity scores (CRIB II, SNAPPE-II and neonatal multiple organ dysfunction score (NEOMOD)) in first 24 h in VLBW infants and (ii) to assess the relationship between surrogates of end organ blood flow and serum cortisol levels. STUDY DESIGN: A prospective observational cohort study. Neonates with birth weight <1500 g were eligible for enrollment. Echocardiography evaluation of superior vena cava (SVC) flow was carried out in the first 24 h life. Cortisol levels were measured simultaneously and appropriate clinical scores were calculated. RESULT: A total of 54 VLBW neonates were enrolled following parental consent. Two patients were excluded because of congenital malformations. In 14 babies the cortisol value was not simultaneously obtained. The mean birth weight was 1.08 kg, mean gestational age was 27.8 weeks. There was a significant correlation between cortisol and NEOMOD score (P=0.006). There was no correlation between cortisol and CRIB II score (P=0.34), SVC flow (P=0.49) and mean arterial blood pressure respectively (P=0.35). CONCLUSION: There was no correlation between SVC flow and cortisol values or between cortisol and mean blood pressure values. There was a significant correlation between cortisol levels and neonatal organ dysfunction score evaluated suggesting that stressed VLBW infants do mount a cortisol response.

  14. Effect of adjuvant low-molecular-weight heparin therapy on placental hypoxia and cell apoptosis in puerperae with severe preeclampsia

    Directory of Open Access Journals (Sweden)

    Miao Zhou1

    2017-04-01

    Full Text Available Objective: To study the effect of adjuvant low-molecular-weight heparin therapy on placental hypoxia and cell apoptosis in puerperae with severe preeclampsia. Methods: A total of 94 puerperae with severe preeclampsia who received treatment and safely gave birth in our hospital between May 2014 and May 2016 were selected as the research subjects and randomly divided into the LMWH group who received low-molecular-weight heparin combined with conventional symptomatic treatment and the control group who received conventional symptomatic treatment. Before and after treatment, serum was collected respectively to determine the levels of placental hypoxia-related cytokines, and after delivery, the placentas were collected to detect oxidative stress indexes and cell apoptosis indexes. Results: After treatment, serum PLGF and PAPP-A levels of both groups were significantly higher than those before treatment while sFlt-1 and sEng levels were significantly lower than those before treatment, and after treatment, serum PLGF and PAPP-A levels of LMWH group were significantly higher than those of control group while sFlt-1 and sEng levels were significantly lower than those of control group; ROS and RNS levels as well as Fas, FasL, caspase-3 and caspase-8 protein expression in placenta tissue of LMWH group were significantly lower than those of control group while GPx-1, SOD-1 and Trx levels as well as Survivin, XIAP and Bcl-2 protein expression were significantly higher than those of control group. Conclusion: Adjuvant low-molecular-weight heparin therapy can relieve the placental hypoxia, improve oxidative stress reaction and inhibit cell apoptosis in puerperae with severe preeclampsia.

  15. Deep learning for image classification

    Science.gov (United States)

    McCoppin, Ryan; Rizki, Mateen

    2014-06-01

    This paper provides an overview of deep learning and introduces the several subfields of deep learning including a specific tutorial of convolutional neural networks. Traditional methods for learning image features are compared to deep learning techniques. In addition, we present our preliminary classification results, our basic implementation of a convolutional restricted Boltzmann machine on the Mixed National Institute of Standards and Technology database (MNIST), and we explain how to use deep learning networks to assist in our development of a robust gender classification system.

  16. Content Abstract Classification Using Naive Bayes

    Science.gov (United States)

    Latif, Syukriyanto; Suwardoyo, Untung; Aldrin Wihelmus Sanadi, Edwin

    2018-03-01

    This study aims to classify abstract content based on the use of the highest number of words in an abstract content of the English language journals. This research uses a system of text mining technology that extracts text data to search information from a set of documents. Abstract content of 120 data downloaded at www.computer.org. Data grouping consists of three categories: DM (Data Mining), ITS (Intelligent Transport System) and MM (Multimedia). Systems built using naive bayes algorithms to classify abstract journals and feature selection processes using term weighting to give weight to each word. Dimensional reduction techniques to reduce the dimensions of word counts rarely appear in each document based on dimensional reduction test parameters of 10% -90% of 5.344 words. The performance of the classification system is tested by using the Confusion Matrix based on comparative test data and test data. The results showed that the best classification results were obtained during the 75% training data test and 25% test data from the total data. Accuracy rates for categories of DM, ITS and MM were 100%, 100%, 86%. respectively with dimension reduction parameters of 30% and the value of learning rate between 0.1-0.5.

  17. A Java-based tool for the design of classification microarrays

    Directory of Open Access Journals (Sweden)

    Broschat Shira L

    2008-08-01

    Full Text Available Abstract Background Classification microarrays are used for purposes such as identifying strains of bacteria and determining genetic relationships to understand the epidemiology of an infectious disease. For these cases, mixed microarrays, which are composed of DNA from more than one organism, are more effective than conventional microarrays composed of DNA from a single organism. Selection of probes is a key factor in designing successful mixed microarrays because redundant sequences are inefficient and limited representation of diversity can restrict application of the microarray. We have developed a Java-based software tool, called PLASMID, for use in selecting the minimum set of probe sequences needed to classify different groups of plasmids or bacteria. Results The software program was successfully applied to several different sets of data. The utility of PLASMID was illustrated using existing mixed-plasmid microarray data as well as data from a virtual mixed-genome microarray constructed from different strains of Streptococcus. Moreover, use of data from expression microarray experiments demonstrated the generality of PLASMID. Conclusion In this paper we describe a new software tool for selecting a set of probes for a classification microarray. While the tool was developed for the design of mixed microarrays–and mixed-plasmid microarrays in particular–it can also be used to design expression arrays. The user can choose from several clustering methods (including hierarchical, non-hierarchical, and a model-based genetic algorithm, several probe ranking methods, and several different display methods. A novel approach is used for probe redundancy reduction, and probe selection is accomplished via stepwise discriminant analysis. Data can be entered in different formats (including Excel and comma-delimited text, and dendrogram, heat map, and scatter plot images can be saved in several different formats (including jpeg and tiff. Weights

  18. A Java-based tool for the design of classification microarrays.

    Science.gov (United States)

    Meng, Da; Broschat, Shira L; Call, Douglas R

    2008-08-04

    Classification microarrays are used for purposes such as identifying strains of bacteria and determining genetic relationships to understand the epidemiology of an infectious disease. For these cases, mixed microarrays, which are composed of DNA from more than one organism, are more effective than conventional microarrays composed of DNA from a single organism. Selection of probes is a key factor in designing successful mixed microarrays because redundant sequences are inefficient and limited representation of diversity can restrict application of the microarray. We have developed a Java-based software tool, called PLASMID, for use in selecting the minimum set of probe sequences needed to classify different groups of plasmids or bacteria. The software program was successfully applied to several different sets of data. The utility of PLASMID was illustrated using existing mixed-plasmid microarray data as well as data from a virtual mixed-genome microarray constructed from different strains of Streptococcus. Moreover, use of data from expression microarray experiments demonstrated the generality of PLASMID. In this paper we describe a new software tool for selecting a set of probes for a classification microarray. While the tool was developed for the design of mixed microarrays-and mixed-plasmid microarrays in particular-it can also be used to design expression arrays. The user can choose from several clustering methods (including hierarchical, non-hierarchical, and a model-based genetic algorithm), several probe ranking methods, and several different display methods. A novel approach is used for probe redundancy reduction, and probe selection is accomplished via stepwise discriminant analysis. Data can be entered in different formats (including Excel and comma-delimited text), and dendrogram, heat map, and scatter plot images can be saved in several different formats (including jpeg and tiff). Weights generated using stepwise discriminant analysis can be stored for

  19. A new classification scheme of plastic wastes based upon recycling labels

    Energy Technology Data Exchange (ETDEWEB)

    Özkan, Kemal, E-mail: kozkan@ogu.edu.tr [Computer Engineering Dept., Eskişehir Osmangazi University, 26480 Eskişehir (Turkey); Ergin, Semih, E-mail: sergin@ogu.edu.tr [Electrical Electronics Engineering Dept., Eskişehir Osmangazi University, 26480 Eskişehir (Turkey); Işık, Şahin, E-mail: sahini@ogu.edu.tr [Computer Engineering Dept., Eskişehir Osmangazi University, 26480 Eskişehir (Turkey); Işıklı, İdil, E-mail: idil.isikli@bilecik.edu.tr [Electrical Electronics Engineering Dept., Bilecik University, 11210 Bilecik (Turkey)

    2015-01-15

    Highlights: • PET, HPDE or PP types of plastics are considered. • An automated classification of plastic bottles based on the feature extraction and classification methods is performed. • The decision mechanism consists of PCA, Kernel PCA, FLDA, SVD and Laplacian Eigenmaps methods. • SVM is selected to achieve the classification task and majority voting technique is used. - Abstract: Since recycling of materials is widely assumed to be environmentally and economically beneficial, reliable sorting and processing of waste packaging materials such as plastics is very important for recycling with high efficiency. An automated system that can quickly categorize these materials is certainly needed for obtaining maximum classification while maintaining high throughput. In this paper, first of all, the photographs of the plastic bottles have been taken and several preprocessing steps were carried out. The first preprocessing step is to extract the plastic area of a bottle from the background. Then, the morphological image operations are implemented. These operations are edge detection, noise removal, hole removing, image enhancement, and image segmentation. These morphological operations can be generally defined in terms of the combinations of erosion and dilation. The effect of bottle color as well as label are eliminated using these operations. Secondly, the pixel-wise intensity values of the plastic bottle images have been used together with the most popular subspace and statistical feature extraction methods to construct the feature vectors in this study. Only three types of plastics are considered due to higher existence ratio of them than the other plastic types in the world. The decision mechanism consists of five different feature extraction methods including as Principal Component Analysis (PCA), Kernel PCA (KPCA), Fisher’s Linear Discriminant Analysis (FLDA), Singular Value Decomposition (SVD) and Laplacian Eigenmaps (LEMAP) and uses a simple

  20. A new classification scheme of plastic wastes based upon recycling labels

    International Nuclear Information System (INIS)

    Özkan, Kemal; Ergin, Semih; Işık, Şahin; Işıklı, İdil

    2015-01-01

    Highlights: • PET, HPDE or PP types of plastics are considered. • An automated classification of plastic bottles based on the feature extraction and classification methods is performed. • The decision mechanism consists of PCA, Kernel PCA, FLDA, SVD and Laplacian Eigenmaps methods. • SVM is selected to achieve the classification task and majority voting technique is used. - Abstract: Since recycling of materials is widely assumed to be environmentally and economically beneficial, reliable sorting and processing of waste packaging materials such as plastics is very important for recycling with high efficiency. An automated system that can quickly categorize these materials is certainly needed for obtaining maximum classification while maintaining high throughput. In this paper, first of all, the photographs of the plastic bottles have been taken and several preprocessing steps were carried out. The first preprocessing step is to extract the plastic area of a bottle from the background. Then, the morphological image operations are implemented. These operations are edge detection, noise removal, hole removing, image enhancement, and image segmentation. These morphological operations can be generally defined in terms of the combinations of erosion and dilation. The effect of bottle color as well as label are eliminated using these operations. Secondly, the pixel-wise intensity values of the plastic bottle images have been used together with the most popular subspace and statistical feature extraction methods to construct the feature vectors in this study. Only three types of plastics are considered due to higher existence ratio of them than the other plastic types in the world. The decision mechanism consists of five different feature extraction methods including as Principal Component Analysis (PCA), Kernel PCA (KPCA), Fisher’s Linear Discriminant Analysis (FLDA), Singular Value Decomposition (SVD) and Laplacian Eigenmaps (LEMAP) and uses a simple

  1. Management of severe subarachnoid hemorrhage (SAH) with diffusion-weighted imaging in acute stage

    International Nuclear Information System (INIS)

    Shamoto, Hiroshi; Shimizu, Hiroaki; Matsumoto, Yasushi; Fujiwara, Satoru; Tominaga, Teiji

    2007-01-01

    Determining the treatment strategy of severe subarachnoid hemorrhage (SAH) (Hunt and Kosnik Grade 4 and 5) requires objective evaluation to represent severity. In the present study, we investigated the role of diffusion-weighted imaging (DWI) in the acute stage as an objective tool. DWI was performed within 48 hours after the onset and preoperatively in 36 patients who fulfilled following the inclusion criteria: admission Hunt and Kosnik Grade 4 or 5, and Fischer Group 3. Twelve of 14 patients without abnormal findings in DWI underwent surgery in the acute stage. Although 2 of 14 patients with high age were supposed to undergo surgery in the chronic stage, 1 patient died in aneurysmal re-rupture. Glasgow outcome scales (GOS) were good recovery (GR) in 5, moderate disability (MD) in 6, standard deviation (SD) in 1 and D in 2 patients. Thirteen of 22 patients with DWI abnormality had small lesions less than 10 mm in diameter. Twelve of 13 patients underwent surgery in the acute stage, and 1 died of aneurysmal re-rupture while waiting for surgery in the chronic stage. GOS were GR in 3, MD in 4, SD in 3 and D in 3 patients. Although 5 patients with diffuse DWI lesions underwent surgery in the acute stage, 2 were SD and 3 were D. Four patients were supposed to undergo delayed surgery. However, 2 of them died of recurrent hemorrhage while waiting. GOS were SD in 2 and D in 2 patients. The present study indicates that DWI may provide objective evaluation of brain damage in severe SAH. However, since there were varieties of DWI findings and clinical courses, careful decisions must be taken in management of severe SAH patients. (author)

  2. Hypothalamic obesity in patients with craniopharyngioma: Profound changes of several weight regulatory circuits

    Directory of Open Access Journals (Sweden)

    Christian eRoth

    2011-10-01

    Full Text Available One of the most striking examples of dysfunctional hypothalamic signaling of energy homeostasis is observed in patients with hypothalamic lesions leading to hypothalamic obesity (HO. This drastic condition is frequently seen in patients with craniopharyngioma (CP, an embryological tumor located in the hypothalamic and/or pituitary region, frequently causing not only hypopituitarism, but also leading to damage of medial hypothalamic nuclei due to the tumor and its treatment. HO syndrome in CP patients is characterized by fatigue, decreased physical activity, uncontrolled appetite, and morbid obesity, and is associated with insulin and leptin resistance. Mechanisms leading to the profoundly disturbed energy homeostasis are complex. This review summarizes different aspects of important clinical studies as well as data obtained in rodent studies. In addition a model is provided describing how medial hypothalamic lesion can interact simultaneously with several weight regulating circuitries.

  3. Pros and cons of conjoint analysis of discrete choice experiments to define classification and response criteria in rheumatology.

    Science.gov (United States)

    Taylor, William J

    2016-03-01

    Conjoint analysis of choice or preference data has been used in marketing for over 40 years but has appeared in healthcare settings much more recently. It may be a useful technique for applications within the rheumatology field. Conjoint analysis in rheumatology contexts has mainly used the approaches implemented in 1000Minds Ltd, Dunedin, New Zealand, Sawtooth Software, Orem UT, USA. Examples include classification criteria, composite response criteria, service prioritization tools and utilities assessment. Limitations imposed by very many attributes can be managed using new techniques. Conjoint analysis studies of classification and response criteria suggest that the assumption of equal weighting of attributes cannot be met, which challenges traditional approaches to composite criteria construction. Weights elicited through choice experiments with experts can derive more accurate classification criteria, than unweighted criteria. Studies that find significant variation in attribute weights for composite response criteria for gout make construction of such criteria problematic. Better understanding of various multiattribute phenomena is likely to increase with increased use of conjoint analysis, especially when the attributes concern individual perceptions or opinions. In addition to classification criteria, some applications for conjoint analysis that are emerging in rheumatology include prioritization tools, remission criteria, and utilities for life areas.

  4. Waste classification and methods applied to specific disposal sites

    International Nuclear Information System (INIS)

    Rogers, V.C.

    1979-01-01

    An adequate definition of the classes of radioactive wastes is necessary to regulating the disposal of radioactive wastes. A classification system is proposed in which wastes are classified according to characteristics relating to their disposal. Several specific sites are analyzed with the methodology in order to gain insights into the classification of radioactive wastes. Also presented is the analysis of ocean dumping as it applies to waste classification. 5 refs

  5. sw-SVM: sensor weighting support vector machines for EEG-based brain-computer interfaces.

    Science.gov (United States)

    Jrad, N; Congedo, M; Phlypo, R; Rousseau, S; Flamary, R; Yger, F; Rakotomamonjy, A

    2011-10-01

    In many machine learning applications, like brain-computer interfaces (BCI), high-dimensional sensor array data are available. Sensor measurements are often highly correlated and signal-to-noise ratio is not homogeneously spread across sensors. Thus, collected data are highly variable and discrimination tasks are challenging. In this work, we focus on sensor weighting as an efficient tool to improve the classification procedure. We present an approach integrating sensor weighting in the classification framework. Sensor weights are considered as hyper-parameters to be learned by a support vector machine (SVM). The resulting sensor weighting SVM (sw-SVM) is designed to satisfy a margin criterion, that is, the generalization error. Experimental studies on two data sets are presented, a P300 data set and an error-related potential (ErrP) data set. For the P300 data set (BCI competition III), for which a large number of trials is available, the sw-SVM proves to perform equivalently with respect to the ensemble SVM strategy that won the competition. For the ErrP data set, for which a small number of trials are available, the sw-SVM shows superior performances as compared to three state-of-the art approaches. Results suggest that the sw-SVM promises to be useful in event-related potentials classification, even with a small number of training trials.

  6. Severity classification of repeated isoflurane anesthesia in C57BL/6JRj mice-Assessing the degree of distress.

    Directory of Open Access Journals (Sweden)

    Katharina Hohlbaum

    Full Text Available According to the EU Directive 2010/63, the severity of a procedure has to be classified as mild, moderate or severe. General anesthesia is thought to be mild, but the Directive does not differentiate between single and repeated anesthesia. Therefore, we investigated the impact of repeated administration of isoflurane, the most commonly used inhalation anesthetic, on the well-being of adult C57BL/6JRj mice, in comparison to single administrations and to untreated animals, when applied six times for 45 min at an interval of 3-4 days. For the animals anesthetized, excitations, phases of anesthesia, and vital parameters were monitored. Well-being after anesthesia was assessed using a behavioral test battery including luxury behavior like burrowing and nest building behavior, the Mouse Grimace Scale (MGS, the free exploratory paradigm for anxiety-related behavior, home cage activity and the rotarod test for activity, as well as food intake and body weight. Additionally, hair corticosterone and fecal corticosterone metabolites were measured. Our results show that nest building behavior, home cage activity, body weight, and corticosterone concentrations were not influenced by anesthesia, whereas changes in burrowing behavior, the MGS, food intake, and the free exploratory behavior indicated that the well-being of the mice was more affected by repeated than single isoflurane anesthesia. This effect depended on the sex of the animals, with female mice being more susceptible than male mice. However, repeated isoflurane anesthesia caused only short-term mild distress and impairment of well-being, mainly in the immediate postanesthetic period. Well-being stabilized at 8 days after the last anesthesia, at the latest. Therefore, we conclude that when using our anesthesia protocol, the severity of both single and repeated isoflurane anesthesia in C57BL/6JRj mice can be classified as mild. However, within the mild severity category, repeated isoflurane

  7. Pathohistological classification systems in gastric cancer: diagnostic relevance and prognostic value.

    Science.gov (United States)

    Berlth, Felix; Bollschweiler, Elfriede; Drebber, Uta; Hoelscher, Arnulf H; Moenig, Stefan

    2014-05-21

    Several pathohistological classification systems exist for the diagnosis of gastric cancer. Many studies have investigated the correlation between the pathohistological characteristics in gastric cancer and patient characteristics, disease specific criteria and overall outcome. It is still controversial as to which classification system imparts the most reliable information, and therefore, the choice of system may vary in clinical routine. In addition to the most common classification systems, such as the Laurén and the World Health Organization (WHO) classifications, other authors have tried to characterize and classify gastric cancer based on the microscopic morphology and in reference to the clinical outcome of the patients. In more than 50 years of systematic classification of the pathohistological characteristics of gastric cancer, there is no sole classification system that is consistently used worldwide in diagnostics and research. However, several national guidelines for the treatment of gastric cancer refer to the Laurén or the WHO classifications regarding therapeutic decision-making, which underlines the importance of a reliable classification system for gastric cancer. The latest results from gastric cancer studies indicate that it might be useful to integrate DNA- and RNA-based features of gastric cancer into the classification systems to establish prognostic relevance. This article reviews the diagnostic relevance and the prognostic value of different pathohistological classification systems in gastric cancer.

  8. Acoustic classification of dwellings

    DEFF Research Database (Denmark)

    Berardi, Umberto; Rasmussen, Birgit

    2014-01-01

    insulation performance, national schemes for sound classification of dwellings have been developed in several European countries. These schemes define acoustic classes according to different levels of sound insulation. Due to the lack of coordination among countries, a significant diversity in terms...... exchanging experiences about constructions fulfilling different classes, reducing trade barriers, and finally increasing the sound insulation of dwellings.......Schemes for the classification of dwellings according to different building performances have been proposed in the last years worldwide. The general idea behind these schemes relates to the positive impact a higher label, and thus a better performance, should have. In particular, focusing on sound...

  9. The role of diffusion-weighted magnetic resonance imaging in the classification of hepatic hydatid cysts

    Energy Technology Data Exchange (ETDEWEB)

    Çeçe, Hasan, E-mail: hasan_cece@yahoo.com [Harran University, Faculty of Medicine, Department of Radiology, 63300 Şanlıurfa (Turkey); Gündoğan, Mehmet, E-mail: drgundogan@hotmail.com [Harran University, Faculty of Medicine, Department of Radiology, 63300 Şanlıurfa (Turkey); Karakaş, Ömer, E-mail: dromerkarakas@hotmail.com [Harran University, Faculty of Medicine, Department of Radiology, 63300 Şanlıurfa (Turkey); Karakaş, Ekrem, E-mail: karakasekrem@yahoo.com [Harran University, Faculty of Medicine, Department of Radiology, 63300 Şanlıurfa (Turkey); Boyacı, Fatıma Nurefşan, E-mail: drnurefsan@yahoo.com [Harran University, Faculty of Medicine, Department of Radiology, 63300 Şanlıurfa (Turkey); Yıldız, Sema, E-mail: drsemayildiz@yahoo.com [Harran University, Faculty of Medicine, Department of Radiology, 63300 Şanlıurfa (Turkey); Özgönül, Abdullah, E-mail: drozgonul@yahoo.com.tr [Harran University, Faculty of Medicine, Department of General Surgery, Şanlıurfa (Turkey); Karakaş, Emel Yiğit, E-mail: e.ygtkarakas@yahoo.com.tr [Şanlıurfa Training and Research Hospital, Department of Internal Medicine, Şanlıurfa (Turkey); and others

    2013-01-15

    The aim of the study was to classify different types of hepatic hydatid cysts (HHCs) by measuring the mean apparent diffusion coefficient (ADC) using diffusion-weighted magnetic resonance imaging (DWI). This prospective study comprised 44 patients. The 44 HHCs were classified using Gharbi ultrasonographic classification (GUC) and then T2WIs and DWIs were obtained. The ADC values were measured of the hydatid cyst (HC) subtypes. The distribution of the ADC values in the cyst groups was compared using the Kruskal–Wallis test for multi groups and the Mann–Whitney U test for paired groups. To evaluate the efficacy of ADC values in cyst diagnosis, receiver operating characteristic (ROC) analysis was performed. According to the GUC, there were 15 type 1, 11 type 2, 7 type 3, 5 type 4 and 6 type 5 HHCs. According to the ADC values in the paired comparisons, while types 1, 2 and 5 HCs were statistically differentiated from all other groups except the type 3 group, the type 4 group was differentiated from all other groups and the type 3 group was only differentiated from the type 4 group. When two groups were formed from the HHC subtypes with types 1, 2, and 3 in one group and types 4 and 5 in the other, a statistically significant difference was determined in the mean ADC values of these new groups. In conclusion the measurement of ADC values can be considered a promising parameter as an alternative to ultrasonography in the determination of subtypes of HHCs.

  10. Agreement Between Actual and Perceived Body Weight in Adolescents and Their Weight Control Behaviors

    Directory of Open Access Journals (Sweden)

    Sun Mi Shin

    2017-06-01

    Full Text Available Background : To investigate the agreements between actual and perceived body weight status among adolescents and to identify the associations of disagreements with their weight control behaviors. Methods : This study used the secondary data of a sample survey (n=13,871 of the Seoul Student Health Examination among middle and high schools in 2010. Agreements between actual (underweight, normal, overweight, and obese, according to 2007 Korean National Growth Charts and perceived body weight status (underweight, normal, overweight, and obese were examined using Chi-square and Cohen’s kappa agreement, and then multinomial logistic regression including gender, grade, and attempt of weight control or method of weight control was done. Results : Agreements between actual and perceived body weight status were only 45.2%, and disagreements were up to 54.8%, including mild over- (20.4%, severe over- (1.8%, mild under- (29.5%, and severe under-estimation (3.1%. The kappa coefficient of agreement was only 0.19. The odds ratios on severe over-estimated perception were 1.59 (95% CI, 1.22-2.07 in female subjects, 1.78 (95% CI, 1.36-2.34 in diet control behaviors, and 1.53 (95% CI, 1.18-2.00 in exercise. The odds ratios on severe under-estimated perception were only 0.40 (95% CI, 0.32–0.50 in female subjects but 5.77 (95% CI, 3.68-9.06 in taking medication. Conclusion : There were associations of body weight control behaviors with disagreements of actual and perceived weight status. Therefore, further study is needed to identify the weight disagreement-related factors and to promote the desired weight control behaviors for adolescents.

  11. Weighted Discriminative Dictionary Learning based on Low-rank Representation

    International Nuclear Information System (INIS)

    Chang, Heyou; Zheng, Hao

    2017-01-01

    Low-rank representation has been widely used in the field of pattern classification, especially when both training and testing images are corrupted with large noise. Dictionary plays an important role in low-rank representation. With respect to the semantic dictionary, the optimal representation matrix should be block-diagonal. However, traditional low-rank representation based dictionary learning methods cannot effectively exploit the discriminative information between data and dictionary. To address this problem, this paper proposed weighted discriminative dictionary learning based on low-rank representation, where a weighted representation regularization term is constructed. The regularization associates label information of both training samples and dictionary atoms, and encourages to generate a discriminative representation with class-wise block-diagonal structure, which can further improve the classification performance where both training and testing images are corrupted with large noise. Experimental results demonstrate advantages of the proposed method over the state-of-the-art methods. (paper)

  12. Penalized feature selection and classification in bioinformatics

    OpenAIRE

    Ma, Shuangge; Huang, Jian

    2008-01-01

    In bioinformatics studies, supervised classification with high-dimensional input variables is frequently encountered. Examples routinely arise in genomic, epigenetic and proteomic studies. Feature selection can be employed along with classifier construction to avoid over-fitting, to generate more reliable classifier and to provide more insights into the underlying causal relationships. In this article, we provide a review of several recently developed penalized feature selection and classific...

  13. Correction Equations to Adjust Self-Reported Height and Weight for Obesity Estimates among College Students

    Science.gov (United States)

    Mozumdar, Arupendra; Liguori, Gary

    2011-01-01

    The purposes of this study were to generate correction equations for self-reported height and weight quartiles and to test the accuracy of the body mass index (BMI) classification based on corrected self-reported height and weight among 739 male and 434 female college students. The BMIqc (from height and weight quartile-specific, corrected…

  14. Handling Dynamic Weights in Weighted Frequent Pattern Mining

    Science.gov (United States)

    Ahmed, Chowdhury Farhan; Tanbeer, Syed Khairuzzaman; Jeong, Byeong-Soo; Lee, Young-Koo

    Even though weighted frequent pattern (WFP) mining is more effective than traditional frequent pattern mining because it can consider different semantic significances (weights) of items, existing WFP algorithms assume that each item has a fixed weight. But in real world scenarios, the weight (price or significance) of an item can vary with time. Reflecting these changes in item weight is necessary in several mining applications, such as retail market data analysis and web click stream analysis. In this paper, we introduce the concept of a dynamic weight for each item, and propose an algorithm, DWFPM (dynamic weighted frequent pattern mining), that makes use of this concept. Our algorithm can address situations where the weight (price or significance) of an item varies dynamically. It exploits a pattern growth mining technique to avoid the level-wise candidate set generation-and-test methodology. Furthermore, it requires only one database scan, so it is eligible for use in stream data mining. An extensive performance analysis shows that our algorithm is efficient and scalable for WFP mining using dynamic weights.

  15. Classifying Classifications

    DEFF Research Database (Denmark)

    Debus, Michael S.

    2017-01-01

    This paper critically analyzes seventeen game classifications. The classifications were chosen on the basis of diversity, ranging from pre-digital classification (e.g. Murray 1952), over game studies classifications (e.g. Elverdam & Aarseth 2007) to classifications of drinking games (e.g. LaBrie et...... al. 2013). The analysis aims at three goals: The classifications’ internal consistency, the abstraction of classification criteria and the identification of differences in classification across fields and/or time. Especially the abstraction of classification criteria can be used in future endeavors...... into the topic of game classifications....

  16. Obesity and the decision tree: predictors of sustained weight loss after bariatric surgery.

    Science.gov (United States)

    Lee, Yi-Chih; Lee, Wei-Jei; Lin, Yang-Chu; Liew, Phui-Ly; Lee, Chia Ko; Lin, Steven C H; Lee, Tian-Shyung

    2009-01-01

    Bariatric surgery is the only long-lasting effective treatment to reduce body weight in morbid obesity. Previous literature in using data mining techniques to predict weight loss in obese patients who have undergone bariatric surgery is limited. This study used initial evaluations before bariatric surgery and data mining techniques to predict weight outcomes in morbidly obese patients seeking surgical treatment. 251 morbidly obese patients undergoing laparoscopic mini-gastric bypass (LMGB) or adjustable gastric banding (LAGB) with complete clinical data at baseline and at two years were enrolled for analysis. Decision Tree, Logistic Regression and Discriminant analysis technologies were used to predict weight loss. Overall classification capability of the designed diagnostic models was evaluated by the misclassification costs. Two hundred fifty-one patients consisting of 68 men and 183 women was studied; with mean age 33 years. Mean +/- SD weight loss at 2 year was 74.5 +/- 16.4 kg. During two years of follow up, two-hundred and five (81.7%) patients had successful weight reduction while 46 (18.3%) were failed to reduce body weight. Operation methods, alanine transaminase (ALT), aspartate transaminase (AST), white blood cell counts (WBC), insulin and hemoglobin A1c (HbA1c) levels were the predictive factors for successful weight reduction. Decision tree model was a better classification models than traditional logistic regression and discriminant analysis in view of predictive accuracies.

  17. A graduated food addiction classification approach significantly differentiates obesity among people with type 2 diabetes.

    Science.gov (United States)

    Raymond, Karren-Lee; Kannis-Dymand, Lee; Lovell, Geoff P

    2016-10-01

    This study examined a graduated severity level approach to food addiction classification against associations with World Health Organization obesity classifications (body mass index, kg/m 2 ) among 408 people with type 2 diabetes. A survey including the Yale Food Addiction Scale and several demographic questions demonstrated four distinct Yale Food Addiction Scale symptom severity groups (in line with Diagnostic and Statistical Manual of Mental Disorders (5th ed.) severity indicators): non-food addiction, mild food addiction, moderate food addiction and severe food addiction. Analysis of variance with post hoc tests demonstrated each severity classification group was significantly different in body mass index, with each grouping being associated with increased World Health Organization obesity classifications. These findings have implications for diagnosing food addiction and implementing treatment and prevention methodologies of obesity among people with type 2 diabetes.

  18. Long-term effects of weight reduction on the severity of psoriasis in a cohort derived from a randomized trial

    DEFF Research Database (Denmark)

    Jensen, Peter; Christensen, Robin; Zachariae, Claus

    2016-01-01

    randomized phase with an LED for 8 wk and 8 wk of normal food intake combined with 2 LED products/d, followed by a 48-wk period of weight maintenance with the latter diet. After the randomization phase, the control group received the same 8 + 8-wk LED intervention, and all patients were then followed for 48...... who were allocated to a control group or a low-energy diet (LED) group. Here we followed the participants for an additional 48-wk period. In total, 56 patients with psoriasis [mean ± SD body mass index (in kg/m(2)): 34.4 ± 5.3] underwent a 64-wk weight-loss program consisting of an initial 16-wk...... wk while on the weight-loss maintenance diet. The main outcome was the Psoriasis Area and Severity Index (PASI), and secondary outcome was the Dermatology Life Quality Index (DLQI). RESULTS: For the present study, 56 patients were eligible, 38 agreed to participate, and 32 completed. After the 16-wk...

  19. Definition and classification of epilepsy. Classification of epileptic seizures 2016

    Directory of Open Access Journals (Sweden)

    K. Yu. Mukhin

    2017-01-01

    Full Text Available Epilepsy is one of the most common neurological diseases, especially in childhood and adolescence. The incidence varies from 15 to 113 cases per 100 000 population with the maximum among children under 1 year old. The prevalence of epilepsy is high, ranging from 5 to 8 cases (in some regions – 10 cases per 1000 children under 15 years old. Classification of the disease has great importance for diagnosis, treatment and prognosis. The article presents a novel strategy for classification of epileptic seizures, developed in 2016. It contains a number of brand new concepts, including a very important one, saying that some seizures, previously considered as generalized or focal only, can be, in fact, both focal and generalized. They include tonic, atonic, myoclonic seizures and epileptic spasms. The term “secondarily generalized seizure” is replace by the term “bilateral tonic-clonic seizure” (as soon as it is not a separate type of epileptic seizures, and the term reflects the spread of discharge from any area of cerebral cortex and evolution of any types of focal seizures. International League Against Epilepsy recommends to abandon the term “pseudo-epileptic seizures” and replace it by the term “psychogenic non-epileptic seizures”. If a doctor is not sure that seizures have epileptic nature, the term “paroxysmal event” should be used without specifying the disease. The conception of childhood epileptic encephalopathies, developed within this novel classification project, is one of the most significant achievements, since in this case not only the seizures, but even epileptiform activity can induce severe disorders of higher mental functions. In addition to detailed description of the new strategy for classification of epileptic seizures, the article contains a comprehensive review of the existing principles of epilepsy and epileptic seizures classification.

  20. A new classification for 'Pistol Grip Deformity'. Correlation between the severity of the deformity and the grade of osteoarthritis of the hip

    International Nuclear Information System (INIS)

    Ipach, Ingmar; Mittag, F.; Sachsenmaier, S.; Kluba, T.; Heinrich, P.

    2011-01-01

    Purpose: Two types of femoroacetabular impingement (FAI) are described as reasons for the early development of osteoarthritis of the hip. Cam impingement develops from contact between an abnormal head-neck junction and the acetabular rim. Pincer impingement is characterized by local or general overcoverage of the femoral head by the acetabular rim. Both forms might cause early osteoarthritis of the hip. A decreased head/neck offset has been recognized on AP pelvic views and labeled as 'pistol grip deformity'. The aim of the study was to develop a classification for this deformity with regard to the stage of osteoarthritis of the hip. Materials and Methods: 76 pelvic and axial views were analyzed for alpha angle and head ratio. 22 of them had a normal shape in the head-neck region and no osteoarthritis signs, 27 had a 'pistol grip deformity' and osteoarthritis I and 27 had a 'pistol grip deformity' and osteoarthritis II -IV . The CART method was used to develop a classification. Results: There was a statistically significant correlation between alpha angle and head ratio. A statistically significant difference in alpha angle and head ratio was seen between the three groups. Using the CART method, we developed a three-step classification system for the 'pistol grip deformity' with very high accuracy. This deformity was aggravated by increasing age. Conclusion: Using this model it is possible to differentiate between normal shapes of the head-neck junction and different severities of the pistol grip deformity. (orig.)

  1. Classification of Pulse Waveforms Using Edit Distance with Real Penalty

    Directory of Open Access Journals (Sweden)

    Zhang Dongyu

    2010-01-01

    Full Text Available Abstract Advances in sensor and signal processing techniques have provided effective tools for quantitative research in traditional Chinese pulse diagnosis (TCPD. Because of the inevitable intraclass variation of pulse patterns, the automatic classification of pulse waveforms has remained a difficult problem. In this paper, by referring to the edit distance with real penalty (ERP and the recent progress in -nearest neighbors (KNN classifiers, we propose two novel ERP-based KNN classifiers. Taking advantage of the metric property of ERP, we first develop an ERP-induced inner product and a Gaussian ERP kernel, then embed them into difference-weighted KNN classifiers, and finally develop two novel classifiers for pulse waveform classification. The experimental results show that the proposed classifiers are effective for accurate classification of pulse waveform.

  2. A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography.

    Science.gov (United States)

    Grassmann, Felix; Mengelkamp, Judith; Brandl, Caroline; Harsch, Sebastian; Zimmermann, Martina E; Linkohr, Birgit; Peters, Annette; Heid, Iris M; Palm, Christoph; Weber, Bernhard H F

    2018-04-10

    Age-related macular degeneration (AMD) is a common threat to vision. While classification of disease stages is critical to understanding disease risk and progression, several systems based on color fundus photographs are known. Most of these require in-depth and time-consuming analysis of fundus images. Herein, we present an automated computer-based classification algorithm. Algorithm development for AMD classification based on a large collection of color fundus images. Validation is performed on a cross-sectional, population-based study. We included 120 656 manually graded color fundus images from 3654 Age-Related Eye Disease Study (AREDS) participants. AREDS participants were >55 years of age, and non-AMD sight-threatening diseases were excluded at recruitment. In addition, performance of our algorithm was evaluated in 5555 fundus images from the population-based Kooperative Gesundheitsforschung in der Region Augsburg (KORA; Cooperative Health Research in the Region of Augsburg) study. We defined 13 classes (9 AREDS steps, 3 late AMD stages, and 1 for ungradable images) and trained several convolution deep learning architectures. An ensemble of network architectures improved prediction accuracy. An independent dataset was used to evaluate the performance of our algorithm in a population-based study. κ Statistics and accuracy to evaluate the concordance between predicted and expert human grader classification. A network ensemble of 6 different neural net architectures predicted the 13 classes in the AREDS test set with a quadratic weighted κ of 92% (95% confidence interval, 89%-92%) and an overall accuracy of 63.3%. In the independent KORA dataset, images wrongly classified as AMD were mainly the result of a macular reflex observed in young individuals. By restricting the KORA analysis to individuals >55 years of age and prior exclusion of other retinopathies, the weighted and unweighted κ increased to 50% and 63%, respectively. Importantly, the algorithm

  3. Cirse Quality Assurance Document and Standards for Classification of Complications: The Cirse Classification System.

    Science.gov (United States)

    Filippiadis, D K; Binkert, C; Pellerin, O; Hoffmann, R T; Krajina, A; Pereira, P L

    2017-08-01

    Interventional radiology provides a wide variety of vascular, nonvascular, musculoskeletal, and oncologic minimally invasive techniques aimed at therapy or palliation of a broad spectrum of pathologic conditions. Outcome data for these techniques are globally evaluated by hospitals, insurance companies, and government agencies targeting in a high-quality health care policy, including reimbursement strategies. To analyze effectively the outcome of a technique, accurate reporting of complications is necessary. Throughout the literature, numerous classification systems for complications grading and classification have been reported. Until now, there has been no method for uniform reporting of complications both in terms of definition and grading. The purpose of this CIRSE guideline is to provide a classification system of complications based on combining outcome and severity of sequelae. The ultimate challenge will be the adoption of this system by practitioners in different countries and health economies within the European Union and beyond.

  4. Word Embedding Perturbation for Sentence Classification

    OpenAIRE

    Zhang, Dongxu; Yang, Zhichao

    2018-01-01

    In this technique report, we aim to mitigate the overfitting problem of natural language by applying data augmentation methods. Specifically, we attempt several types of noise to perturb the input word embedding, such as Gaussian noise, Bernoulli noise, and adversarial noise, etc. We also apply several constraints on different types of noise. By implementing these proposed data augmentation methods, the baseline models can gain improvements on several sentence classification tasks.

  5. Baseline Gray- and White Matter Volume Predict Successful Weight Loss in the Elderly

    Science.gov (United States)

    Mokhtari, Fatemeh; Paolini, Brielle M.; Burdette, Jonathan H.; Marsh, Anthony P.; Rejeski, W. Jack; Laurienti, Paul J.

    2016-01-01

    Objective The purpose of this study is to investigate if structural brain phenotypes can be used to predict weight loss success following behavioral interventions in older adults that are overweight or obese and have cardiometabolic dysfunction. Methods A support vector machine (SVM) with a repeated random subsampling validation approach was used to classify participants into the upper and lower halves of the weight loss distribution following 18 months of a weight loss intervention. Predictions were based on baseline brain gray matter (GM) and white matter (WM) volume from 52 individuals that completed the intervention and a magnetic resonance imaging session. Results The SVM resulted in an average classification accuracy of 72.62 % based on GM and WM volume. A receiver operating characteristic analysis indicated that classification performance was robust based on an area under the curve of 0.82. Conclusions Our findings suggest that baseline brain structure is able to predict weight loss success following 18 months of treatment. The identification of brain structure as a predictor of successful weight loss is an innovative approach to identifying phenotypes for responsiveness to intensive lifestyle interventions. This phenotype could prove useful in future research focusing on the tailoring of treatment for weight loss. PMID:27804273

  6. Skew-signings of positive weighted digraphs

    Directory of Open Access Journals (Sweden)

    Kawtar Attas

    2018-07-01

    Full Text Available An arc-weighted digraph is a pair (D , ω where D is a digraph and ω is an arc-weight function that assigns to each arc u v of D a nonzero real number ω (u v . Given an arc-weighted digraph (D , ω with vertices v 1 , … , v n , the weighted adjacency matrix of (D , ω is defined as the n × n matrix A (D , ω = [ a i j ] where a i j = ω ( v i v j if v i v j is an arc of D , and 0 otherwise. Let (D , ω be a positive arc-weighted digraph and assume that D is loopless and symmetric. A skew-signing of (D , ω is an arc-weight function ω ′ such that ω ′ (u v = ± ω (u v and ω ′ (u v ω ′ (v u < 0 for every arc u v of D . In this paper, we give necessary and sufficient conditions under which the characteristic polynomial of A (D , ω ′ is the same for all skew-signings ω ′ of (D , ω . Our main theorem generalizes a result of Cavers et al. (2012 about skew-adjacency matrices of graphs. Keywords: Arc-weighted digraphs, Skew-signing of a digraph, Weighted adjacency matrix, Mathematics Subject Classification: 05C22, 05C31, 05C50

  7. Facial aging: A clinical classification

    Directory of Open Access Journals (Sweden)

    Shiffman Melvin

    2007-01-01

    Full Text Available The purpose of this classification of facial aging is to have a simple clinical method to determine the severity of the aging process in the face. This allows a quick estimate as to the types of procedures that the patient would need to have the best results. Procedures that are presently used for facial rejuvenation include laser, chemical peels, suture lifts, fillers, modified facelift and full facelift. The physician is already using his best judgment to determine which procedure would be best for any particular patient. This classification may help to refine these decisions.

  8. Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation

    Directory of Open Access Journals (Sweden)

    Rui Sun

    2016-08-01

    Full Text Available Vision-based pedestrian detection has become an active topic in computer vision and autonomous vehicles. It aims at detecting pedestrians appearing ahead of the vehicle using a camera so that autonomous vehicles can assess the danger and take action. Due to varied illumination and appearance, complex background and occlusion pedestrian detection in outdoor environments is a difficult problem. In this paper, we propose a novel hierarchical feature extraction and weighted kernel sparse representation model for pedestrian classification. Initially, hierarchical feature extraction based on a CENTRIST descriptor is used to capture discriminative structures. A max pooling operation is used to enhance the invariance of varying appearance. Then, a kernel sparse representation model is proposed to fully exploit the discrimination information embedded in the hierarchical local features, and a Gaussian weight function as the measure to effectively handle the occlusion in pedestrian images. Extensive experiments are conducted on benchmark databases, including INRIA, Daimler, an artificially generated dataset and a real occluded dataset, demonstrating the more robust performance of the proposed method compared to state-of-the-art pedestrian classification methods.

  9. Boundedness of positive operators on weighted amalgams

    Directory of Open Access Journals (Sweden)

    Aguilar Cañestro María Isabel

    2011-01-01

    Full Text Available Abstract In this article, we characterize the pairs (u, v of positive measurable functions such that T maps the weighted amalgam in (Lp (u, ℓ q for all , where T belongs to a class of positive operators which includes Hardy operators, maximal operators, and fractional integrals. 2000 Mathematics Subject Classification 26D10, 26D15 (42B35

  10. Data Augmentation for Plant Classification

    NARCIS (Netherlands)

    Pawara, Pornntiwa; Okafor, Emmanuel; Schomaker, Lambertus; Wiering, Marco

    2017-01-01

    Data augmentation plays a crucial role in increasing the number of training images, which often aids to improve classification performances of deep learning techniques for computer vision problems. In this paper, we employ the deep learning framework and determine the effects of several

  11. International Standards for Neurological Classification of Spinal Cord Injury

    DEFF Research Database (Denmark)

    Kirshblum, S C; Biering-Sorensen, F; Betz, R

    2014-01-01

    The International Standards for the Neurological Classification of Spinal Cord Injury (ISNCSCI) is routinely used to determine the levels of injury and to classify the severity of the injury. Questions are often posed to the International Standards Committee of the American Spinal Injury Associat......The International Standards for the Neurological Classification of Spinal Cord Injury (ISNCSCI) is routinely used to determine the levels of injury and to classify the severity of the injury. Questions are often posed to the International Standards Committee of the American Spinal Injury...

  12. How recalibration method, pricing, and coding affect DRG weights

    Science.gov (United States)

    Carter, Grace M.; Rogowski, Jeannette A.

    1992-01-01

    We compared diagnosis-related group (DRG) weights calculated using the hospital-specific relative-value (HSR V) methodology with those calculated using the standard methodology for each year from 1985 through 1989 and analyzed differences between the two methods in detail for 1989. We provide evidence suggesting that classification error and subsidies of higher weighted cases by lower weighted cases caused compression in the weights used for payment as late as the fifth year of the prospective payment system. However, later weights calculated by the standard method are not compressed because a statistical correlation between high markups and high case-mix indexes offsets the cross-subsidization. HSR V weights from the same files are compressed because this methodology is more sensitive to cross-subsidies. However, both sets of weights produce equally good estimates of hospital-level costs net of those expenses that are paid by outlier payments. The greater compression of the HSR V weights is counterbalanced by the fact that more high-weight cases qualify as outliers. PMID:10127456

  13. Validation of ICDPIC software injury severity scores using a large regional trauma registry.

    Science.gov (United States)

    Greene, Nathaniel H; Kernic, Mary A; Vavilala, Monica S; Rivara, Frederick P

    2015-10-01

    Administrative or quality improvement registries may or may not contain the elements needed for investigations by trauma researchers. International Classification of Diseases Program for Injury Categorisation (ICDPIC), a statistical program available through Stata, is a powerful tool that can extract injury severity scores from ICD-9-CM codes. We conducted a validation study for use of the ICDPIC in trauma research. We conducted a retrospective cohort validation study of 40,418 patients with injury using a large regional trauma registry. ICDPIC-generated AIS scores for each body region were compared with trauma registry AIS scores (gold standard) in adult and paediatric populations. A separate analysis was conducted among patients with traumatic brain injury (TBI) comparing the ICDPIC tool with ICD-9-CM embedded severity codes. Performance in characterising overall injury severity, by the ISS, was also assessed. The ICDPIC tool generated substantial correlations in thoracic and abdominal trauma (weighted κ 0.87-0.92), and in head and neck trauma (weighted κ 0.76-0.83). The ICDPIC tool captured TBI severity better than ICD-9-CM code embedded severity and offered the advantage of generating a severity value for every patient (rather than having missing data). Its ability to produce an accurate severity score was consistent within each body region as well as overall. The ICDPIC tool performs well in classifying injury severity and is superior to ICD-9-CM embedded severity for TBI. Use of ICDPIC demonstrates substantial efficiency and may be a preferred tool in determining injury severity for large trauma datasets, provided researchers understand its limitations and take caution when examining smaller trauma datasets. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  14. Considering the Spatial Layout Information of Bag of Features (BoF) Framework for Image Classification.

    Science.gov (United States)

    Mu, Guangyu; Liu, Ying; Wang, Limin

    2015-01-01

    The spatial pooling method such as spatial pyramid matching (SPM) is very crucial in the bag of features model used in image classification. SPM partitions the image into a set of regular grids and assumes that the spatial layout of all visual words obey the uniform distribution over these regular grids. However, in practice, we consider that different visual words should obey different spatial layout distributions. To improve SPM, we develop a novel spatial pooling method, namely spatial distribution pooling (SDP). The proposed SDP method uses an extension model of Gauss mixture model to estimate the spatial layout distributions of the visual vocabulary. For each visual word type, SDP can generate a set of flexible grids rather than the regular grids from the traditional SPM. Furthermore, we can compute the grid weights for visual word tokens according to their spatial coordinates. The experimental results demonstrate that SDP outperforms the traditional spatial pooling methods, and is competitive with the state-of-the-art classification accuracy on several challenging image datasets.

  15. Detection and classification of interstitial lung diseases and emphysema using a joint morphological-fuzzy approach

    Science.gov (United States)

    Chang Chien, Kuang-Che; Fetita, Catalin; Brillet, Pierre-Yves; Prêteux, Françoise; Chang, Ruey-Feng

    2009-02-01

    Multi-detector computed tomography (MDCT) has high accuracy and specificity on volumetrically capturing serial images of the lung. It increases the capability of computerized classification for lung tissue in medical research. This paper proposes a three-dimensional (3D) automated approach based on mathematical morphology and fuzzy logic for quantifying and classifying interstitial lung diseases (ILDs) and emphysema. The proposed methodology is composed of several stages: (1) an image multi-resolution decomposition scheme based on a 3D morphological filter is used to detect and analyze the different density patterns of the lung texture. Then, (2) for each pattern in the multi-resolution decomposition, six features are computed, for which fuzzy membership functions define a probability of association with a pathology class. Finally, (3) for each pathology class, the probabilities are combined up according to the weight assigned to each membership function and two threshold values are used to decide the final class of the pattern. The proposed approach was tested on 10 MDCT cases and the classification accuracy was: emphysema: 95%, fibrosis/honeycombing: 84% and ground glass: 97%.

  16. Non-Hodgkin lymphoma response evaluation with MRI texture classification

    Directory of Open Access Journals (Sweden)

    Heinonen Tomi T

    2009-06-01

    Full Text Available Abstract Background To show magnetic resonance imaging (MRI texture appearance change in non-Hodgkin lymphoma (NHL during treatment with response controlled by quantitative volume analysis. Methods A total of 19 patients having NHL with an evaluable lymphoma lesion were scanned at three imaging timepoints with 1.5T device during clinical treatment evaluation. Texture characteristics of images were analyzed and classified with MaZda application and statistical tests. Results NHL tissue MRI texture imaged before treatment and under chemotherapy was classified within several subgroups, showing best discrimination with 96% correct classification in non-linear discriminant analysis of T2-weighted images. Texture parameters of MRI data were successfully tested with statistical tests to assess the impact of the separability of the parameters in evaluating chemotherapy response in lymphoma tissue. Conclusion Texture characteristics of MRI data were classified successfully; this proved texture analysis to be potential quantitative means of representing lymphoma tissue changes during chemotherapy response monitoring.

  17. Heuristic approach to the classification of postpartum endometritis and its forms

    Directory of Open Access Journals (Sweden)

    E. A. Balashova

    2017-01-01

    Full Text Available Тhe work is dedicated to the development of a method of automated medical diagnosis based on the description of biomedical systems using two parameters: energy, reflecting the interaction of its elements, and entropy characterizing the organization of the system. The violations of the energy-entropy cycle of biomedical systems is reflected in the symptoms of the disease. Statistical link between the symptoms of the condition of the body and the nature of excitation of its elements best expressed in the heuristic description of the system state. High accuracy classification of the patient's condition is achieved by using heuristic detection methods. In the proposed approach, allowing to estimate the probability of correct diagnosis increases the accuracy of the classification, and the estimated minimum amount of training samples and the capacity of its constituent signs. Classification technique consists in averaging the characteristic values in the selected classes, the preparation of the complex of symptoms of the most important signs of the disease, to conduct a "rough" diagnostic threshold rules that allow to distinguish severe forms of the disease, then differential diagnosis the severity of the disease. The proposed method was tested for classification of the forms of puerperal endometritis (mild, moderate, severe. The training sample contained 70 case histories. Syndrome to classify the patient's condition was composed of 17 characteristics. Threshold diagnosis has allowed to establish the presence of disease and to separate heavy. Differential diagnosis was used for classification of mild and moderate severity of postpartum endometritis. The accuracy of the classification of forms of postpartum endometritis amounted to 97.1%.

  18. Weighted Polynomial Approximation for Automated Detection of Inspiratory Flow Limitation

    Directory of Open Access Journals (Sweden)

    Sheng-Cheng Huang

    2017-01-01

    Full Text Available Inspiratory flow limitation (IFL is a critical symptom of sleep breathing disorders. A characteristic flattened flow-time curve indicates the presence of highest resistance flow limitation. This study involved investigating a real-time algorithm for detecting IFL during sleep. Three categories of inspiratory flow shape were collected from previous studies for use as a development set. Of these, 16 cases were labeled as non-IFL and 78 as IFL which were further categorized into minor level (20 cases and severe level (58 cases of obstruction. In this study, algorithms using polynomial functions were proposed for extracting the features of IFL. Methods using first- to third-order polynomial approximations were applied to calculate the fitting curve to obtain the mean absolute error. The proposed algorithm is described by the weighted third-order (w.3rd-order polynomial function. For validation, a total of 1,093 inspiratory breaths were acquired as a test set. The accuracy levels of the classifications produced by the presented feature detection methods were analyzed, and the performance levels were compared using a misclassification cobweb. According to the results, the algorithm using the w.3rd-order polynomial approximation achieved an accuracy of 94.14% for IFL classification. We concluded that this algorithm achieved effective automatic IFL detection during sleep.

  19. Analysis of composition-based metagenomic classification.

    Science.gov (United States)

    Higashi, Susan; Barreto, André da Motta Salles; Cantão, Maurício Egidio; de Vasconcelos, Ana Tereza Ribeiro

    2012-01-01

    An essential step of a metagenomic study is the taxonomic classification, that is, the identification of the taxonomic lineage of the organisms in a given sample. The taxonomic classification process involves a series of decisions. Currently, in the context of metagenomics, such decisions are usually based on empirical studies that consider one specific type of classifier. In this study we propose a general framework for analyzing the impact that several decisions can have on the classification problem. Instead of focusing on any specific classifier, we define a generic score function that provides a measure of the difficulty of the classification task. Using this framework, we analyze the impact of the following parameters on the taxonomic classification problem: (i) the length of n-mers used to encode the metagenomic sequences, (ii) the similarity measure used to compare sequences, and (iii) the type of taxonomic classification, which can be conventional or hierarchical, depending on whether the classification process occurs in a single shot or in several steps according to the taxonomic tree. We defined a score function that measures the degree of separability of the taxonomic classes under a given configuration induced by the parameters above. We conducted an extensive computational experiment and found out that reasonable values for the parameters of interest could be (i) intermediate values of n, the length of the n-mers; (ii) any similarity measure, because all of them resulted in similar scores; and (iii) the hierarchical strategy, which performed better in all of the cases. As expected, short n-mers generate lower configuration scores because they give rise to frequency vectors that represent distinct sequences in a similar way. On the other hand, large values for n result in sparse frequency vectors that represent differently metagenomic fragments that are in fact similar, also leading to low configuration scores. Regarding the similarity measure, in

  20. [The impact of weight management and related diuretic medication intervention based on body weight changes on cardiac function and re-hospitalization rate in patients with chronic congestive heart failure].

    Science.gov (United States)

    Wang, F W; Shi, J; Shi, J; Yang, J W; Wang, Z H; Ye, J H; Ye, Y; Zheng, H Q; Huang, J

    2017-10-24

    Objective: To explore the impact of weight management and related medication intervention based on body weight changes on cardiac function among patients with chronic congestive heart failure (CHF). Methods: Using prospective, randomized, controlled study methods, consecutive CHF patients, who hospitalized in our department from June 2014 to June 2016 ( n =350), were randomly divided into intervention group ( n =175) and control group ( n =175). Patients in the intervention group received weight management guidance and the post discharge diuretic drugs regimen was adjusted based on body weight changes. The control group received routine medical care post discharge. Left ventricular ejection fraction (LVEF), B type natriuretic peptide precursor (NT-proBNP), 6 minutes walk distance and NYHA classification at one day before discharge and after 6 months were compared between the two groups respectively. Results: Follow-up visit data were not available from 6 patients in the control and intervention group respectively. NYHA classification, LVEF, NT-proBNP and 6 minutes walk distance were similar between the two groups at one day before discharge (all P >0.05). After 6 months, the LVEF and 6 minutes walk distance were significantly higher while NT-proBNP level was significantly lower in the intervention group compared to the control group (all P weight remained unchanged in the intervention group, while body weight tended to be higher in the control group compared to one day before discharge. Conclusion: The weight management and diuretic drug regimen adjudgment intervention based on body weight changes can improve cardiac function and reduced re-hospitalization rate in CHF patients.

  1. Turkish Music Genre Classification using Audio and Lyrics Features

    Directory of Open Access Journals (Sweden)

    Önder ÇOBAN

    2017-05-01

    Full Text Available Music Information Retrieval (MIR has become a popular research area in recent years. In this context, researchers have developed music information systems to find solutions for such major problems as automatic playlist creation, hit song detection, and music genre or mood classification. Meta-data information, lyrics, or melodic content of music are used as feature resource in previous works. However, lyrics do not often used in MIR systems and the number of works in this field is not enough especially for Turkish. In this paper, firstly, we have extended our previously created Turkish MIR (TMIR dataset, which comprises of Turkish lyrics, by including the audio file of each song. Secondly, we have investigated the effect of using audio and textual features together or separately on automatic Music Genre Classification (MGC. We have extracted textual features from lyrics using different feature extraction models such as word2vec and traditional Bag of Words. We have conducted our experiments on Support Vector Machine (SVM algorithm and analysed the impact of feature selection and different feature groups on MGC. We have considered lyrics based MGC as a text classification task and also investigated the effect of term weighting method. Experimental results show that textual features can also be effective as well as audio features for Turkish MGC, especially when a supervised term weighting method is employed. We have achieved the highest success rate as 99,12\\% by using both audio and textual features together.

  2. Combining multiple classifiers for age classification

    CSIR Research Space (South Africa)

    Van Heerden, C

    2009-11-01

    Full Text Available The authors compare several different classifier combination methods on a single task, namely speaker age classification. This task is well suited to combination strategies, since significantly different feature classes are employed. Support vector...

  3. Neurodevelopmental outcomes of infants with very low birth weights are associated with the severity of their extra-uterine growth retardation

    Directory of Open Access Journals (Sweden)

    Han-Chun Chien

    2018-04-01

    Full Text Available Background: For infants with very low birth weights (VLBW, their neurodevelopmental outcomes are thought to be associated with extra-uterine growth retardation (EUGR. In this study, based on a single institute, we analyzed the association between different levels or severity of EUGR of VLBW infants and their neurodevelopmental outcomes later at a corrected age of 24 months. Methods: This is a hospital-based retrospective cohort study. The severity of EUGR was classified into three categories according to the z-score of discharge weight: z < −2.0, <−2.5, and <−3.0. The outcomes were assessed using the Bayley Scales of Infant Development-II (BSID-II at a corrected age of 24 months. We then estimated the association of EUGR with low mental developmental index (MDI or low psychomotor developmental index (PDI. Multiple logistic regression and stratified analyses were used to adjust for the possible confounding factors. Results: In total, 224 VLBW infants were enrolled in this study from 1997 to 2006. In the univariate analysis, EUGR for weight at discharge from hospital was associated with MDI <85 at the corrected age of 24 months, and this association was related to the severity of EUGR (z < −2.5, OR: 1.92 (1.04–3.53; z < −3.0, OR: 2.83 (1.26–6.36. In addition, the relationship was not confounded by gender nor small for gestational age. The stratified analysis against hemodynamic significant patent ductus arteriosus also revealed that EUGR was an independent predictor for neurodevelopmental outcomes. Conclusion: In VLBW preterm infants, EUGR was significantly associated with low MDI scores assessed at a corrected age of 24 months. Early evaluation and recognition of EUGR should be emphasized when caring for preterm infants. Key Words: EUGR, VLBW, neurodevelopment

  4. Dewey Decimal Classification for U. S. Conn: An Advantage?

    Science.gov (United States)

    Marek, Kate

    This paper examines the use of the Dewey Decimal Classification (DDC) system at the U. S. Conn Library at Wayne State College (WSC) in Nebraska. Several developments in the last 20 years which have eliminated the trend toward reclassification of academic library collections from DDC to the Library of Congress (LC) classification scheme are…

  5. A Semisupervised Cascade Classification Algorithm

    Directory of Open Access Journals (Sweden)

    Stamatis Karlos

    2016-01-01

    Full Text Available Classification is one of the most important tasks of data mining techniques, which have been adopted by several modern applications. The shortage of enough labeled data in the majority of these applications has shifted the interest towards using semisupervised methods. Under such schemes, the use of collected unlabeled data combined with a clearly smaller set of labeled examples leads to similar or even better classification accuracy against supervised algorithms, which use labeled examples exclusively during the training phase. A novel approach for increasing semisupervised classification using Cascade Classifier technique is presented in this paper. The main characteristic of Cascade Classifier strategy is the use of a base classifier for increasing the feature space by adding either the predicted class or the probability class distribution of the initial data. The classifier of the second level is supplied with the new dataset and extracts the decision for each instance. In this work, a self-trained NB∇C4.5 classifier algorithm is presented, which combines the characteristics of Naive Bayes as a base classifier and the speed of C4.5 for final classification. We performed an in-depth comparison with other well-known semisupervised classification methods on standard benchmark datasets and we finally reached to the point that the presented technique has better accuracy in most cases.

  6. NEURAL NETWORKS AS A CLASSIFICATION TOOL BIOTECHNOLOGICAL SYSTEMS (FOR EXAMPLE FLOUR PRODUCTION

    Directory of Open Access Journals (Sweden)

    V. K. Bitykov

    2015-01-01

    Full Text Available Summary. To date, artificial intelligence systems are the most common type to classify objects of different quality. The proposed modeling technology to predict the quality of flour products by using artificial neural networks allows to solve problems of analysis of the factors determining the quality of the products. Interest in artificial neural networks has grown due to the fact that they can change their behavior depending on external environment. This factor more than any other responsible for the interest that they cause. After the presentation of input signals (possibly together with the desired outputs, they self-configurable to provide the desired reaction. We developed a set of training algorithms, each with their own strengths and weaknesses. The solution to the problem of classification is one of the most important applications of neural networks, which represents a problem of attributing the sample to one of several non-intersecting sets. To solve this problem developed algorithms for synthesis of NA with the use of nonlinear activation functions, the algorithms for training the network. Training the NS involves determining the weights of layers of neurons. Training the NA occurs with the teacher, that is, the network must meet the values of both input and desired output signals, and it is according to some internal algorithm adjusts the weights of their synaptic connections. The work was built an artificial neural network, multilayer perceptron example. With the help of correlation analysis in total sample revealed that the traits are correlated at the significance level of 0.01 with grade quality bread. The classification accuracy exceeds 90%.

  7. Correlation of endoscopic severity of gastroesophageal reflux disease (gerd) with body mass index (bmi)

    International Nuclear Information System (INIS)

    Zafar, S.; Haq, I.U.; Butt, A.R.; Shafiq, F.; Huda, G.; Mirza, G.; Rehman, A.U.

    2007-01-01

    To assess the correlation of endoscopic severity of Gastroesophageal Reflux Disease (GERD) with Body Mass Index (BMI). This study was conducted on 203 patients, who presented with upper GI symptoms. Patients who fulfilled the symptom criteria were referred for endoscopy. Classification of GERD was done according to LA Grading classification system. Body mass index (BMI) was calculated as Body Weight (BW) in kilograms (kg) divided by the square of the body height (BH) in meter (m2). Patient data was analyzed using SPSS 12 software. Statistical evaluation was done using non-parametric Wilcoxon's-sign Rank test. P-value <0.05 was considered to be statistically significant. Distribution of GERD was as follows: GERD-A subjects 65 (32%), GERD B subjects 72 (35.4%), GERD-C subjects 23 (11.3%), GERD-D subjects 10 (4.92%), while Non-Erosive Reflux Disease (NERD) was present in 33 subjects (16.2%). Mean BMI was 27+5.02SD (range of 18.2-38.3). BMI of patients having NERD was in normal range but patients who were having advanced disease i.e. Grade C-D were in obese range of BMI, while those who were having LA grade A-B were in overweight BMI range. When regrouped as mild GERD (grade A-B) and NERD versus severe GERD (grade C-D), there was a strong significant correlation between severity of GERD and BMI, as detected by Wilcoxon's signed Rank test (p=0.001). Higher BMI seems to be associated with higher degree of endoscopic GERD severity. (author)

  8. Graph-Based Semi-Supervised Hyperspectral Image Classification Using Spatial Information

    Science.gov (United States)

    Jamshidpour, N.; Homayouni, S.; Safari, A.

    2017-09-01

    Hyperspectral image classification has been one of the most popular research areas in the remote sensing community in the past decades. However, there are still some problems that need specific attentions. For example, the lack of enough labeled samples and the high dimensionality problem are two most important issues which degrade the performance of supervised classification dramatically. The main idea of semi-supervised learning is to overcome these issues by the contribution of unlabeled samples, which are available in an enormous amount. In this paper, we propose a graph-based semi-supervised classification method, which uses both spectral and spatial information for hyperspectral image classification. More specifically, two graphs were designed and constructed in order to exploit the relationship among pixels in spectral and spatial spaces respectively. Then, the Laplacians of both graphs were merged to form a weighted joint graph. The experiments were carried out on two different benchmark hyperspectral data sets. The proposed method performed significantly better than the well-known supervised classification methods, such as SVM. The assessments consisted of both accuracy and homogeneity analyses of the produced classification maps. The proposed spectral-spatial SSL method considerably increased the classification accuracy when the labeled training data set is too scarce.When there were only five labeled samples for each class, the performance improved 5.92% and 10.76% compared to spatial graph-based SSL, for AVIRIS Indian Pine and Pavia University data sets respectively.

  9. GRAPH-BASED SEMI-SUPERVISED HYPERSPECTRAL IMAGE CLASSIFICATION USING SPATIAL INFORMATION

    Directory of Open Access Journals (Sweden)

    N. Jamshidpour

    2017-09-01

    Full Text Available Hyperspectral image classification has been one of the most popular research areas in the remote sensing community in the past decades. However, there are still some problems that need specific attentions. For example, the lack of enough labeled samples and the high dimensionality problem are two most important issues which degrade the performance of supervised classification dramatically. The main idea of semi-supervised learning is to overcome these issues by the contribution of unlabeled samples, which are available in an enormous amount. In this paper, we propose a graph-based semi-supervised classification method, which uses both spectral and spatial information for hyperspectral image classification. More specifically, two graphs were designed and constructed in order to exploit the relationship among pixels in spectral and spatial spaces respectively. Then, the Laplacians of both graphs were merged to form a weighted joint graph. The experiments were carried out on two different benchmark hyperspectral data sets. The proposed method performed significantly better than the well-known supervised classification methods, such as SVM. The assessments consisted of both accuracy and homogeneity analyses of the produced classification maps. The proposed spectral-spatial SSL method considerably increased the classification accuracy when the labeled training data set is too scarce.When there were only five labeled samples for each class, the performance improved 5.92% and 10.76% compared to spatial graph-based SSL, for AVIRIS Indian Pine and Pavia University data sets respectively.

  10. Using multiclass classification to automate the identification of patient safety incident reports by type and severity.

    Science.gov (United States)

    Wang, Ying; Coiera, Enrico; Runciman, William; Magrabi, Farah

    2017-06-12

    Approximately 10% of admissions to acute-care hospitals are associated with an adverse event. Analysis of incident reports helps to understand how and why incidents occur and can inform policy and practice for safer care. Unfortunately our capacity to monitor and respond to incident reports in a timely manner is limited by the sheer volumes of data collected. In this study, we aim to evaluate the feasibility of using multiclass classification to automate the identification of patient safety incidents in hospitals. Text based classifiers were applied to identify 10 incident types and 4 severity levels. Using the one-versus-one (OvsO) and one-versus-all (OvsA) ensemble strategies, we evaluated regularized logistic regression, linear support vector machine (SVM) and SVM with a radial-basis function (RBF) kernel. Classifiers were trained and tested with "balanced" datasets (n_ Type  = 2860, n_ SeverityLevel  = 1160) from a state-wide incident reporting system. Testing was also undertaken with imbalanced "stratified" datasets (n_ Type  = 6000, n_ SeverityLevel =5950) from the state-wide system and an independent hospital reporting system. Classifier performance was evaluated using a confusion matrix, as well as F-score, precision and recall. The most effective combination was a OvsO ensemble of binary SVM RBF classifiers with binary count feature extraction. For incident type, classifiers performed well on balanced and stratified datasets (F-score: 78.3, 73.9%), but were worse on independent datasets (68.5%). Reports about falls, medications, pressure injury, aggression and blood products were identified with high recall and precision. "Documentation" was the hardest type to identify. For severity level, F-score for severity assessment code (SAC) 1 (extreme risk) was 87.3 and 64% for SAC4 (low risk) on balanced data. With stratified data, high recall was achieved for SAC1 (82.8-84%) but precision was poor (6.8-11.2%). High risk incidents (SAC2) were confused

  11. Impact of weight loss achieved through a multidisciplinary intervention on appetite in patients with severe obesity

    DEFF Research Database (Denmark)

    Coutinho, Silvia R; Rehfeld, Jens F; Holst, Jens J

    2018-01-01

    The impact of lifestyle-induced weight loss (WL) on appetite in patients with obesity remains controversial. This study aimed was to assess the short- and long-term impact of WL achieved by diet and exercise, on appetite in patients with obesity. Thirty-five (22 females) adults with severe obesity......), in the fasting and postprandial states, were measured at baseline (B), week 4 (W4), 1 and 2-years (and average values for all fasting and postprandial time points computed). BW was significantly reduced and VO2max (ml/kg/min) increased at all time points compared with B (3.5, 8.1 and 8.4 % WL and 7, 11 and 8...... compared with B. Average GLP-1 was reduced at W4 and CCK increased at 2y. After lifestyle-induced WL, patients with severe obesity will, therefore, have to deal with increased hunger in the long-term. In conclusion, sustained WL at 2y achieved with diet and exercise is associated with increased hunger...

  12. Intelligent Computer Vision System for Automated Classification

    International Nuclear Information System (INIS)

    Jordanov, Ivan; Georgieva, Antoniya

    2010-01-01

    In this paper we investigate an Intelligent Computer Vision System applied for recognition and classification of commercially available cork tiles. The system is capable of acquiring and processing gray images using several feature generation and analysis techniques. Its functionality includes image acquisition, feature extraction and preprocessing, and feature classification with neural networks (NN). We also discuss system test and validation results from the recognition and classification tasks. The system investigation also includes statistical feature processing (features number and dimensionality reduction techniques) and classifier design (NN architecture, target coding, learning complexity and performance, and training with our own metaheuristic optimization method). The NNs trained with our genetic low-discrepancy search method (GLPτS) for global optimisation demonstrated very good generalisation abilities. In our view, the reported testing success rate of up to 95% is due to several factors: combination of feature generation techniques; application of Analysis of Variance (ANOVA) and Principal Component Analysis (PCA), which appeared to be very efficient for preprocessing the data; and use of suitable NN design and learning method.

  13. Severe obesity and diabetes self-care attitudes, behaviours and burden : Implications for weight management from a matched case-controlled study. Results from Diabetes MILES-Australia

    NARCIS (Netherlands)

    Dixon, J.B.; Browne, J.L.; Mosely, K.G.; Jones, K.M.; Pouwer, F.; Speight, J.

    2014-01-01

    Aims To investigate whether diabetes self-care attitudes, behaviours and perceived burden, particularly related to weight management, diet and physical activity, differ between adults with Type 2 diabetes who are severely obese and matched non-severely obese control subjects. Methods The 1795

  14. An Improved Brain-Inspired Emotional Learning Algorithm for Fast Classification

    Directory of Open Access Journals (Sweden)

    Ying Mei

    2017-06-01

    Full Text Available Classification is an important task of machine intelligence in the field of information. The artificial neural network (ANN is widely used for classification. However, the traditional ANN shows slow training speed, and it is hard to meet the real-time requirement for large-scale applications. In this paper, an improved brain-inspired emotional learning (BEL algorithm is proposed for fast classification. The BEL algorithm was put forward to mimic the high speed of the emotional learning mechanism in mammalian brain, which has the superior features of fast learning and low computational complexity. To improve the accuracy of BEL in classification, the genetic algorithm (GA is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in the BEL neural network. The combinational algorithm named as GA-BEL has been tested on eight University of California at Irvine (UCI datasets and two well-known databases (Japanese Female Facial Expression, Cohn–Kanade. The comparisons of experiments indicate that the proposed GA-BEL is more accurate than the original BEL algorithm, and it is much faster than the traditional algorithm.

  15. Convolutional neural network with transfer learning for rice type classification

    Science.gov (United States)

    Patel, Vaibhav Amit; Joshi, Manjunath V.

    2018-04-01

    Presently, rice type is identified manually by humans, which is time consuming and error prone. Therefore, there is a need to do this by machine which makes it faster with greater accuracy. This paper proposes a deep learning based method for classification of rice types. We propose two methods to classify the rice types. In the first method, we train a deep convolutional neural network (CNN) using the given segmented rice images. In the second method, we train a combination of a pretrained VGG16 network and the proposed method, while using transfer learning in which the weights of a pretrained network are used to achieve better accuracy. Our approach can also be used for classification of rice grain as broken or fine. We train a 5-class model for classifying rice types using 4000 training images and another 2- class model for the classification of broken and normal rice using 1600 training images. We observe that despite having distinct rice images, our architecture, pretrained on ImageNet data boosts classification accuracy significantly.

  16. Weighted approximation with varying weight

    CERN Document Server

    Totik, Vilmos

    1994-01-01

    A new construction is given for approximating a logarithmic potential by a discrete one. This yields a new approach to approximation with weighted polynomials of the form w"n"(" "= uppercase)P"n"(" "= uppercase). The new technique settles several open problems, and it leads to a simple proof for the strong asymptotics on some L p(uppercase) extremal problems on the real line with exponential weights, which, for the case p=2, are equivalent to power- type asymptotics for the leading coefficients of the corresponding orthogonal polynomials. The method is also modified toyield (in a sense) uniformly good approximation on the whole support. This allows one to deduce strong asymptotics in some L p(uppercase) extremal problems with varying weights. Applications are given, relating to fast decreasing polynomials, asymptotic behavior of orthogonal polynomials and multipoint Pade approximation. The approach is potential-theoretic, but the text is self-contained.

  17. Ensemble Classification of Data Streams Based on Attribute Reduction and a Sliding Window

    Directory of Open Access Journals (Sweden)

    Yingchun Chen

    2018-04-01

    Full Text Available With the current increasing volume and dimensionality of data, traditional data classification algorithms are unable to satisfy the demands of practical classification applications of data streams. To deal with noise and concept drift in data streams, we propose an ensemble classification algorithm based on attribute reduction and a sliding window in this paper. Using mutual information, an approximate attribute reduction algorithm based on rough sets is used to reduce data dimensionality and increase the diversity of reduced results in the algorithm. A double-threshold concept drift detection method and a three-stage sliding window control strategy are introduced to improve the performance of the algorithm when dealing with both noise and concept drift. The classification precision is further improved by updating the base classifiers and their nonlinear weights. Experiments on synthetic datasets and actual datasets demonstrate the performance of the algorithm in terms of classification precision, memory use, and time efficiency.

  18. Voice based gender classification using machine learning

    Science.gov (United States)

    Raahul, A.; Sapthagiri, R.; Pankaj, K.; Vijayarajan, V.

    2017-11-01

    Gender identification is one of the major problem speech analysis today. Tracing the gender from acoustic data i.e., pitch, median, frequency etc. Machine learning gives promising results for classification problem in all the research domains. There are several performance metrics to evaluate algorithms of an area. Our Comparative model algorithm for evaluating 5 different machine learning algorithms based on eight different metrics in gender classification from acoustic data. Agenda is to identify gender, with five different algorithms: Linear Discriminant Analysis (LDA), K-Nearest Neighbour (KNN), Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) on basis of eight different metrics. The main parameter in evaluating any algorithms is its performance. Misclassification rate must be less in classification problems, which says that the accuracy rate must be high. Location and gender of the person have become very crucial in economic markets in the form of AdSense. Here with this comparative model algorithm, we are trying to assess the different ML algorithms and find the best fit for gender classification of acoustic data.

  19. A proposed United States resource classification system

    International Nuclear Information System (INIS)

    Masters, C.D.

    1980-01-01

    Energy is a world-wide problem calling for world-wide communication to resolve the many supply and distribution problems. Essential to a communication problem are a definition and comparability of elements being communicated. The US Geological Survey, with the co-operation of the US Bureau of Mines and the US Department of Energy, has devised a classification system for all mineral resources, the principles of which, it is felt, offer the possibility of world communication. At present several other systems, extant or under development (Potential Gas Committee of the USA, United Nations Resource Committee, and the American Society of Testing and Materials) are internally consistent and provide easy communication linkage. The system in use by the uranium community in the United States of America, however, ties resource quantities to forward-cost dollar values rendering them inconsistent with other classifications and therefore not comparable. This paper develops the rationale for the new USGS resource classification and notes its benefits relative to a forward-cost classification and its relationship specifically to other current classifications. (author)

  20. An Extended Spectral-Spatial Classification Approach for Hyperspectral Data

    Science.gov (United States)

    Akbari, D.

    2017-11-01

    In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF) algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1) unsupervised feature extraction methods including principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF); (2) supervised feature extraction including decision boundary feature extraction (DBFE), discriminate analysis feature extraction (DAFE), and nonparametric weighted feature extraction (NWFE); (3) genetic algorithm (GA). The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8 % overall accuracy higher than the original MSF-based algorithm.

  1. Nonlinear Inertia Classification Model and Application

    Directory of Open Access Journals (Sweden)

    Mei Wang

    2014-01-01

    Full Text Available Classification model of support vector machine (SVM overcomes the problem of a big number of samples. But the kernel parameter and the punishment factor have great influence on the quality of SVM model. Particle swarm optimization (PSO is an evolutionary search algorithm based on the swarm intelligence, which is suitable for parameter optimization. Accordingly, a nonlinear inertia convergence classification model (NICCM is proposed after the nonlinear inertia convergence (NICPSO is developed in this paper. The velocity of NICPSO is firstly defined as the weighted velocity of the inertia PSO, and the inertia factor is selected to be a nonlinear function. NICPSO is used to optimize the kernel parameter and a punishment factor of SVM. Then, NICCM classifier is trained by using the optical punishment factor and the optical kernel parameter that comes from the optimal particle. Finally, NICCM is applied to the classification of the normal state and fault states of online power cable. It is experimentally proved that the iteration number for the proposed NICPSO to reach the optimal position decreases from 15 to 5 compared with PSO; the training duration is decreased by 0.0052 s and the recognition precision is increased by 4.12% compared with SVM.

  2. Cesarean deliveries and maternal weight retention.

    Science.gov (United States)

    Kapinos, Kandice A; Yakusheva, Olga; Weiss, Marianne

    2017-10-04

    Cesarean delivery accounts for nearly one-third of all births in the U.S. and contributes to an additional $38 billion in healthcare costs each year. Although Cesarean delivery has a long record of improving maternal and neonatal mortality and morbidity, increased utilization over time has yielded public health concerns and calls for reductions. Observational evidence suggests Cesarean delivery is associated with increased maternal postpartum weight, which may have significant implications for the obesity epidemic. Previous literature, however, typically does not address selection biases stemming from correlations of pre-pregnancy weight and reproductive health with Cesarean delivery. We used fetal malpresentation as a natural experiment as it predicts Cesarean delivery but is uncorrelated with pre-pregnancy weight or maternal health. We used hospital administrative data (including fields used in vital birth record) from the state of Wisconsin from 2006 to 2013 to create a sample of mothers with at least two births. Using propensity score methods, we compared maternal weight prior to the second pregnancy of mothers who delivered via Cesarean due to fetal malpresentation to mothers who deliver vaginally. We found no evidence that Cesarean delivery in the first pregnancy causally leads to greater maternal weight, BMI, or movement to a higher BMI classification prior to the second pregnancy. After accounting for correlations between pre-pregnancy weight, gestational weight gain, and mode of delivery, there is no evidence of a causal link between Cesarean delivery and maternal weight retention.

  3. Multispectral Image classification using the theories of neural networks

    International Nuclear Information System (INIS)

    Ardisasmita, M.S.; Subki, M.I.R.

    1997-01-01

    Image classification is the one of the important part of digital image analysis. the objective of image classification is to identify and regroup the features occurring in an image into one or several classes in terms of the object. basic to the understanding of multispectral classification is the concept of the spectral response of an object as a function of the electromagnetic radiation and the wavelength of the spectrum. new approaches to classification has been developed to improve the result of analysis, these state-of-the-art classifiers are based upon the theories of neural networks. Neural network classifiers are algorithmes which mimic the computational abilities of the human brain. Artificial neurons are simple emulation's of biological neurons; they take in information from sensors or other artificial neurons, perform very simple operations on this data, and pass the result to other recognize the spectral signature of each image pixel. Neural network image classification has been divided into supervised and unsupervised training procedures. In the supervised approach, examples of each cover type can be located and the computer can compute spectral signatures to categorize all pixels in a digital image into several land cover classes. In supervised classification, spectral signatures are generated by mathematically grouping and it does not require analyst-specified training data. Thus, in the supervised approach we define useful information categories and then examine their spectral reparability; in the unsupervised approach the computer determines spectrally sapable classes and then we define thei information value

  4. Classification of tubulo-papillary renal cortical tumours using estimates of nuclear volume

    DEFF Research Database (Denmark)

    Brooks, B; Sørensen, Flemming Brandt; Olsen, S

    1993-01-01

    The classification of renal cortical tumours is problematic, with no clear division of benign from malignant tumours. Unbiased stereological estimates of volume-weighted nuclear volume (nuclear vv) were obtained by point sampling of nuclear intercepts in a retrospective study of 36 variably sized...

  5. Risk factors affecting injury severity determined by the MAIS score.

    Science.gov (United States)

    Ferreira, Sara; Amorim, Marco; Couto, Antonio

    2017-07-04

    Traffic crashes result in a loss of life but also impact the quality of life and productivity of crash survivors. Given the importance of traffic crash outcomes, the issue has received attention from researchers and practitioners as well as government institutions, such as the European Commission (EC). Thus, to obtain detailed information on the injury type and severity of crash victims, hospital data have been proposed for use alongside police crash records. A new injury severity classification based on hospital data, called the maximum abbreviated injury scale (MAIS), was developed and recently adopted by the EC. This study provides an in-depth analysis of the factors that affect injury severity as classified by the MAIS score. In this study, the MAIS score was derived from the International Classification of Diseases. The European Union adopted an MAIS score equal to or greater than 3 as the definition for a serious traffic crash injury. Gains are expected from using both police and hospital data because the injury severities of the victims are detailed by medical staff and the characteristics of the crash and the site of its occurrence are also provided. The data were obtained by linking police and hospital data sets from the Porto metropolitan area of Portugal over a 6-year period (2006-2011). A mixed logit model was used to understand the factors that contribute to the injury severity of traffic victims and to explore the impact of these factors on injury severity. A random parameter approach offers methodological flexibility to capture individual-specific heterogeneity. Additionally, to understand the importance of using a reliable injury severity scale, we compared MAIS with length of hospital stay (LHS), a classification used by several countries, including Portugal, to officially report injury severity. To do so, the same statistical technique was applied using the same variables to analyze their impact on the injury severity classified according to LHS

  6. Seafloor backscatter signal simulation and classification

    Digital Repository Service at National Institute of Oceanography (India)

    Mahale, V.; El Dine, W.G.; Chakraborty, B.

    . In this model a smooth echo envelope is generated then mixed up with multiplicative and additive noise. Several such echo signals were simulated for three types of seafloor. An Artificial Neural Network based classification technique is conceived to classify...

  7. A Comprehensive Study of Features and Algorithms for URL-Based Topic Classification

    CERN Document Server

    Weber, I; Henzinger, M; Baykan, E

    2011-01-01

    Given only the URL of a Web page, can we identify its topic? We study this problem in detail by exploring a large number of different feature sets and algorithms on several datasets. We also show that the inherent overlap between topics and the sparsity of the information in URLs makes this a very challenging problem. Web page classification without a page's content is desirable when the content is not available at all, when a classification is needed before obtaining the content, or when classification speed is of utmost importance. For our experiments we used five different corpora comprising a total of about 3 million (URL, classification) pairs. We evaluated several techniques for feature generation and classification algorithms. The individual binary classifiers were then combined via boosting into metabinary classifiers. We achieve typical F-measure values between 80 and 85, and a typical precision of around 86. The precision can be pushed further over 90 while maintaining a typical level of recall betw...

  8. Body weight loss by very-low-calorie diet program improves small artery reactive hyperemia in severely obese patients.

    Science.gov (United States)

    Merino, J; Megias-Rangil, I; Ferré, R; Plana, N; Girona, J; Rabasa, A; Aragonés, G; Cabré, A; Bonada, A; Heras, M; Masana, L

    2013-01-01

    Endothelial dysfunction is a major underlying mechanism for the elevated cardiovascular risk associated with increased body weight. We aimed to assess the impact of weight loss induced by an intensive very-low-calorie diet (VLCD) on arterial wall function in severely obese patients (SOP). Thirty-four SOP were admitted to the metabolic ward of the hospital for a 3-week period. A VLCD characterized by a liquid diet providing 800 kcal/day was administered. The small artery reactivity to postischemic hyperemia index (saRHI), a surrogate marker of endothelial function, was assessed before and 1 week after hospital discharge. Anthropometry and biochemical parameters were also measured. Obese and non-obese age- and gender-matched groups were recruited for baseline comparisons. SOP had significantly lower saRHI compared with obese and non-obese individuals. SaRHI significantly increased after the intervention in SOP (1.595 ± 0.236 vs. 1.737 ± 0.417, p = 0.015). A significant improvement in glucose (p = 0.026), systolic blood pressure (p = 0.049), LDLc (p reactivity, and it was associated with the amelioration of metabolic and inflammation markers. Endothelial dysfunction may be softened by body weight loss interventions and useful in the management of cardiovascular risk factors in SOP.

  9. Face classification using electronic synapses

    Science.gov (United States)

    Yao, Peng; Wu, Huaqiang; Gao, Bin; Eryilmaz, Sukru Burc; Huang, Xueyao; Zhang, Wenqiang; Zhang, Qingtian; Deng, Ning; Shi, Luping; Wong, H.-S. Philip; Qian, He

    2017-05-01

    Conventional hardware platforms consume huge amount of energy for cognitive learning due to the data movement between the processor and the off-chip memory. Brain-inspired device technologies using analogue weight storage allow to complete cognitive tasks more efficiently. Here we present an analogue non-volatile resistive memory (an electronic synapse) with foundry friendly materials. The device shows bidirectional continuous weight modulation behaviour. Grey-scale face classification is experimentally demonstrated using an integrated 1024-cell array with parallel online training. The energy consumption within the analogue synapses for each iteration is 1,000 × (20 ×) lower compared to an implementation using Intel Xeon Phi processor with off-chip memory (with hypothetical on-chip digital resistive random access memory). The accuracy on test sets is close to the result using a central processing unit. These experimental results consolidate the feasibility of analogue synaptic array and pave the way toward building an energy efficient and large-scale neuromorphic system.

  10. Random forests for classification in ecology

    Science.gov (United States)

    Cutler, D.R.; Edwards, T.C.; Beard, K.H.; Cutler, A.; Hess, K.T.; Gibson, J.; Lawler, J.J.

    2007-01-01

    Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature. ?? 2007 by the Ecological Society of America.

  11. Improved motion description for action classification

    NARCIS (Netherlands)

    Jain, M.; Jégou, H.; Bouthemy, P.

    2016-01-01

    Even though the importance of explicitly integrating motion characteristics in video descriptions has been demonstrated by several recent papers on action classification, our current work concludes that adequately decomposing visual motion into dominant and residual motions, i.e., camera and scene

  12. Self-reported pain severity, quality of life, disability, anxiety and depression in patients classified with 'nociceptive', 'peripheral neuropathic' and 'central sensitisation' pain. The discriminant validity of mechanisms-based classifications of low back (±leg) pain.

    LENUS (Irish Health Repository)

    Smart, Keith M

    2012-04-01

    Evidence of validity is required to support the use of mechanisms-based classifications of pain clinically. The purpose of this study was to evaluate the discriminant validity of \\'nociceptive\\' (NP), \\'peripheral neuropathic\\' (PNP) and \\'central sensitisation\\' (CSP) as mechanisms-based classifications of pain in patients with low back (±leg) pain by evaluating the extent to which patients classified in this way differ from one another according to health measures associated with various dimensions of pain. This study employed a cross-sectional, between-subjects design. Four hundred and sixty-four patients with low back (±leg) pain were assessed using a standardised assessment protocol. Clinicians classified each patient\\'s pain using a mechanisms-based classification approach. Patients completed a number of self-report measures associated with pain severity, health-related quality of life, functional disability, anxiety and depression. Discriminant validity was evaluated using a multivariate analysis of variance. There was a statistically significant difference between pain classifications on the combined self-report measures, (p = .001; Pillai\\'s Trace = .33; partial eta squared = .16). Patients classified with CSP (n = 106) reported significantly more severe pain, poorer general health-related quality of life, and greater levels of back pain-related disability, depression and anxiety compared to those classified with PNP (n = 102) and NP (n = 256). A similar pattern was found in patients with PNP compared to NP. Mechanisms-based pain classifications may reflect meaningful differences in attributes underlying the multidimensionality of pain. Further studies are required to evaluate the construct and criterion validity of mechanisms-based classifications of musculoskeletal pain.

  13. New Classification of Focal Cortical Dysplasia: Application to Practical Diagnosis

    Science.gov (United States)

    Bae, Yoon-Sung; Kang, Hoon-Chul; Kim, Heung Dong; Kim, Se Hoon

    2012-01-01

    Background and Purpose: Malformation of cortical development (MCD) is a well-known cause of drug-resistant epilepsy and focal cortical dysplasia (FCD) is the most common neuropathological finding in surgical specimens from drug-resistant epilepsy patients. Palmini’s classification proposed in 2004 is now widely used to categorize FCD. Recently, however, Blumcke et al. recommended a new system for classifying FCD in 2011. Methods: We applied the new classification system in practical diagnosis of a sample of 117 patients who underwent neurosurgical operations due to drug-resistant epilepsy at Severance Hospital in Seoul, Korea. Results: Among 117 cases, a total of 16 cases were shifted to other FCD subtypes under the new classification system. Five cases were reclassified to type IIIa and five cases were categorized as dual pathology. The other six cases were changed within the type I category. Conclusions: The most remarkable changes in the new classification system are the advent of dual pathology and FCD type III. Thus, it will be very important for pathologists and clinicians to discriminate between these new categories. More large-scale research needs to be conducted to elucidate the clinical influence of the alterations within the classification of type I disease. Although the new FCD classification system has several advantages compared to the former, the correlation with clinical characteristics is not yet clear. PMID:24649461

  14. Feature weighting using particle swarm optimization for learning vector quantization classifier

    Science.gov (United States)

    Dongoran, A.; Rahmadani, S.; Zarlis, M.; Zakarias

    2018-03-01

    This paper discusses and proposes a method of feature weighting in classification assignments on competitive learning artificial neural network LVQ. The weighting feature method is the search for the weight of an attribute using the PSO so as to give effect to the resulting output. This method is then applied to the LVQ-Classifier and tested on the 3 datasets obtained from the UCI Machine Learning repository. Then an accuracy analysis will be generated by two approaches. The first approach using LVQ1, referred to as LVQ-Classifier and the second approach referred to as PSOFW-LVQ, is a proposed model. The result shows that the PSO algorithm is capable of finding attribute weights that increase LVQ-classifier accuracy.

  15. A Development of Group Decision Support System for Strategic Item Classification using Analytic Hierarchy Process

    International Nuclear Information System (INIS)

    Yoon, Sung Ho; Tae, Jae Woong; Yang, Seung Hyo; Shin, Dong Hoon

    2016-01-01

    Korea has carried out export controls on nuclear items that reflect the Nuclear Suppliers Group (NSG) guidelines (Notice on Trade of Strategic Item of Foreign Trade Act) since joining the NSG in 1995. Nuclear export control starts with classifications that determine whether export items are relevant to nuclear proliferation or not according to NSG guidelines. However, due to qualitative characteristics of nuclear item definition in the guidelines, classification spends a lot of time and effort to make a consensus. The aim of this study is to provide an analysis of an experts' group decision support system (GDSS) based on an analytic hierarchy process (AHP) for the classification of strategic items. The results of this study clearly demonstrated that a GDSS based on an AHP proved positive, systematically providing relative weight among the planning variables and objectives. By using an AHP we can quantify the subjective judgements of reviewers. An order of priority is derived from a numerical value. The verbal and fuzzy measurement of an AHP enables us reach a consensus among reviewers in a GDSS. An AHP sets common weight factors which are a priority of each attribute that represent the views of an entire group. It makes a consistency in decision-making that is important for classification

  16. A Development of Group Decision Support System for Strategic Item Classification using Analytic Hierarchy Process

    Energy Technology Data Exchange (ETDEWEB)

    Yoon, Sung Ho; Tae, Jae Woong; Yang, Seung Hyo; Shin, Dong Hoon [Korea Institute of Nuclear Nonproliferation and Control, Daejeon (Korea, Republic of)

    2016-05-15

    Korea has carried out export controls on nuclear items that reflect the Nuclear Suppliers Group (NSG) guidelines (Notice on Trade of Strategic Item of Foreign Trade Act) since joining the NSG in 1995. Nuclear export control starts with classifications that determine whether export items are relevant to nuclear proliferation or not according to NSG guidelines. However, due to qualitative characteristics of nuclear item definition in the guidelines, classification spends a lot of time and effort to make a consensus. The aim of this study is to provide an analysis of an experts' group decision support system (GDSS) based on an analytic hierarchy process (AHP) for the classification of strategic items. The results of this study clearly demonstrated that a GDSS based on an AHP proved positive, systematically providing relative weight among the planning variables and objectives. By using an AHP we can quantify the subjective judgements of reviewers. An order of priority is derived from a numerical value. The verbal and fuzzy measurement of an AHP enables us reach a consensus among reviewers in a GDSS. An AHP sets common weight factors which are a priority of each attribute that represent the views of an entire group. It makes a consistency in decision-making that is important for classification.

  17. Energy-efficiency based classification of the manufacturing workstation

    Science.gov (United States)

    Frumuşanu, G.; Afteni, C.; Badea, N.; Epureanu, A.

    2017-08-01

    EU Directive 92/75/EC established for the first time an energy consumption labelling scheme, further implemented by several other directives. As consequence, nowadays many products (e.g. home appliances, tyres, light bulbs, houses) have an EU Energy Label when offered for sale or rent. Several energy consumption models of manufacturing equipments have been also developed. This paper proposes an energy efficiency - based classification of the manufacturing workstation, aiming to characterize its energetic behaviour. The concept of energy efficiency of the manufacturing workstation is defined. On this base, a classification methodology has been developed. It refers to specific criteria and their evaluation modalities, together to the definition & delimitation of energy efficiency classes. The energy class position is defined after the amount of energy needed by the workstation in the middle point of its operating domain, while its extension is determined by the value of the first coefficient from the Taylor series that approximates the dependence between the energy consume and the chosen parameter of the working regime. The main domain of interest for this classification looks to be the optimization of the manufacturing activities planning and programming. A case-study regarding an actual lathe classification from energy efficiency point of view, based on two different approaches (analytical and numerical) is also included.

  18. Feasibility and validity of International Classification of Diseases based case mix indices.

    Science.gov (United States)

    Yang, Che-Ming; Reinke, William

    2006-10-06

    Severity of illness is an omnipresent confounder in health services research. Resource consumption can be applied as a proxy of severity. The most commonly cited hospital resource consumption measure is the case mix index (CMI) and the best-known illustration of the CMI is the Diagnosis Related Group (DRG) CMI used by Medicare in the U.S. For countries that do not have DRG type CMIs, the adjustment for severity has been troublesome for either reimbursement or research purposes. The research objective of this study is to ascertain the construct validity of CMIs derived from International Classification of Diseases (ICD) in comparison with DRG CMI. The study population included 551 acute care hospitals in Taiwan and 2,462,006 inpatient reimbursement claims. The 18th version of GROUPER, the Medicare DRG classification software, was applied to Taiwan's 1998 National Health Insurance (NHI) inpatient claim data to derive the Medicare DRG CMI. The same weighting principles were then applied to determine the ICD principal diagnoses and procedures based costliness and length of stay (LOS) CMIs. Further analyses were conducted based on stratifications according to teaching status, accreditation levels, and ownership categories. The best ICD-based substitute for the DRG costliness CMI (DRGCMI) is the ICD principal diagnosis costliness CMI (ICDCMI-DC) in general and in most categories with Spearman's correlation coefficients ranging from 0.938-0.462. The highest correlation appeared in the non-profit sector. ICD procedure costliness CMI (ICDCMI-PC) outperformed ICDCMI-DC only at the medical center level, which consists of tertiary care hospitals and is more procedure intensive. The results of our study indicate that an ICD-based CMI can quite fairly approximate the DRGCMI, especially ICDCMI-DC. Therefore, substituting ICDs for DRGs in computing the CMI ought to be feasible and valid in countries that have not implemented DRGs.

  19. Feasibility and validity of International Classification of Diseases based case mix indices

    Directory of Open Access Journals (Sweden)

    Reinke William

    2006-10-01

    Full Text Available Abstract Background Severity of illness is an omnipresent confounder in health services research. Resource consumption can be applied as a proxy of severity. The most commonly cited hospital resource consumption measure is the case mix index (CMI and the best-known illustration of the CMI is the Diagnosis Related Group (DRG CMI used by Medicare in the U.S. For countries that do not have DRG type CMIs, the adjustment for severity has been troublesome for either reimbursement or research purposes. The research objective of this study is to ascertain the construct validity of CMIs derived from International Classification of Diseases (ICD in comparison with DRG CMI. Methods The study population included 551 acute care hospitals in Taiwan and 2,462,006 inpatient reimbursement claims. The 18th version of GROUPER, the Medicare DRG classification software, was applied to Taiwan's 1998 National Health Insurance (NHI inpatient claim data to derive the Medicare DRG CMI. The same weighting principles were then applied to determine the ICD principal diagnoses and procedures based costliness and length of stay (LOS CMIs. Further analyses were conducted based on stratifications according to teaching status, accreditation levels, and ownership categories. Results The best ICD-based substitute for the DRG costliness CMI (DRGCMI is the ICD principal diagnosis costliness CMI (ICDCMI-DC in general and in most categories with Spearman's correlation coefficients ranging from 0.938-0.462. The highest correlation appeared in the non-profit sector. ICD procedure costliness CMI (ICDCMI-PC outperformed ICDCMI-DC only at the medical center level, which consists of tertiary care hospitals and is more procedure intensive. Conclusion The results of our study indicate that an ICD-based CMI can quite fairly approximate the DRGCMI, especially ICDCMI-DC. Therefore, substituting ICDs for DRGs in computing the CMI ought to be feasible and valid in countries that have not

  20. A New Classification Approach Based on Multiple Classification Rules

    OpenAIRE

    Zhongmei Zhou

    2014-01-01

    A good classifier can correctly predict new data for which the class label is unknown, so it is important to construct a high accuracy classifier. Hence, classification techniques are much useful in ubiquitous computing. Associative classification achieves higher classification accuracy than some traditional rule-based classification approaches. However, the approach also has two major deficiencies. First, it generates a very large number of association classification rules, especially when t...

  1. A Novel Feature Level Fusion for Heart Rate Variability Classification Using Correntropy and Cauchy-Schwarz Divergence.

    Science.gov (United States)

    Goshvarpour, Ateke; Goshvarpour, Atefeh

    2018-04-30

    Heart rate variability (HRV) analysis has become a widely used tool for monitoring pathological and psychological states in medical applications. In a typical classification problem, information fusion is a process whereby the effective combination of the data can achieve a more accurate system. The purpose of this article was to provide an accurate algorithm for classifying HRV signals in various psychological states. Therefore, a novel feature level fusion approach was proposed. First, using the theory of information, two similarity indicators of the signal were extracted, including correntropy and Cauchy-Schwarz divergence. Applying probabilistic neural network (PNN) and k-nearest neighbor (kNN), the performance of each index in the classification of meditators and non-meditators HRV signals was appraised. Then, three fusion rules, including division, product, and weighted sum rules were used to combine the information of both similarity measures. For the first time, we propose an algorithm to define the weights of each feature based on the statistical p-values. The performance of HRV classification using combined features was compared with the non-combined features. Totally, the accuracy of 100% was obtained for discriminating all states. The results showed the strong ability and proficiency of division and weighted sum rules in the improvement of the classifier accuracies.

  2. Validity of childhood adiposity classification in predicting adolescent overweight and obesity.

    Science.gov (United States)

    Huerta, Michael; Zarka, Salman; Bibi, Haim; Haviv, Jacob; Scharf, Shimon; Gdalevich, Michael

    2010-05-03

    Identification of children at risk for adolescent overweight can assist in targeting interventions. Uncertainty remains regarding the validity of current body mass index (BMI) reference values in predicting future risk on a population basis. This study aimed to assess the validity of current childhood adiposity classifications in predicting adolescent overweight and obesity among Israeli youth. Historical cohort study. School-based childhood health studies and adolescent physical examinations. A total of 3 163 subjects surveyed first at age 8-15 and again at age 17-19. Age, sex, height, weight and BMI. Sensitivity, specificity, positive and negative predictive values, and relative risk of childhood adiposity classification. Childhood overweight and obesity showed low sensitivity and high specificity for predicting adolescent overweight and obesity. Positive predictive values were low and varied by age and sex, but negative predictive values were consistently high in both sexes and all ages (range 0.85-0.99). After adjusting for age and sex, both childhood overweight and obesity substantially increased the risk of adolescent overweight (relative risk [RR] 7.03 and 7.20, respectively) and adolescent obesity (RR 24.34 and 28.41, respectively). Childhood overweight and obesity are strong risk factors for adolescent overweight and obesity among Israeli youth. Normal weight children were at very low risk for adolescent overweight. These findings suggest that population-based health promotion aimed at maintaining normal weight among children should be given preference over risk-guided approaches targeting weight reduction among obese children.

  3. Classification of objects on hyperspectral images — further developments

    DEFF Research Database (Denmark)

    Kucheryavskiy, Sergey V.; Williams, Paul

    Classification of objects (such as tablets, cereals, fruits, etc.) is one of the very important applications of hyperspectral imaging and image analysis. Quite often, a hyperspectral image is represented and analyzed just as a bunch of spectra without taking into account spatial information about...... the pixels, which makes classification objects inefficient. Recently, several methods, which combine spectral and spatial information, has been also developed and this approach becomes more and more wide-spread. The methods use local rank, topology, spectral features calculated for separate objects and other...... spatial characteristics. In this work we would like to show several improvements to the classification method, which utilizes spectral features calculated for individual objects [1]. The features are based (in general) on descriptors of spatial patterns of individual object’s pixels in a common principal...

  4. Constructions and classifications of projective Poisson varieties.

    Science.gov (United States)

    Pym, Brent

    2018-01-01

    This paper is intended both as an introduction to the algebraic geometry of holomorphic Poisson brackets, and as a survey of results on the classification of projective Poisson manifolds that have been obtained in the past 20 years. It is based on the lecture series delivered by the author at the Poisson 2016 Summer School in Geneva. The paper begins with a detailed treatment of Poisson surfaces, including adjunction, ruled surfaces and blowups, and leading to a statement of the full birational classification. We then describe several constructions of Poisson threefolds, outlining the classification in the regular case, and the case of rank-one Fano threefolds (such as projective space). Following a brief introduction to the notion of Poisson subspaces, we discuss Bondal's conjecture on the dimensions of degeneracy loci on Poisson Fano manifolds. We close with a discussion of log symplectic manifolds with simple normal crossings degeneracy divisor, including a new proof of the classification in the case of rank-one Fano manifolds.

  5. Constructions and classifications of projective Poisson varieties

    Science.gov (United States)

    Pym, Brent

    2018-03-01

    This paper is intended both as an introduction to the algebraic geometry of holomorphic Poisson brackets, and as a survey of results on the classification of projective Poisson manifolds that have been obtained in the past 20 years. It is based on the lecture series delivered by the author at the Poisson 2016 Summer School in Geneva. The paper begins with a detailed treatment of Poisson surfaces, including adjunction, ruled surfaces and blowups, and leading to a statement of the full birational classification. We then describe several constructions of Poisson threefolds, outlining the classification in the regular case, and the case of rank-one Fano threefolds (such as projective space). Following a brief introduction to the notion of Poisson subspaces, we discuss Bondal's conjecture on the dimensions of degeneracy loci on Poisson Fano manifolds. We close with a discussion of log symplectic manifolds with simple normal crossings degeneracy divisor, including a new proof of the classification in the case of rank-one Fano manifolds.

  6. 78 FR 68983 - Cotton Futures Classification: Optional Classification Procedure

    Science.gov (United States)

    2013-11-18

    ...-AD33 Cotton Futures Classification: Optional Classification Procedure AGENCY: Agricultural Marketing... regulations to allow for the addition of an optional cotton futures classification procedure--identified and... response to requests from the U.S. cotton industry and ICE, AMS will offer a futures classification option...

  7. A fingerprint classification algorithm based on combination of local and global information

    Science.gov (United States)

    Liu, Chongjin; Fu, Xiang; Bian, Junjie; Feng, Jufu

    2011-12-01

    Fingerprint recognition is one of the most important technologies in biometric identification and has been wildly applied in commercial and forensic areas. Fingerprint classification, as the fundamental procedure in fingerprint recognition, can sharply decrease the quantity for fingerprint matching and improve the efficiency of fingerprint recognition. Most fingerprint classification algorithms are based on the number and position of singular points. Because the singular points detecting method only considers the local information commonly, the classification algorithms are sensitive to noise. In this paper, we propose a novel fingerprint classification algorithm combining the local and global information of fingerprint. Firstly we use local information to detect singular points and measure their quality considering orientation structure and image texture in adjacent areas. Furthermore the global orientation model is adopted to measure the reliability of singular points group. Finally the local quality and global reliability is weighted to classify fingerprint. Experiments demonstrate the accuracy and effectivity of our algorithm especially for the poor quality fingerprint images.

  8. Employee weight management through health coaching.

    Science.gov (United States)

    Merrill, R M; Aldana, S G; Bowden, D E

    2010-01-01

    This study will evaluate the effectiveness of an interactive health coaching intervention at lowering weight. The study involved 5405 overweight or obese employees aged 18-85, who entered the program sometime during 2001-2008. Average body mass index (BMI) significantly decreased from 32.1 at baseline to 31.4 at 3 months, 31.0 at 6 months, and 30.6 at 12 months. Decreasing BMI was more pronounced in older age groups and among women, those using weight loss medication, those with higher BMI, and those with higher motivation and confidence to make behavior changes. When the effects of these variables on the decreasing trend in BMI were simultaneously estimated, only baseline classifications of BMI, health status, and confidence remained significant. Change in BMI through 12 months was -0.7% for those with normal weight, -2.0% for overweight, -3.6% for obese, and -7.1% for morbidly obese individuals at baseline. Among morbidly obese individuals, decrease in BMI through 12 months was -7.6% for those with "high" confidence to lose weight at baseline vs -4.4% for those with low confidence. Better health status at baseline was also related to more pronounced weight loss. Interactive health coaching significantly lowered BMI among participants through 3, 6, and 12 months of follow-up.

  9. HIV classification using coalescent theory

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Ming [Los Alamos National Laboratory; Letiner, Thomas K [Los Alamos National Laboratory; Korber, Bette T [Los Alamos National Laboratory

    2008-01-01

    Algorithms for subtype classification and breakpoint detection of HIV-I sequences are based on a classification system of HIV-l. Hence, their quality highly depend on this system. Due to the history of creation of the current HIV-I nomenclature, the current one contains inconsistencies like: The phylogenetic distance between the subtype B and D is remarkably small compared with other pairs of subtypes. In fact, it is more like the distance of a pair of subsubtypes Robertson et al. (2000); Subtypes E and I do not exist any more since they were discovered to be composed of recombinants Robertson et al. (2000); It is currently discussed whether -- instead of CRF02 being a recombinant of subtype A and G -- subtype G should be designated as a circulating recombination form (CRF) nd CRF02 as a subtype Abecasis et al. (2007); There are 8 complete and over 400 partial HIV genomes in the LANL-database which belong neither to a subtype nor to a CRF (denoted by U). Moreover, the current classification system is somehow arbitrary like all complex classification systems that were created manually. To this end, it is desirable to deduce the classification system of HIV systematically by an algorithm. Of course, this problem is not restricted to HIV, but applies to all fast mutating and recombining viruses. Our work addresses the simpler subproblem to score classifications of given input sequences of some virus species (classification denotes a partition of the input sequences in several subtypes and CRFs). To this end, we reconstruct ancestral recombination graphs (ARG) of the input sequences under restrictions determined by the given classification. These restritions are imposed in order to ensure that the reconstructed ARGs do not contradict the classification under consideration. Then, we find the ARG with maximal probability by means of Markov Chain Monte Carlo methods. The probability of the most probable ARG is interpreted as a score for the classification. To our

  10. Epidemiology, classification, and modifiable risk factors of peripheral arterial disease

    Directory of Open Access Journals (Sweden)

    Nicolas W Shammas

    2007-05-01

    Full Text Available Nicolas W ShammasMidwest Cardiovascular Research Foundation, Cardiovascular Medicine, PC, Davenport, IA, USAAbstract: Peripheral arterial disease (PAD is part of a global vascular problem of diffuse atherosclerosis. PAD patients die mostly of cardiac and cerebrovascular-related events and much less frequently due to obstructive disease of the lower extremities. Aggressive risk factors modification is needed to reduce cardiac mortality in PAD patients. These include smoking cessation, reduction of blood pressure to current guidelines, aggressive low density lipoprotein lowering, losing weight, controlling diabetes and the use of oral antiplatelet drugs such as aspirin or clopidogrel. In addition to quitting smoking and exercise, cilostazol and statins have been shown to reduce claudication in patients with PAD. Patients with critical rest limb ischemia or severe progressive claudication need to be treated with revascularization to minimize the chance of limb loss, reduce symptoms, and improve quality of life.Keywords: peripheral arterial disease, epidemiology, risk factors, classification

  11. Total motile sperm count: a better indicator for the severity of male factor infertility than the WHO sperm classification system.

    Science.gov (United States)

    Hamilton, J A M; Cissen, M; Brandes, M; Smeenk, J M J; de Bruin, J P; Kremer, J A M; Nelen, W L D M; Hamilton, C J C M

    2015-05-01

    Does the prewash total motile sperm count (TMSC) have a better predictive value for spontaneous ongoing pregnancy (SOP) than the World Health Organization (WHO) classification system? The prewash TMSC shows a better correlation with the spontaneous ongoing pregnancy rate (SOPR) than the WHO 2010 classification system. According to the WHO classification system, an abnormal semen analysis can be diagnosed as oligozoospermia, astenozoospermia, teratozoospermia or combinations of these and azoospermia. This classification is based on the fifth percentile cut-off values of a cohort of 1953 men with proven fertility. Although this classification suggests accuracy, the relevance for the prognosis of an infertile couple and the choice of treatment is questionable. The TMSC is obtained by multiplying the sample volume by the density and the percentage of A and B motility spermatozoa. We analyzed data from a longitudinal cohort study among unselected infertile couples who were referred to three Dutch hospitals between January 2002 and December 2006. Of the total cohort of 2476 infertile couples, only the couples with either male infertility as a single diagnosis or unexplained infertility were included (n = 1177) with a follow-up period of 3 years. In all couples a semen analysis was performed. Based on the best semen analysis if more tests were performed, couples were grouped according to the WHO classification system and the TMSC range, as described in the Dutch national guidelines for male infertility. The primary outcome measure was the SOPR, which occurred before, during or after treatments, including expectant management, intrauterine insemination, in vitro fertilization or intracytoplasmic sperm injection. After adjustment for the confounding factors (female and male age, duration and type of infertility and result of the postcoital test) the odd ratios (ORs) for risk of SOP for each WHO and TMSC group were calculated. The couples with unexplained infertility were

  12. De Facto Exchange Rate Regime Classifications Are Better Than You Think

    OpenAIRE

    Michael Bleaney; Mo Tian; Lin Yin

    2015-01-01

    Several de facto exchange rate regime classifications have been widely used in empirical research, but they are known to disagree with one another to a disturbing extent. We dissect the algorithms employed and argue that they can be significantly improved. We implement the improvements, and show that there is a far higher agreement rate between the modified classifications. We conclude that the current pessimism about de facto exchange rate regime classification schemes is unwarranted.

  13. Interobserver and intraobserver reliability of radiographic classification of acromioclavicular joint dislocations.

    Science.gov (United States)

    Ringenberg, Jonathan D; Foughty, Zachary; Hall, Adam D; Aldridge, J Mack; Wilson, Joseph B; Kuremsky, Marshall A

    2018-03-01

    The classification and treatment of acromioclavicular (AC) joint dislocations remain controversial. The purpose of this study was to determine the interobserver and intraobserver reliability of the Rockwood classification system. We hypothesized poor interobserver and intraobserver reliability, limiting the role of the Rockwood classification system in determining severity of AC joint dislocations and accurately guiding treatment decisions. We identified 200 patients with AC joint injuries using the International Classification of Diseases, Ninth Revision code 831.04. Fifty patients met inclusion criteria. Deidentified radiographs were compiled and presented to 6 fellowship-trained upper extremity orthopedic surgeons. The surgeons classified each patient into 1 of the 6 classification types described by Rockwood. A second review was performed several months later by 2 surgeons. A κ value was calculated to determine the interobserver and intraobserver reliability. The interobserver and intraobserver κ values were fair (κ = 0.278) and moderate (κ = 0.468), respectively. Interobserver results showed that 4 of the 50 radiographic images had a unanimous classification. Intraobserver results for the 2 surgeons showed that 18 of the 50 images were rated the same on second review by the first surgeon and 38 of the 50 images were rated the same on second review by the second surgeon. We found that the Rockwood classification system has limited interobserver and intraobserver reliability. We believe that unreliable classification may account for some of the inconsistent treatment outcomes among patients with similarly classified injuries. We suggest that a better classification system is needed to use radiographic imaging for diagnosis and treatment of AC joint dislocations. Copyright © 2017 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.

  14. Classification of proteins: available structural space for molecular modeling.

    Science.gov (United States)

    Andreeva, Antonina

    2012-01-01

    The wealth of available protein structural data provides unprecedented opportunity to study and better understand the underlying principles of protein folding and protein structure evolution. A key to achieving this lies in the ability to analyse these data and to organize them in a coherent classification scheme. Over the past years several protein classifications have been developed that aim to group proteins based on their structural relationships. Some of these classification schemes explore the concept of structural neighbourhood (structural continuum), whereas other utilize the notion of protein evolution and thus provide a discrete rather than continuum view of protein structure space. This chapter presents a strategy for classification of proteins with known three-dimensional structure. Steps in the classification process along with basic definitions are introduced. Examples illustrating some fundamental concepts of protein folding and evolution with a special focus on the exceptions to them are presented.

  15. Three-dimensional textural features of conventional MRI improve diagnostic classification of childhood brain tumours.

    Science.gov (United States)

    Fetit, Ahmed E; Novak, Jan; Peet, Andrew C; Arvanitits, Theodoros N

    2015-09-01

    The aim of this study was to assess the efficacy of three-dimensional texture analysis (3D TA) of conventional MR images for the classification of childhood brain tumours in a quantitative manner. The dataset comprised pre-contrast T1 - and T2-weighted MRI series obtained from 48 children diagnosed with brain tumours (medulloblastoma, pilocytic astrocytoma and ependymoma). 3D and 2D TA were carried out on the images using first-, second- and higher order statistical methods. Six supervised classification algorithms were trained with the most influential 3D and 2D textural features, and their performances in the classification of tumour types, using the two feature sets, were compared. Model validation was carried out using the leave-one-out cross-validation (LOOCV) approach, as well as stratified 10-fold cross-validation, in order to provide additional reassurance. McNemar's test was used to test the statistical significance of any improvements demonstrated by 3D-trained classifiers. Supervised learning models trained with 3D textural features showed improved classification performances to those trained with conventional 2D features. For instance, a neural network classifier showed 12% improvement in area under the receiver operator characteristics curve (AUC) and 19% in overall classification accuracy. These improvements were statistically significant for four of the tested classifiers, as per McNemar's tests. This study shows that 3D textural features extracted from conventional T1 - and T2-weighted images can improve the diagnostic classification of childhood brain tumours. Long-term benefits of accurate, yet non-invasive, diagnostic aids include a reduction in surgical procedures, improvement in surgical and therapy planning, and support of discussions with patients' families. It remains necessary, however, to extend the analysis to a multicentre cohort in order to assess the scalability of the techniques used. Copyright © 2015 John Wiley & Sons, Ltd.

  16. Computing Adaptive Feature Weights with PSO to Improve Android Malware Detection

    Directory of Open Access Journals (Sweden)

    Yanping Xu

    2017-01-01

    Full Text Available Android malware detection is a complex and crucial issue. In this paper, we propose a malware detection model using a support vector machine (SVM method based on feature weights that are computed by information gain (IG and particle swarm optimization (PSO algorithms. The IG weights are evaluated based on the relevance between features and class labels, and the PSO weights are adaptively calculated to result in the best fitness (the performance of the SVM classification model. Moreover, to overcome the defects of basic PSO, we propose a new adaptive inertia weight method called fitness-based and chaotic adaptive inertia weight-PSO (FCAIW-PSO that improves on basic PSO and is based on the fitness and a chaotic term. The goal is to assign suitable weights to the features to ensure the best Android malware detection performance. The results of experiments indicate that the IG weights and PSO weights both improve the performance of SVM and that the performance of the PSO weights is better than that of the IG weights.

  17. Five-way smoking status classification using text hot-spot identification and error-correcting output codes.

    Science.gov (United States)

    Cohen, Aaron M

    2008-01-01

    We participated in the i2b2 smoking status classification challenge task. The purpose of this task was to evaluate the ability of systems to automatically identify patient smoking status from discharge summaries. Our submission included several techniques that we compared and studied, including hot-spot identification, zero-vector filtering, inverse class frequency weighting, error-correcting output codes, and post-processing rules. We evaluated our approaches using the same methods as the i2b2 task organizers, using micro- and macro-averaged F1 as the primary performance metric. Our best performing system achieved a micro-F1 of 0.9000 on the test collection, equivalent to the best performing system submitted to the i2b2 challenge. Hot-spot identification, zero-vector filtering, classifier weighting, and error correcting output coding contributed additively to increased performance, with hot-spot identification having by far the largest positive effect. High performance on automatic identification of patient smoking status from discharge summaries is achievable with the efficient and straightforward machine learning techniques studied here.

  18. Combined Kernel-Based BDT-SMO Classification of Hyperspectral Fused Images

    Directory of Open Access Journals (Sweden)

    Fenghua Huang

    2014-01-01

    Full Text Available To solve the poor generalization and flexibility problems that single kernel SVM classifiers have while classifying combined spectral and spatial features, this paper proposed a solution to improve the classification accuracy and efficiency of hyperspectral fused images: (1 different radial basis kernel functions (RBFs are employed for spectral and textural features, and a new combined radial basis kernel function (CRBF is proposed by combining them in a weighted manner; (2 the binary decision tree-based multiclass SMO (BDT-SMO is used in the classification of hyperspectral fused images; (3 experiments are carried out, where the single radial basis function- (SRBF- based BDT-SMO classifier and the CRBF-based BDT-SMO classifier are used, respectively, to classify the land usages of hyperspectral fused images, and genetic algorithms (GA are used to optimize the kernel parameters of the classifiers. The results show that, compared with SRBF, CRBF-based BDT-SMO classifiers display greater classification accuracy and efficiency.

  19. NIM: A Node Influence Based Method for Cancer Classification

    Directory of Open Access Journals (Sweden)

    Yiwen Wang

    2014-01-01

    Full Text Available The classification of different cancer types owns great significance in the medical field. However, the great majority of existing cancer classification methods are clinical-based and have relatively weak diagnostic ability. With the rapid development of gene expression technology, it is able to classify different kinds of cancers using DNA microarray. Our main idea is to confront the problem of cancer classification using gene expression data from a graph-based view. Based on a new node influence model we proposed, this paper presents a novel high accuracy method for cancer classification, which is composed of four parts: the first is to calculate the similarity matrix of all samples, the second is to compute the node influence of training samples, the third is to obtain the similarity between every test sample and each class using weighted sum of node influence and similarity matrix, and the last is to classify each test sample based on its similarity between every class. The data sets used in our experiments are breast cancer, central nervous system, colon tumor, prostate cancer, acute lymphoblastic leukemia, and lung cancer. experimental results showed that our node influence based method (NIM is more efficient and robust than the support vector machine, K-nearest neighbor, C4.5, naive Bayes, and CART.

  20. Inherited epidermolysis bullosa : Updated recommendations on diagnosis and classification

    NARCIS (Netherlands)

    Fine, Jo-David; Bruckner-Tuderman, Leena; Eady, Robin A. J.; Bauer, Eugene A.; Bauer, Johann W.; Has, Cristina; Heagerty, Adrian; Hintner, Helmut; Hovnanian, Alain; Jonkman, Marcel F.; Leigh, Irene; Marinkovich, M. Peter; Martinez, Anna E.; McGrath, John A.; Mellerio, Jemima E.; Moss, Celia; Murrell, Dedee F.; Shimizu, Hiroshi; Uitto, Jouni; Woodley, David; Zambruno, Giovanna

    Background: Several newtargeted genes and clinical subtypes have been identified since publication in 2008 of the report of the last international consensus meeting on diagnosis and classification of epidermolysis bullosa (EB). As a correlate, new clinical manifestations have been seen in several

  1. Preliminary Research on Grassland Fine-classification Based on MODIS

    International Nuclear Information System (INIS)

    Hu, Z W; Zhang, S; Yu, X Y; Wang, X S

    2014-01-01

    Grassland ecosystem is important for climatic regulation, maintaining the soil and water. Research on the grassland monitoring method could provide effective reference for grassland resource investigation. In this study, we used the vegetation index method for grassland classification. There are several types of climate in China. Therefore, we need to use China's Main Climate Zone Maps and divide the study region into four climate zones. Based on grassland classification system of the first nation-wide grass resource survey in China, we established a new grassland classification system which is only suitable for this research. We used MODIS images as the basic data resources, and use the expert classifier method to perform grassland classification. Based on the 1:1,000,000 Grassland Resource Map of China, we obtained the basic distribution of all the grassland types and selected 20 samples evenly distributed in each type, then used NDVI/EVI product to summarize different spectral features of different grassland types. Finally, we introduced other classification auxiliary data, such as elevation, accumulate temperature (AT), humidity index (HI) and rainfall. China's nation-wide grassland classification map is resulted by merging the grassland in different climate zone. The overall classification accuracy is 60.4%. The result indicated that expert classifier is proper for national wide grassland classification, but the classification accuracy need to be improved

  2. Is overall similarity classification less effortful than single-dimension classification?

    Science.gov (United States)

    Wills, Andy J; Milton, Fraser; Longmore, Christopher A; Hester, Sarah; Robinson, Jo

    2013-01-01

    It is sometimes argued that the implementation of an overall similarity classification is less effortful than the implementation of a single-dimension classification. In the current article, we argue that the evidence securely in support of this view is limited, and report additional evidence in support of the opposite proposition--overall similarity classification is more effortful than single-dimension classification. Using a match-to-standards procedure, Experiments 1A, 1B and 2 demonstrate that concurrent load reduces the prevalence of overall similarity classification, and that this effect is robust to changes in the concurrent load task employed, the level of time pressure experienced, and the short-term memory requirements of the classification task. Experiment 3 demonstrates that participants who produced overall similarity classifications from the outset have larger working memory capacities than those who produced single-dimension classifications initially, and Experiment 4 demonstrates that instructions to respond meticulously increase the prevalence of overall similarity classification.

  3. Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection

    Science.gov (United States)

    Li, Zuhe; Fan, Yangyu; Liu, Weihua; Yu, Zeqi; Wang, Fengqin

    2017-01-01

    We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder's hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65% to 78% average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50% while improving classification performance.

  4. Diffusion-weighted magnetic resonance imaging of thymoma: ability of the Apparent Diffusion Coefficient in predicting the World Health Organization (WHO) classification and the Masaoka-Koga staging system and its prognostic significance on disease-free survival

    International Nuclear Information System (INIS)

    Priola, Adriano Massimiliano; Priola, Sandro Massimo; Gned, Dario; Ducco, Lorena; Veltri, Andrea; Giraudo, Maria Teresa; Fornari, Alessandro; Ferrero, Bruno

    2016-01-01

    To evaluate the usefulness of diffusion-weighted magnetic resonance for distinguishing thymomas according to WHO and Masaoka-Koga classifications and in predicting disease-free survival (DFS) by using the apparent diffusion coefficient (ADC). Forty-one patients were grouped based on WHO (low-risk vs. high-risk) and Masaoka-Koga (early vs. advanced) classifications. For prognosis, seven patients with recurrence at follow-up were grouped separately from healthy subjects. Differences on ADC levels between groups were tested using Student-t testing. Logistic regression models and areas under the ROC curve (AUROC) were estimated. Mean ADC values were different between groups of WHO (low-risk = 1.58 ± 0.20 x 10 -3 mm 2 /sec; high-risk = 1.21 ± 0.23 x 10 -3 mm 2 /sec; p < 0.0001) and Masaoka-Koga (early = 1.43 ± 0.26 x 10 -3 mm 2 /sec; advanced = 1.31 ± 0.31 x 10 -3 mm 2 /sec; p = 0.016) classifications. Mean ADC of type-B3 (1.05 ± 0.17 x 10 -3 mm 2 /sec) was lower than type-B2 (1.32 ± 0.20 x 10 -3 mm 2 /sec; p = 0.023). AUROC in discriminating groups was 0.864 for WHO classification (cut-point = 1.309 x 10 -3 mm 2 /sec; accuracy = 78.1 %) and 0.730 for Masaoka-Koga classification (cut-point = 1.243 x 10 -3 mm 2 /sec; accuracy = 73.2 %). Logistic regression models and two-way ANOVA were significant for WHO classification (odds ratio[OR] = 0.93, p = 0.007; p < 0.001), but not for Masaoka-Koga classification (OR = 0.98, p = 0.31; p = 0.38). ADC levels were significantly associated with DFS recurrence rate being higher for patients with ADC ≤ 1.299 x 10 -3 mm 2 /sec (p = 0.001; AUROC, 0.834; accuracy = 78.0 %). ADC helps to differentiate high-risk from low-risk thymomas and discriminates the more aggressive type-B3. Primary tumour ADC is a prognostic indicator of recurrence. (orig.)

  5. Prediction of Excessive Weight Gain in Insulin Treated Patients with Type 2 Diabetes

    DEFF Research Database (Denmark)

    Cichosz, Simon Lebech; Lundby-Christensen, Louise; Johansen, Mette D

    2017-01-01

    of this study was to identify predictors of weight gain in insulin treated patients with Type 2 diabetes mellitus. METHODS: A total of 412 individuals with Type 2 diabetes mellitus were, in addition to metformin or placebo, randomized into 18-month treatment groups with three different insulin analogue......AIMS: Weight gain is an ongoing challenge when initiating insulin therapy in patients with Type 2 diabetes mellitus. However, if prediction of insulin associated weight gain was possible on an individualized level, targeted initiatives could be implemented to reduce weight gain. The objective...... treatment regimens. Participants with excessive weight gain were defined as the group with weight gain in the 4(th) quartile. We developed a pattern classification method to predict individuals prone to excessive weight gain. RESULTS: The median weight gain among all patients (n = 412) was 2.4 (95...

  6. [Definition, etiology, classification and presentation forms].

    Science.gov (United States)

    Mas Garriga, Xavier

    2014-01-01

    Osteoarthritis is defined as a degenerative process affecting the joints as a result of mechanical and biological disorders that destabilize the balance between the synthesis and degradation of joint cartilage, stimulating the growth of subchondral bone; chronic synovitis is also present. Currently, the joint is considered as a functional unit that includes distinct tissues, mainly cartilage, the synovial membrane, and subchondral bone, all of which are involved in the pathogenesis of the disease. Distinct risk factors for the development of osteoarthritis have been described: general, unmodifiable risk factors (age, sex, and genetic makeup), general, modifiable risk factors (obesity and hormonal factors) and local risk factors (prior joint anomalies and joint overload). Notable among the main factors related to disease progression are joint alignment defects and generalized osteoarthritis. Several classifications of osteoarthritis have been proposed but none is particularly important for the primary care management of the disease. These classifications include etiological (primary or idiopathic forms and secondary forms) and topographical (typical and atypical localizations) classifications, the Kellgren and Lawrence classification (radiological repercussions) and that of the American College of Rheumatology for osteoarthritis of the hand, hip and knee. The prevalence of knee osteoarthritis is 10.2% in Spain and shows a marked discrepancy between clinical and radiological findings. Hand osteoarthritis, with a prevalence of symptomatic involvement of around 6.2%, has several forms of presentation (nodal osteoarthritis, generalized osteoarthritis, rhizarthrosis, and erosive osteoarthritis). Symptomatic osteoarthritis of the hip affects between 3.5% and 5.6% of persons older than 50 years and has different radiological patterns depending on femoral head migration. Copyright © 2014 Elsevier España, S.L. All rights reserved.

  7. AN EXTENDED SPECTRAL–SPATIAL CLASSIFICATION APPROACH FOR HYPERSPECTRAL DATA

    Directory of Open Access Journals (Sweden)

    D. Akbari

    2017-11-01

    Full Text Available In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1 unsupervised feature extraction methods including principal component analysis (PCA, independent component analysis (ICA, and minimum noise fraction (MNF; (2 supervised feature extraction including decision boundary feature extraction (DBFE, discriminate analysis feature extraction (DAFE, and nonparametric weighted feature extraction (NWFE; (3 genetic algorithm (GA. The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8 % overall accuracy higher than the original MSF-based algorithm.

  8. Casemix classification payment for sub-acute and non-acute inpatient care, Thailand.

    Science.gov (United States)

    Khiaocharoen, Orathai; Pannarunothai, Supasit; Zungsontiporn, Chairoj; Riewpaiboon, Wachara

    2010-07-01

    There is a need to develop other casemix classifications, apart from DRG for sub-acute and non-acute inpatient care payment mechanism in Thailand. To develop a casemix classification for sub-acute and non-acute inpatient service. The study began with developing a classification system, analyzing cost, assigning payment weights, and ended with testing the validity of this new casemix system. Coefficient of variation, reduction in variance, linear regression, and split-half cross-validation were employed. The casemix for sub-acute and non-acute inpatient services contained 98 groups. Two percent of them had a coefficient of variation of the cost of higher than 1.5. The reduction in variance of cost after the classification was 32%. Two classification variables (physical function and the rehabilitation impairment categories) were key determinants of the cost (adjusted R2 = 0.749, p = .001). Validity results of split-half cross-validation of sub-acute and non-acute inpatient service were high. The present study indicated that the casemix for sub-acute and non-acute inpatient services closely predicted the hospital resource use and should be further developed for payment of the inpatients sub-acute and non-acute phase.

  9. A hybrid particle swarm optimization-SVM classification for automatic cardiac auscultation

    Directory of Open Access Journals (Sweden)

    Prasertsak Charoen

    2017-04-01

    Full Text Available Cardiac auscultation is a method for a doctor to listen to heart sounds, using a stethoscope, for examining the condition of the heart. Automatic cardiac auscultation with machine learning is a promising technique to classify heart conditions without need of doctors or expertise. In this paper, we develop a classification model based on support vector machine (SVM and particle swarm optimization (PSO for an automatic cardiac auscultation system. The model consists of two parts: heart sound signal processing part and a proposed PSO for weighted SVM (WSVM classifier part. In this method, the PSO takes into account the degree of importance for each feature extracted from wavelet packet (WP decomposition. Then, by using principle component analysis (PCA, the features can be selected. The PSO technique is used to assign diverse weights to different features for the WSVM classifier. Experimental results show that both continuous and binary PSO-WSVM models achieve better classification accuracy on the heart sound samples, by reducing system false negatives (FNs, compared to traditional SVM and genetic algorithm (GA based SVM.

  10. Phenotype classification of zebrafish embryos by supervised learning.

    Directory of Open Access Journals (Sweden)

    Nathalie Jeanray

    Full Text Available Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.

  11. Determinants of Integrated Management of Childhood Illness (IMCI) non-severe pneumonia classification and care in Malawi health facilities: Analysis of a national facility census.

    Science.gov (United States)

    Johansson, Emily White; Nsona, Humphreys; Carvajal-Aguirre, Liliana; Amouzou, Agbessi; Hildenwall, Helena

    2017-12-01

    Research shows inadequate Integrated Management of Childhood Illness (IMCI)-pneumonia care in various low-income settings but evidence is largely from small-scale studies with limited evidence of patient-, provider- and facility-levels determinants of IMCI non-severe pneumonia classification and its management. The Malawi Service Provision Assessment 2013-2014 included 3149 outpatients aged 2-59 months with completed observations, interviews and re-examinations. Mixed-effects logistic regression models quantified the influence of patient-, provider and facility-level determinants on having IMCI non-severe pneumonia and its management in observed consultations. Among 3149 eligible outpatients, 590 (18.7%) had IMCI non-severe pneumonia classification in re-examination. 228 (38.7%) classified cases received first-line antibiotics and 159 (26.9%) received no antibiotics. 18.6% with cough or difficult breathing had 60-second respiratory rates counted during consultations, and conducting this assessment was significantly associated with IMCI training ever received (odds ratio (OR) = 2.37, 95% confidence interval (CI): 1.29-4.31) and negative rapid diagnostic test results (OR = 3.21, 95% CI: 1.45-7.13). Older children had lower odds of assessments than infants (OR = 48-59 months: 0.35, 95% CI: 0.16-0.75). Children presenting with any of the following complaints also had reduced odds of assessment: fever, diarrhea, skin problem or any danger sign. First-line antibiotic treatment for classified cases was significantly associated with high temperatures (OR = 3.26, 95% CI: 1.24-8.55) while older children had reduced odds of first-line treatment compared to infants (OR = 48-59 months: 0.29, 95% CI: 0.10-0.83). RDT-confirmed malaria was a significant predictor of no antibiotic receipt for IMCI non-severe pneumonia (OR = 10.65, 95% CI: 2.39-47.36). IMCI non-severe pneumonia care was sub-optimal in Malawi health facilities in 2013-2014 with inadequate

  12. Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder.

    Science.gov (United States)

    Mwangi, Benson; Ebmeier, Klaus P; Matthews, Keith; Steele, J Douglas

    2012-05-01

    Quantitative abnormalities of brain structure in patients with major depressive disorder have been reported at a group level for decades. However, these structural differences appear subtle in comparison with conventional radiologically defined abnormalities, with considerable inter-subject variability. Consequently, it has not been possible to readily identify scans from patients with major depressive disorder at an individual level. Recently, machine learning techniques such as relevance vector machines and support vector machines have been applied to predictive classification of individual scans with variable success. Here we describe a novel hybrid method, which combines machine learning with feature selection and characterization, with the latter aimed at maximizing the accuracy of machine learning prediction. The method was tested using a multi-centre dataset of T(1)-weighted 'structural' scans. A total of 62 patients with major depressive disorder and matched controls were recruited from referred secondary care clinical populations in Aberdeen and Edinburgh, UK. The generalization ability and predictive accuracy of the classifiers was tested using data left out of the training process. High prediction accuracy was achieved (~90%). While feature selection was important for maximizing high predictive accuracy with machine learning, feature characterization contributed only a modest improvement to relevance vector machine-based prediction (~5%). Notably, while the only information provided for training the classifiers was T(1)-weighted scans plus a categorical label (major depressive disorder versus controls), both relevance vector machine and support vector machine 'weighting factors' (used for making predictions) correlated strongly with subjective ratings of illness severity. These results indicate that machine learning techniques have the potential to inform clinical practice and research, as they can make accurate predictions about brain scan data from

  13. Better physical activity classification using smartphone acceleration sensor.

    Science.gov (United States)

    Arif, Muhammad; Bilal, Mohsin; Kattan, Ahmed; Ahamed, S Iqbal

    2014-09-01

    Obesity is becoming one of the serious problems for the health of worldwide population. Social interactions on mobile phones and computers via internet through social e-networks are one of the major causes of lack of physical activities. For the health specialist, it is important to track the record of physical activities of the obese or overweight patients to supervise weight loss control. In this study, acceleration sensor present in the smartphone is used to monitor the physical activity of the user. Physical activities including Walking, Jogging, Sitting, Standing, Walking upstairs and Walking downstairs are classified. Time domain features are extracted from the acceleration data recorded by smartphone during different physical activities. Time and space complexity of the whole framework is done by optimal feature subset selection and pruning of instances. Classification results of six physical activities are reported in this paper. Using simple time domain features, 99 % classification accuracy is achieved. Furthermore, attributes subset selection is used to remove the redundant features and to minimize the time complexity of the algorithm. A subset of 30 features produced more than 98 % classification accuracy for the six physical activities.

  14. ILAE Classification of the Epilepsies Position Paper of the ILAE Commission for Classification and Terminology

    Science.gov (United States)

    Scheffer, Ingrid E; Berkovic, Samuel; Capovilla, Giuseppe; Connolly, Mary B; French, Jacqueline; Guilhoto, Laura; Hirsch, Edouard; Jain, Satish; Mathern, Gary W.; Moshé, Solomon L; Nordli, Douglas R; Perucca, Emilio; Tomson, Torbjörn; Wiebe, Samuel; Zhang, Yue-Hua; Zuberi, Sameer M

    2017-01-01

    Summary The ILAE Classification of the Epilepsies has been updated to reflect our gain in understanding of the epilepsies and their underlying mechanisms following the major scientific advances which have taken place since the last ratified classification in 1989. As a critical tool for the practising clinician, epilepsy classification must be relevant and dynamic to changes in thinking, yet robust and translatable to all areas of the globe. Its primary purpose is for diagnosis of patients, but it is also critical for epilepsy research, development of antiepileptic therapies and communication around the world. The new classification originates from a draft document submitted for public comments in 2013 which was revised to incorporate extensive feedback from the international epilepsy community over several rounds of consultation. It presents three levels, starting with seizure type where it assumes that the patient is having epileptic seizures as defined by the new 2017 ILAE Seizure Classification. After diagnosis of the seizure type, the next step is diagnosis of epilepsy type, including focal epilepsy, generalized epilepsy, combined generalized and focal epilepsy, and also an unknown epilepsy group. The third level is that of epilepsy syndrome where a specific syndromic diagnosis can be made. The new classification incorporates etiology along each stage, emphasizing the need to consider etiology at each step of diagnosis as it often carries significant treatment implications. Etiology is broken into six subgroups, selected because of their potential therapeutic consequences. New terminology is introduced such as developmental and epileptic encephalopathy. The term benign is replaced by the terms self-limited and pharmacoresponsive, to be used where appropriate. It is hoped that this new framework will assist in improving epilepsy care and research in the 21st century. PMID:28276062

  15. Determinants of Severity in Acute Pancreatitis: A Nation-wide Multicenter Prospective Cohort Study.

    Science.gov (United States)

    Sternby, Hanna; Bolado, Federico; Canaval-Zuleta, Héctor J; Marra-López, Carlos; Hernando-Alonso, Ana I; Del-Val-Antoñana, Adolfo; García-Rayado, Guillermo; Rivera-Irigoin, Robin; Grau-García, Francisco J; Oms, Lluís; Millastre-Bocos, Judith; Pascual-Moreno, Isabel; Martínez-Ares, David; Rodríguez-Oballe, Juan A; López-Serrano, Antonio; Ruiz-Rebollo, María L; Viejo-Almanzor, Alejandro; González-de-la-Higuera, Belén; Orive-Calzada, Aitor; Gómez-Anta, Ignacio; Pamies-Guilabert, José; Fernández-Gutiérrez-Del-Álamo, Fátima; Iranzo-González-Cruz, Isabel; Pérez-Muñante, Mónica E; Esteba, María D; Pardillos-Tomé, Ana; Zapater, Pedro; de-Madaria, Enrique

    2018-04-18

    The aim of this study was to compare and validate the different classifications of severity in acute pancreatitis (AP) and to investigate which characteristics of the disease are associated with worse outcomes. AP is a heterogeneous disease, ranging from uneventful cases to patients with considerable morbidity and high mortality rates. Severity classifications based on legitimate determinants of severity are important to correctly describe the course of disease. A prospective multicenter cohort study involving patients with AP from 23 hospitals in Spain. The Atlanta Classification (AC), Revised Atlanta Classification (RAC), and Determinant-based Classification (DBC) were compared. Binary logistic multivariate analysis was performed to investigate independent determinants of severity. A total of 1655 patients were included; 70 patients (4.2%) died. RAC and DBC were equally superior to AC for describing the clinical course of AP. Although any kind of organ failure was associated with increased morbidity and mortality, persistent organ failure (POF) was the most significant determinant of severity. All local complications were associated with worse outcomes. Infected pancreatic necrosis correlated with high morbidity, but in the presence of POF, it was not associated to higher mortality when compared with sterile necrotizing pancreatitis. Exacerbation of previous comorbidity was associated with increased morbidity and mortality. The RAC and DBC both signify an advance in the description and differentiation of AP patients. Herein, we describe the complications of the disease independently associated to morbidity and mortality. Our findings are valuable not only when designing future studies on AP but also for the improvement of current classifications.

  16. Inter-Labeler and Intra-Labeler Variability of Condition Severity Classification Models Using Active and Passive Learning Methods

    Science.gov (United States)

    Nissim, Nir; Shahar, Yuval; Boland, Mary Regina; Tatonetti, Nicholas P; Elovici, Yuval; Hripcsak, George; Moskovitch, Robert

    2018-01-01

    Background and Objectives Labeling instances by domain experts for classification is often time consuming and expensive. To reduce such labeling efforts, we had proposed the application of active learning (AL) methods, introduced our CAESAR-ALE framework for classifying the severity of clinical conditions, and shown its significant reduction of labeling efforts. The use of any of three AL methods (one well known [SVM-Margin], and two that we introduced [Exploitation and Combination_XA]) significantly reduced (by 48% to 64%) condition labeling efforts, compared to standard passive (random instance-selection) SVM learning. Furthermore, our new AL methods achieved maximal accuracy using 12% fewer labeled cases than the SVM-Margin AL method. However, because labelers have varying levels of expertise, a major issue associated with learning methods, and AL methods in particular, is how to best to use the labeling provided by a committee of labelers. First, we wanted to know, based on the labelers’ learning curves, whether using AL methods (versus standard passive learning methods) has an effect on the Intra-labeler variability (within the learning curve of each labeler) and inter-labeler variability (among the learning curves of different labelers). Then, we wanted to examine the effect of learning (either passively or actively) from the labels created by the majority consensus of a group of labelers. Methods We used our CAESAR-ALE framework for classifying the severity of clinical conditions, the three AL methods and the passive learning method, as mentioned above, to induce the classifications models. We used a dataset of 516 clinical conditions and their severity labeling, represented by features aggregated from the medical records of 1.9 million patients treated at Columbia University Medical Center. We analyzed the variance of the classification performance within (intra-labeler), and especially among (inter-labeler) the classification models that were induced by

  17. Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods.

    Science.gov (United States)

    Nissim, Nir; Shahar, Yuval; Elovici, Yuval; Hripcsak, George; Moskovitch, Robert

    2017-09-01

    Labeling instances by domain experts for classification is often time consuming and expensive. To reduce such labeling efforts, we had proposed the application of active learning (AL) methods, introduced our CAESAR-ALE framework for classifying the severity of clinical conditions, and shown its significant reduction of labeling efforts. The use of any of three AL methods (one well known [SVM-Margin], and two that we introduced [Exploitation and Combination_XA]) significantly reduced (by 48% to 64%) condition labeling efforts, compared to standard passive (random instance-selection) SVM learning. Furthermore, our new AL methods achieved maximal accuracy using 12% fewer labeled cases than the SVM-Margin AL method. However, because labelers have varying levels of expertise, a major issue associated with learning methods, and AL methods in particular, is how to best to use the labeling provided by a committee of labelers. First, we wanted to know, based on the labelers' learning curves, whether using AL methods (versus standard passive learning methods) has an effect on the Intra-labeler variability (within the learning curve of each labeler) and inter-labeler variability (among the learning curves of different labelers). Then, we wanted to examine the effect of learning (either passively or actively) from the labels created by the majority consensus of a group of labelers. We used our CAESAR-ALE framework for classifying the severity of clinical conditions, the three AL methods and the passive learning method, as mentioned above, to induce the classifications models. We used a dataset of 516 clinical conditions and their severity labeling, represented by features aggregated from the medical records of 1.9 million patients treated at Columbia University Medical Center. We analyzed the variance of the classification performance within (intra-labeler), and especially among (inter-labeler) the classification models that were induced by using the labels provided by seven

  18. Classification of brain tumours using short echo time 1H MR spectra

    Science.gov (United States)

    Devos, A.; Lukas, L.; Suykens, J. A. K.; Vanhamme, L.; Tate, A. R.; Howe, F. A.; Majós, C.; Moreno-Torres, A.; van der Graaf, M.; Arús, C.; Van Huffel, S.

    2004-09-01

    The purpose was to objectively compare the application of several techniques and the use of several input features for brain tumour classification using Magnetic Resonance Spectroscopy (MRS). Short echo time 1H MRS signals from patients with glioblastomas ( n = 87), meningiomas ( n = 57), metastases ( n = 39), and astrocytomas grade II ( n = 22) were provided by six centres in the European Union funded INTERPRET project. Linear discriminant analysis, least squares support vector machines (LS-SVM) with a linear kernel and LS-SVM with radial basis function kernel were applied and evaluated over 100 stratified random splittings of the dataset into training and test sets. The area under the receiver operating characteristic curve (AUC) was used to measure the performance of binary classifiers, while the percentage of correct classifications was used to evaluate the multiclass classifiers. The influence of several factors on the classification performance has been tested: L2- vs. water normalization, magnitude vs. real spectra and baseline correction. The effect of input feature reduction was also investigated by using only the selected frequency regions containing the most discriminatory information, and peak integrated values. Using L2-normalized complete spectra the automated binary classifiers reached a mean test AUC of more than 0.95, except for glioblastomas vs. metastases. Similar results were obtained for all classification techniques and input features except for water normalized spectra, where classification performance was lower. This indicates that data acquisition and processing can be simplified for classification purposes, excluding the need for separate water signal acquisition, baseline correction or phasing.

  19. A Novel Imbalanced Data Classification Approach Based on Logistic Regression and Fisher Discriminant

    Directory of Open Access Journals (Sweden)

    Baofeng Shi

    2015-01-01

    Full Text Available We introduce an imbalanced data classification approach based on logistic regression significant discriminant and Fisher discriminant. First of all, a key indicators extraction model based on logistic regression significant discriminant and correlation analysis is derived to extract features for customer classification. Secondly, on the basis of the linear weighted utilizing Fisher discriminant, a customer scoring model is established. And then, a customer rating model where the customer number of all ratings follows normal distribution is constructed. The performance of the proposed model and the classical SVM classification method are evaluated in terms of their ability to correctly classify consumers as default customer or nondefault customer. Empirical results using the data of 2157 customers in financial engineering suggest that the proposed approach better performance than the SVM model in dealing with imbalanced data classification. Moreover, our approach contributes to locating the qualified customers for the banks and the bond investors.

  20. Evidence-based severity assessment: Impact of repeated versus single open-field testing on welfare in C57BL/6J mice.

    Science.gov (United States)

    Bodden, Carina; Siestrup, Sophie; Palme, Rupert; Kaiser, Sylvia; Sachser, Norbert; Richter, S Helene

    2018-01-15

    According to current guidelines on animal experiments, a prospective assessment of the severity of each procedure is mandatory. However, so far, the classification of procedures into different severity categories mainly relies on theoretic considerations, since it is not entirely clear which of the various procedures compromise the welfare of animals, or, to what extent. Against this background, a systematic empirical investigation of the impact of each procedure, including behavioral testing, seems essential. Therefore, the present study was designed to elucidate the effects of repeated versus single testing on mouse welfare, using one of the most commonly used paradigms for behavioral phenotyping in behavioral neuroscience, the open-field test. In an independent groups design, laboratory mice (Mus musculus f. domestica) experienced either repeated, single, or no open-field testing - procedures that are assigned to different severity categories. Interestingly, testing experiences did not affect fecal corticosterone metabolites, body weights, elevated plus-maze or home cage behavior differentially. Thus, with respect to the assessed endocrinological, physical, and behavioral outcome measures, no signs of compromised welfare could be detected in mice that were tested in the open-field repeatedly, once, or, not at all. These findings challenge current classification guidelines and may, furthermore, stimulate systematic research on the severity of single procedures involving living animals. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. Voice classification and vocal tract of singers: a study of x-ray images and morphology.

    Science.gov (United States)

    Roers, Friederike; Mürbe, Dirk; Sundberg, Johan

    2009-01-01

    This investigation compares vocal tract dimensions and the classification of singer voices by examining an x-ray material assembled between 1959 and 1991 of students admitted to the solo singing education at the University of Music, Dresden, Germany. A total of 132 images were available to analysis. Different classifications' values of the lengths of the total vocal tract, the pharynx, and mouth cavities as well as of the relative position of the larynx, the height of the palatal arch, and the estimated vocal fold length were analyzed statistically, and some significant differences were found. The length of the pharynx cavity seemed particularly influential on the total vocal tract length, which varied systematically with classification. Also studied were the relationships between voice classification and the body height and weight and the body mass index. The data support the hypothesis that there are consistent morphological vocal tract differences between singers of different voice classifications.

  2. Multilabel user classification using the community structure of online networks.

    Science.gov (United States)

    Rizos, Georgios; Papadopoulos, Symeon; Kompatsiaris, Yiannis

    2017-01-01

    We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE), an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user's graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score.

  3. Multilabel user classification using the community structure of online networks.

    Directory of Open Access Journals (Sweden)

    Georgios Rizos

    Full Text Available We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE, an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user's graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score.

  4. Blood profile of proteins and steroid hormones predicts weight change after weight loss with interactions of dietary protein level and glycemic index.

    Directory of Open Access Journals (Sweden)

    Ping Wang

    2011-02-01

    Full Text Available Weight regain after weight loss is common. In the Diogenes dietary intervention study, high protein and low glycemic index (GI diet improved weight maintenance.To identify blood predictors for weight change after weight loss following the dietary intervention within the Diogenes study.Blood samples were collected at baseline and after 8-week low caloric diet-induced weight loss from 48 women who continued to lose weight and 48 women who regained weight during subsequent 6-month dietary intervention period with 4 diets varying in protein and GI levels. Thirty-one proteins and 3 steroid hormones were measured.Angiotensin I converting enzyme (ACE was the most important predictor. Its greater reduction during the 8-week weight loss was related to continued weight loss during the subsequent 6 months, identified by both Logistic Regression and Random Forests analyses. The prediction power of ACE was influenced by immunoproteins, particularly fibrinogen. Leptin, luteinizing hormone and some immunoproteins showed interactions with dietary protein level, while interleukin 8 showed interaction with GI level on the prediction of weight maintenance. A predictor panel of 15 variables enabled an optimal classification by Random Forests with an error rate of 24±1%. A logistic regression model with independent variables from 9 blood analytes had a prediction accuracy of 92%.A selected panel of blood proteins/steroids can predict the weight change after weight loss. ACE may play an important role in weight maintenance. The interactions of blood factors with dietary components are important for personalized dietary advice after weight loss.ClinicalTrials.gov NCT00390637.

  5. Automatic liver volume segmentation and fibrosis classification

    Science.gov (United States)

    Bal, Evgeny; Klang, Eyal; Amitai, Michal; Greenspan, Hayit

    2018-02-01

    In this work, we present an automatic method for liver segmentation and fibrosis classification in liver computed-tomography (CT) portal phase scans. The input is a full abdomen CT scan with an unknown number of slices, and the output is a liver volume segmentation mask and a fibrosis grade. A multi-stage analysis scheme is applied to each scan, including: volume segmentation, texture features extraction and SVM based classification. Data contains portal phase CT examinations from 80 patients, taken with different scanners. Each examination has a matching Fibroscan grade. The dataset was subdivided into two groups: first group contains healthy cases and mild fibrosis, second group contains moderate fibrosis, severe fibrosis and cirrhosis. Using our automated algorithm, we achieved an average dice index of 0.93 ± 0.05 for segmentation and a sensitivity of 0.92 and specificity of 0.81for classification. To the best of our knowledge, this is a first end to end automatic framework for liver fibrosis classification; an approach that, once validated, can have a great potential value in the clinic.

  6. A comparison of growth and development of low birth weight and normal newborns at 5 years age

    Directory of Open Access Journals (Sweden)

    Eftekhar H

    1997-09-01

    Full Text Available The growth and developmental status of 252 children with low birth weight (<2500 gr born from 1988 to 1989 as cases were compared with 312 children with normal birth weight (>2500 gr at the fifth birthday. The results of comparing these two groups of children show that: 1 In relation to weight for age of survivors, with increasing of age, weight gaining is higher in the control group in comparison with the case group (P=0.00. 2 In relation to height for age the study revealed, that this indicator differs in two groups but the difference is not significant. 3 In regard to Gomez classification: The percentages of all grades of malnutrition (mild, moderate, severe is grater in the case group than controls. 4 By considering the developmental criteria (skipping, drawing triangle from copy, naming 5 colors, repeating sentences of 10 syllables, counting of 10 coins correctly and respectively: The study showed that developmental indicator of the case group differs from controls. The test statistic "t" showed, there is a significant difference between two variables (P=0.005. 5 By using the Riven test for evaluation of IQ, our findings characterized that, the IQ mean of the LBW and NBW are not truely different.

  7. DTI measurements for Alzheimer’s classification

    Science.gov (United States)

    Maggipinto, Tommaso; Bellotti, Roberto; Amoroso, Nicola; Diacono, Domenico; Donvito, Giacinto; Lella, Eufemia; Monaco, Alfonso; Antonella Scelsi, Marzia; Tangaro, Sabina; Disease Neuroimaging Initiative, Alzheimer's.

    2017-03-01

    Diffusion tensor imaging (DTI) is a promising imaging technique that provides insight into white matter microstructure integrity and it has greatly helped identifying white matter regions affected by Alzheimer’s disease (AD) in its early stages. DTI can therefore be a valuable source of information when designing machine-learning strategies to discriminate between healthy control (HC) subjects, AD patients and subjects with mild cognitive impairment (MCI). Nonetheless, several studies have reported so far conflicting results, especially because of the adoption of biased feature selection strategies. In this paper we firstly analyzed DTI scans of 150 subjects from the Alzheimer’s disease neuroimaging initiative (ADNI) database. We measured a significant effect of the feature selection bias on the classification performance (p-value  informative content provided by DTI measurements for AD classification. Classification performances and biological insight, concerning brain regions related to the disease, provided by cross-validation analysis were both confirmed on the independent test.

  8. SAW Classification Algorithm for Chinese Text Classification

    OpenAIRE

    Xiaoli Guo; Huiyu Sun; Tiehua Zhou; Ling Wang; Zhaoyang Qu; Jiannan Zang

    2015-01-01

    Considering the explosive growth of data, the increased amount of text data’s effect on the performance of text categorization forward the need for higher requirements, such that the existing classification method cannot be satisfied. Based on the study of existing text classification technology and semantics, this paper puts forward a kind of Chinese text classification oriented SAW (Structural Auxiliary Word) algorithm. The algorithm uses the special space effect of Chinese text where words...

  9. Metabolically healthy obesity from childhood to adulthood - Does weight status alone matter?

    Science.gov (United States)

    Blüher, Susann; Schwarz, Peter

    2014-09-01

    Up to 30% of obese people do not display the "typical" metabolic obesity-associated complications. For this group of patients, the term "metabolically healthy obese (MHO)" has been established during the past years and has been the focus of research activities. The development and severity of insulin resistance as well as (subclinical) inflammations seems to play a key role in distinguishing metabolically healthy from metabolically non-healthy individuals. However, an internationally consistent and accepted classification that might also include inflammatory markers as well as features of non-alcoholic fatty liver disease is missing to date, and available data - in terms of prevalence, definition and severity - are heterogeneous, both during childhood/adolescence and during adulthood. In addition, the impact of MHO on future morbidity and mortality compared to obese, metabolically non-healthy as well as normal weight, metabolically healthy individuals is absolutely not clear to date and even conflicting. This review summarizes salient literature related to that topic and provides insight into our current understanding of MHO, covering all age spans from childhood to adulthood. Copyright © 2014 Elsevier Inc. All rights reserved.

  10. Classification in context

    DEFF Research Database (Denmark)

    Mai, Jens Erik

    2004-01-01

    This paper surveys classification research literature, discusses various classification theories, and shows that the focus has traditionally been on establishing a scientific foundation for classification research. This paper argues that a shift has taken place, and suggests that contemporary...... classification research focus on contextual information as the guide for the design and construction of classification schemes....

  11. A comparative evaluation of sequence classification programs

    Directory of Open Access Journals (Sweden)

    Bazinet Adam L

    2012-05-01

    Full Text Available Abstract Background A fundamental problem in modern genomics is to taxonomically or functionally classify DNA sequence fragments derived from environmental sampling (i.e., metagenomics. Several different methods have been proposed for doing this effectively and efficiently, and many have been implemented in software. In addition to varying their basic algorithmic approach to classification, some methods screen sequence reads for ’barcoding genes’ like 16S rRNA, or various types of protein-coding genes. Due to the sheer number and complexity of methods, it can be difficult for a researcher to choose one that is well-suited for a particular analysis. Results We divided the very large number of programs that have been released in recent years for solving the sequence classification problem into three main categories based on the general algorithm they use to compare a query sequence against a database of sequences. We also evaluated the performance of the leading programs in each category on data sets whose taxonomic and functional composition is known. Conclusions We found significant variability in classification accuracy, precision, and resource consumption of sequence classification programs when used to analyze various metagenomics data sets. However, we observe some general trends and patterns that will be useful to researchers who use sequence classification programs.

  12. Sleep in critically ill, mechanically ventilated patients with severe sepsis or COPD

    DEFF Research Database (Denmark)

    Boyko, Y; Jennum, P; Oerding, H

    2018-01-01

    BACKGROUND: The standard method for scoring polysomnographic (PSG) sleep is insufficient in the intensive care unit (ICU). A modified classification has been proposed, but has not been tested in specific groups of ICU patients. We aimed firstly to (1) use the modified classification to describe...... with severe sepsis or COPD completed up to 20-hours PSG recording in each patient. A modified classification for scoring sleep in ICU was used for scoring the PSGs. Sleep assessment by nurses was done at 15 minutes intervals. RESULTS: We included 16 patients with severe sepsis and 17 patients with COPD. Half...

  13. Bariatric Surgery Promising in Migraine Control: a Controlled Trial on Weight Loss and Its Effect on Migraine Headache.

    Science.gov (United States)

    Razeghi Jahromi, Soodeh; Abolhasani, Maryam; Ghorbani, Zeinab; Sadre-Jahani, Solmaz; Alizadeh, Zahra; Talebpour, Mohammad; Meysamie, Alipasha; Togha, Mansoureh

    2018-01-01

    There is evidence that substantial weight loss through bariatric surgery (BS) may result in short-term improvement of migraine severity. However, it still remains to be seen whether smaller amounts of weight loss have a similar effect on migraine headache. This study has been designed to compare the effects of weight reduction through BS and non-surgical modifications. Migraine characteristics were assessed at 1 month before (T0), 1 month (T1), and 6 months (T2) after BS (vertical sleeve gastrectomy (VSG) (n = 25) or behavioral therapy (BT) (n = 26) in obese women (aged 18-60 years) with migraine headache. Migraine was diagnosed using the International Classification of Headache Disorders (ICHDIIβ) criteria. There was significant reduction in the visual analog scale (VAS) from the baseline to T1 and T2 in both groups. The number of migraine-free days showed a significant increase within each group (p migraine characteristics, age, changes in weight, BMI, body fat, and fat-free mass from T0 to T2, the BS group showed statistically significant lower VAS and duration of migraine attacks and a significantly higher number of migraine-free days than the BT group at T1 and T2 (p ≤ 0.028). Our results indicated that far before significant weight reduction after BS (VSG), there was marked alleviation in the severity and duration of migraine and a significant increase in the number of migraine-free days in obese female migraineurs. However, the effects in the BT group were not comparable with the effects in the BS group.

  14. Classification

    DEFF Research Database (Denmark)

    Hjørland, Birger

    2017-01-01

    This article presents and discusses definitions of the term “classification” and the related concepts “Concept/conceptualization,”“categorization,” “ordering,” “taxonomy” and “typology.” It further presents and discusses theories of classification including the influences of Aristotle...... and Wittgenstein. It presents different views on forming classes, including logical division, numerical taxonomy, historical classification, hermeneutical and pragmatic/critical views. Finally, issues related to artificial versus natural classification and taxonomic monism versus taxonomic pluralism are briefly...

  15. Monitoring severe accidents using AI techniques

    Energy Technology Data Exchange (ETDEWEB)

    No, Young Gyu; Ahn, Kwang Il [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Kim, Ju Hyun; Na, Man Gyun [Dept. of Nuclear Engineering, Chosun University, Gwangju (Korea, Republic of); Lim, Dong Hyuk [Korea Institute of Nuclear Nonproliferation and Control, Daejon (Korea, Republic of)

    2012-05-15

    After the Fukushima nuclear accident in 2011, there has been increasing concern regarding severe accidents in nuclear facilities. Severe accident scenarios are difficult for operators to monitor and identify. Therefore, accurate prediction of a severe accident is important in order to manage it appropriately in the unfavorable conditions. In this study, artificial intelligence (AI) techniques, such as support vector classification (SVC), probabilistic neural network (PNN), group method of data handling (GMDH), and fuzzy neural network (FNN), were used to monitor the major transient scenarios of a severe accident caused by three different initiating events, the hot-leg loss of coolant accident (LOCA), the cold-leg LOCA, and the steam generator tube rupture in pressurized water reactors (PWRs). The SVC and PNN models were used for the event classification. The GMDH and FNN models were employed to accurately predict the important timing representing severe accident scenarios. In addition, in order to verify the proposed algorithm, data from a number of numerical simulations were required in order to train the AI techniques due to the shortage of real LOCA data. The data was acquired by performing simulations using the MAAP4 code. The prediction accuracy of the three types of initiating events was sufficiently high to predict severe accident scenarios. Therefore, the AI techniques can be applied successfully in the identification and monitoring of severe accident scenarios in real PWRs.

  16. Monitoring severe accidents using AI techniques

    International Nuclear Information System (INIS)

    No, Young Gyu; Ahn, Kwang Il; Kim, Ju Hyun; Na, Man Gyun; Lim, Dong Hyuk

    2012-01-01

    After the Fukushima nuclear accident in 2011, there has been increasing concern regarding severe accidents in nuclear facilities. Severe accident scenarios are difficult for operators to monitor and identify. Therefore, accurate prediction of a severe accident is important in order to manage it appropriately in the unfavorable conditions. In this study, artificial intelligence (AI) techniques, such as support vector classification (SVC), probabilistic neural network (PNN), group method of data handling (GMDH), and fuzzy neural network (FNN), were used to monitor the major transient scenarios of a severe accident caused by three different initiating events, the hot-leg loss of coolant accident (LOCA), the cold-leg LOCA, and the steam generator tube rupture in pressurized water reactors (PWRs). The SVC and PNN models were used for the event classification. The GMDH and FNN models were employed to accurately predict the important timing representing severe accident scenarios. In addition, in order to verify the proposed algorithm, data from a number of numerical simulations were required in order to train the AI techniques due to the shortage of real LOCA data. The data was acquired by performing simulations using the MAAP4 code. The prediction accuracy of the three types of initiating events was sufficiently high to predict severe accident scenarios. Therefore, the AI techniques can be applied successfully in the identification and monitoring of severe accident scenarios in real PWRs.

  17. Neuromuscular disease classification system

    Science.gov (United States)

    Sáez, Aurora; Acha, Begoña; Montero-Sánchez, Adoración; Rivas, Eloy; Escudero, Luis M.; Serrano, Carmen

    2013-06-01

    Diagnosis of neuromuscular diseases is based on subjective visual assessment of biopsies from patients by the pathologist specialist. A system for objective analysis and classification of muscular dystrophies and neurogenic atrophies through muscle biopsy images of fluorescence microscopy is presented. The procedure starts with an accurate segmentation of the muscle fibers using mathematical morphology and a watershed transform. A feature extraction step is carried out in two parts: 24 features that pathologists take into account to diagnose the diseases and 58 structural features that the human eye cannot see, based on the assumption that the biopsy is considered as a graph, where the nodes are represented by each fiber, and two nodes are connected if two fibers are adjacent. A feature selection using sequential forward selection and sequential backward selection methods, a classification using a Fuzzy ARTMAP neural network, and a study of grading the severity are performed on these two sets of features. A database consisting of 91 images was used: 71 images for the training step and 20 as the test. A classification error of 0% was obtained. It is concluded that the addition of features undetectable by the human visual inspection improves the categorization of atrophic patterns.

  18. Principles of classification of NIS reserves ampersand resources

    International Nuclear Information System (INIS)

    Anon.

    1993-01-01

    The newly independent states of the former Soviet Union (NIS) control large resources of uranium. These resources have been the subject of substantial debate within several forums of the Western uranium industry because of confusion surrounding the degree of correlation between the Soviet and various Western classification systems. As illustrated in this article, although developed independently of Western systems, the classification system used by the NIS is very definitive and provides a sound basis for resource reporting. In 1981 a new open-quotes System of Classification of Reserves and Resources of Mineral Depositsclose quotes was adopted. This classification system, which is still used today in the newly independent states of the former Soviet Union (NIS), subdivides mineral deposits into seven categories in three major groups, based on the level of exploration performed: (1) Explored Reserves (A, B, C 1 ); (2) Evaluated Reserves (C 2 ); and (3) Predicted Resources (P 1 , P 2 , P 3 )

  19. New Myositis Classification Criteria-What We Have Learned Since Bohan and Peter.

    Science.gov (United States)

    Leclair, Valérie; Lundberg, Ingrid E

    2018-03-17

    Idiopathic inflammatory myopathy (IIM) classification criteria have been a subject of debate for many decades. Despite several limitations, the Bohan and Peter criteria are still widely used. The aim of this review is to discuss the evolution of IIM classification criteria. New IIM classification criteria are periodically proposed. The discovery of myositis-specific and myositis-associated autoantibodies led to the development of clinico-serological criteria, while in-depth description of IIM morphological features improved histopathology-based criteria. The long-awaited European League Against Rheumatism and American College of Rheumatology (EULAR/ACR) IIM classification criteria were recently published. The Bohan and Peter criteria are outdated and validated classification criteria are necessary to improve research in IIM. The new EULAR/ACR IIM classification criteria are thus a definite improvement and an important step forward in the field.

  20. Classification of different degrees of adiposity in sedentary rats

    Energy Technology Data Exchange (ETDEWEB)

    Leopoldo, A.S.; Lima-Leopoldo, A.P. [Departamento de Desportos, Centro de Educação Física e Esportes, Universidade Federal do Espírito Santo, Vitória, ES (Brazil); Nascimento, A.F.; Luvizotto, R.A.M.; Sugizaki, M.M. [Instituto de Ciências da Saúde, Universidade Federal do Mato Grosso, Sinop, MT (Brazil); Campos, D.H.S.; Silva, D.C.T. da [Departamento de Clínica Médica, Faculdade de Medicina, Universidade Estadual Paulista, Botucatu, SP (Brazil); Padovani, C.R. [Departamento de Bioestatística, Instituto de Biociências, Universidade Estadual Paulista, Botucatu, SP (Brazil); Cicogna, A.C. [Departamento de Clínica Médica, Faculdade de Medicina, Universidade Estadual Paulista, Botucatu, SP (Brazil)

    2016-02-23

    In experimental studies, several parameters, such as body weight, body mass index, adiposity index, and dual-energy X-ray absorptiometry, have commonly been used to demonstrate increased adiposity and investigate the mechanisms underlying obesity and sedentary lifestyles. However, these investigations have not classified the degree of adiposity nor defined adiposity categories for rats, such as normal, overweight, and obese. The aim of the study was to characterize the degree of adiposity in rats fed a high-fat diet using cluster analysis and to create adiposity intervals in an experimental model of obesity. Thirty-day-old male Wistar rats were fed a normal (n=41) or a high-fat (n=43) diet for 15 weeks. Obesity was defined based on the adiposity index; and the degree of adiposity was evaluated using cluster analysis. Cluster analysis allowed the rats to be classified into two groups (overweight and obese). The obese group displayed significantly higher total body fat and a higher adiposity index compared with those of the overweight group. No differences in systolic blood pressure or nonesterified fatty acid, glucose, total cholesterol, or triglyceride levels were observed between the obese and overweight groups. The adiposity index of the obese group was positively correlated with final body weight, total body fat, and leptin levels. Despite the classification of sedentary rats into overweight and obese groups, it was not possible to identify differences in the comorbidities between the two groups.

  1. Classification of different degrees of adiposity in sedentary rats

    International Nuclear Information System (INIS)

    Leopoldo, A.S.; Lima-Leopoldo, A.P.; Nascimento, A.F.; Luvizotto, R.A.M.; Sugizaki, M.M.; Campos, D.H.S.; Silva, D.C.T. da; Padovani, C.R.; Cicogna, A.C.

    2016-01-01

    In experimental studies, several parameters, such as body weight, body mass index, adiposity index, and dual-energy X-ray absorptiometry, have commonly been used to demonstrate increased adiposity and investigate the mechanisms underlying obesity and sedentary lifestyles. However, these investigations have not classified the degree of adiposity nor defined adiposity categories for rats, such as normal, overweight, and obese. The aim of the study was to characterize the degree of adiposity in rats fed a high-fat diet using cluster analysis and to create adiposity intervals in an experimental model of obesity. Thirty-day-old male Wistar rats were fed a normal (n=41) or a high-fat (n=43) diet for 15 weeks. Obesity was defined based on the adiposity index; and the degree of adiposity was evaluated using cluster analysis. Cluster analysis allowed the rats to be classified into two groups (overweight and obese). The obese group displayed significantly higher total body fat and a higher adiposity index compared with those of the overweight group. No differences in systolic blood pressure or nonesterified fatty acid, glucose, total cholesterol, or triglyceride levels were observed between the obese and overweight groups. The adiposity index of the obese group was positively correlated with final body weight, total body fat, and leptin levels. Despite the classification of sedentary rats into overweight and obese groups, it was not possible to identify differences in the comorbidities between the two groups

  2. An enhanced topologically significant directed random walk in cancer classification using gene expression datasets

    Directory of Open Access Journals (Sweden)

    Choon Sen Seah

    2017-12-01

    Full Text Available Microarray technology has become one of the elementary tools for researchers to study the genome of organisms. As the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analysis, cancerous classification is an emerging important trend. Significant directed random walk is proposed as one of the cancerous classification approach which have higher sensitivity of risk gene prediction and higher accuracy of cancer classification. In this paper, the methodology and material used for the experiment are presented. Tuning parameter selection method and weight as parameter are applied in proposed approach. Gene expression dataset is used as the input datasets while pathway dataset is used to build a directed graph, as reference datasets, to complete the bias process in random walk approach. In addition, we demonstrate that our approach can improve sensitive predictions with higher accuracy and biological meaningful classification result. Comparison result takes place between significant directed random walk and directed random walk to show the improvement in term of sensitivity of prediction and accuracy of cancer classification.

  3. Fuzzy set classifier for waste classification tracking

    International Nuclear Information System (INIS)

    Gavel, D.T.

    1992-01-01

    We have developed an expert system based on fuzzy logic theory to fuse the data from multiple sensors and make classification decisions for objects in a waste reprocessing stream. Fuzzy set theory has been applied in decision and control applications with some success, particularly by the Japanese. We have found that the fuzzy logic system is rather easy to design and train, a feature that can cut development costs considerably. With proper training, the classification accuracy is quite high. We performed several tests sorting radioactive test samples using a gamma spectrometer to compare fuzzy logic to more conventional sorting schemes

  4. Automatic Segmentation of Dermoscopic Images by Iterative Classification

    Directory of Open Access Journals (Sweden)

    Maciel Zortea

    2011-01-01

    Full Text Available Accurate detection of the borders of skin lesions is a vital first step for computer aided diagnostic systems. This paper presents a novel automatic approach to segmentation of skin lesions that is particularly suitable for analysis of dermoscopic images. Assumptions about the image acquisition, in particular, the approximate location and color, are used to derive an automatic rule to select small seed regions, likely to correspond to samples of skin and the lesion of interest. The seed regions are used as initial training samples, and the lesion segmentation problem is treated as binary classification problem. An iterative hybrid classification strategy, based on a weighted combination of estimated posteriors of a linear and quadratic classifier, is used to update both the automatically selected training samples and the segmentation, increasing reliability and final accuracy, especially for those challenging images, where the contrast between the background skin and lesion is low.

  5. 78 FR 54970 - Cotton Futures Classification: Optional Classification Procedure

    Science.gov (United States)

    2013-09-09

    ... Service 7 CFR Part 27 [AMS-CN-13-0043] RIN 0581-AD33 Cotton Futures Classification: Optional Classification Procedure AGENCY: Agricultural Marketing Service, USDA. ACTION: Proposed rule. SUMMARY: The... optional cotton futures classification procedure--identified and known as ``registration'' by the U.S...

  6. Examining Spatial Variation in the Effects of Japanese Red Pine (Pinus densiflora on Burn Severity Using Geographically Weighted Regression

    Directory of Open Access Journals (Sweden)

    Hyun-Joo Lee

    2017-05-01

    Full Text Available Burn severity has profound impacts on the response of post-fire forest ecosystems to fire events. Numerous previous studies have reported that burn severity is determined by variables such as meteorological conditions, pre-fire forest structure, and fuel characteristics. An underlying assumption of these studies was the constant effects of environmental variables on burn severity over space, and these analyses therefore did not consider the spatial dimension. This study examined spatial variation in the effects of Japanese red pine (Pinus densiflora on burn severity. Specifically, this study investigated the presence of spatially varying relationships between Japanese red pine and burn severity due to changes in slope and elevation. We estimated conventional ordinary least squares (OLS and geographically weighted regression (GWR models and compared them using three criteria; the coefficients of determination (R2, Akaike information criterion for small samples (AICc, and Moran’s I-value. The GWR model performed considerably better than the OLS model in explaining variation in burn severity. The results provided strong evidence that the effect of Japanese red pine on burn severity was not constant but varied spatially. Elevation was a significant factor in the variation in the effects of Japanese red pine on burn severity. The influence of red pine on burn severity was considerably higher in low-elevation areas but became less important than the other variables in high-elevation areas. The results of this study can be applied to location-specific strategies for forest managers and can be adopted to improve fire simulation models to more realistically mimic the nature of fire behavior.

  7. Classification of diffuse lung diseases: why and how.

    Science.gov (United States)

    Hansell, David M

    2013-09-01

    The understanding of complex lung diseases, notably the idiopathic interstitial pneumonias and small airways diseases, owes as much to repeated attempts over the years to classify them as to any single conceptual breakthrough. One of the many benefits of a successful classification scheme is that it allows workers, within and between disciplines, to be clear that they are discussing the same disease. This may be of particular importance in the recruitment of individuals for a clinical trial that requires a standardized and homogeneous study population. Different specialties require fundamentally different things from a classification: for epidemiologic studies, a classification that requires categorization of individuals according to histopathologic pattern is not usually practicable. Conversely, a scheme that simply divides diffuse parenchymal disease into inflammatory and noninflammatory categories is unlikely to further the understanding about the pathogenesis of disease. Thus, for some disease groupings, for example, pulmonary vasculopathies, there may be several appropriate classifications, each with its merits and demerits. There has been an interesting shift in the past few years, from the accepted primacy of histopathology as the sole basis on which the classification of parenchymal lung disease has rested, to new ways of considering how these entities relate to each other. Some inventive thinking has resulted in new classifications that undoubtedly benefit patients and clinicians in their endeavor to improve management and outcome. The challenge of understanding the logic behind current classifications and their shortcomings are explored in various examples of lung diseases.

  8. Changing patient classification system for hospital reimbursement in Romania.

    Science.gov (United States)

    Radu, Ciprian-Paul; Chiriac, Delia Nona; Vladescu, Cristian

    2010-06-01

    To evaluate the effects of the change in the diagnosis-related group (DRG) system on patient morbidity and hospital financial performance in the Romanian public health care system. Three variables were assessed before and after the classification switch in July 2007: clinical outcomes, the case mix index, and hospital budgets, using the database of the National School of Public Health and Health Services Management, which contains data regularly received from hospitals reimbursed through the Romanian DRG scheme (291 in 2009). The lack of a Romanian system for the calculation of cost-weights imposed the necessity to use an imported system, which was criticized by some clinicians for not accurately reflecting resource consumption in Romanian hospitals. The new DRG classification system allowed a more accurate clinical classification. However, it also exposed a lack of physicians' knowledge on diagnosing and coding procedures, which led to incorrect coding. Consequently, the reported hospital morbidity changed after the DRG switch, reflecting an increase in the national case-mix index of 25% in 2009 (compared with 2007). Since hospitals received the same reimbursement over the first two years after the classification switch, the new DRG system led them sometimes to change patients' diagnoses in order to receive more funding. Lack of oversight of hospital coding and reporting to the national reimbursement scheme allowed the increase in the case-mix index. The complexity of the new classification system requires more resources (human and financial), better monitoring and evaluation, and improved legislation in order to achieve better hospital resource allocation and more efficient patient care.

  9. A simplified immunohistochemical classification of skeletal muscle fibres in mouse

    Directory of Open Access Journals (Sweden)

    M. Kammoun

    2014-06-01

    Full Text Available The classification of muscle fibres is of particular interest for the study of the skeletal muscle properties in a wide range of scientific fields, especially animal phenotyping. It is therefore important to define a reliable method for classifying fibre types. The aim of this study was to establish a simplified method for the immunohistochemical classification of fibres in mouse. To carry it out, we first tested a combination of several anti myosin heavy chain (MyHC antibodies in order to choose a minimum number of antibodies to implement a semi-automatic classification. Then, we compared the classification of fibres to the MyHC electrophoretic pattern on the same samples. Only two anti MyHC antibodies on serial sections with the fluorescent labeling of the Laminin were necessary to classify properly fibre types in Tibialis Anterior and Soleus mouse muscles in normal physiological conditions. This classification was virtually identical to the classification realized by the electrophoretic separation of MyHC. This immunohistochemical classification can be applied to the total area of Tibialis Anterior and Soleus mouse muscles. Thus, we provide here a useful, simple and time-efficient method for immunohistochemical classification of fibres, applicable for research in mouse

  10. PROGRESSIVE DENSIFICATION AND REGION GROWING METHODS FOR LIDAR DATA CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    J. L. Pérez-García

    2012-07-01

    Full Text Available At present, airborne laser scanner systems are one of the most frequent methods used to obtain digital terrain elevation models. While having the advantage of direct measurement on the object, the point cloud obtained has the need for classification of their points according to its belonging to the ground. This need for classification of raw data has led to appearance of multiple filters focused LiDAR classification information. According this approach, this paper presents a classification method that combines LiDAR data segmentation techniques and progressive densification to carry out the location of the points belonging to the ground. The proposed methodology is tested on several datasets with different terrain characteristics and data availability. In all case, we analyze the advantages and disadvantages that have been obtained compared with the individual techniques application and, in a special way, the benefits derived from the integration of both classification techniques. In order to provide a more comprehensive quality control of the classification process, the obtained results have been compared with the derived from a manual procedure, which is used as reference classification. The results are also compared with other automatic classification methodologies included in some commercial software packages, highly contrasted by users for LiDAR data treatment.

  11. Classifications of objects on hyperspectral images

    DEFF Research Database (Denmark)

    Kucheryavskiy, Sergey

    . In the present work a classification method that combines classic image classification approach and MIA is proposed. The basic idea is to group all pixels and calculate spectral properties of the pixel group to be used further as a vector of predictors for calibration and class prediction. The grouping can...... be done with mathematical morphology methods applied to a score image where objects are well separated. In the case of small overlapping a watershed transformation can be applied to disjoint the objects. The method has been tested on several simulated and real cases and showed good results and significant...... improvements in comparison with a standard MIA approach. The results as well as method details will be reported....

  12. Optimizing Multiple Kernel Learning for the Classification of UAV Data

    Directory of Open Access Journals (Sweden)

    Caroline M. Gevaert

    2016-12-01

    Full Text Available Unmanned Aerial Vehicles (UAVs are capable of providing high-quality orthoimagery and 3D information in the form of point clouds at a relatively low cost. Their increasing popularity stresses the necessity of understanding which algorithms are especially suited for processing the data obtained from UAVs. The features that are extracted from the point cloud and imagery have different statistical characteristics and can be considered as heterogeneous, which motivates the use of Multiple Kernel Learning (MKL for classification problems. In this paper, we illustrate the utility of applying MKL for the classification of heterogeneous features obtained from UAV data through a case study of an informal settlement in Kigali, Rwanda. Results indicate that MKL can achieve a classification accuracy of 90.6%, a 5.2% increase over a standard single-kernel Support Vector Machine (SVM. A comparison of seven MKL methods indicates that linearly-weighted kernel combinations based on simple heuristics are competitive with respect to computationally-complex, non-linear kernel combination methods. We further underline the importance of utilizing appropriate feature grouping strategies for MKL, which has not been directly addressed in the literature, and we propose a novel, automated feature grouping method that achieves a high classification accuracy for various MKL methods.

  13. Is Weight Training Safe during Pregnancy?

    Science.gov (United States)

    Work, Janis A.

    1989-01-01

    Examines the opinions of several experts on the safety of weight training during pregnancy, noting that no definitive research on weight training alone has been done. Experts agree that low-intensity weight training probably poses no harm for mother or fetus; exercise programs should be individualized. (SM)

  14. Predicting sample size required for classification performance

    Directory of Open Access Journals (Sweden)

    Figueroa Rosa L

    2012-02-01

    Full Text Available Abstract Background Supervised learning methods need annotated data in order to generate efficient models. Annotated data, however, is a relatively scarce resource and can be expensive to obtain. For both passive and active learning methods, there is a need to estimate the size of the annotated sample required to reach a performance target. Methods We designed and implemented a method that fits an inverse power law model to points of a given learning curve created using a small annotated training set. Fitting is carried out using nonlinear weighted least squares optimization. The fitted model is then used to predict the classifier's performance and confidence interval for larger sample sizes. For evaluation, the nonlinear weighted curve fitting method was applied to a set of learning curves generated using clinical text and waveform classification tasks with active and passive sampling methods, and predictions were validated using standard goodness of fit measures. As control we used an un-weighted fitting method. Results A total of 568 models were fitted and the model predictions were compared with the observed performances. Depending on the data set and sampling method, it took between 80 to 560 annotated samples to achieve mean average and root mean squared error below 0.01. Results also show that our weighted fitting method outperformed the baseline un-weighted method (p Conclusions This paper describes a simple and effective sample size prediction algorithm that conducts weighted fitting of learning curves. The algorithm outperformed an un-weighted algorithm described in previous literature. It can help researchers determine annotation sample size for supervised machine learning.

  15. The definition and natural history of severe exacerbation of hepatitis B

    Directory of Open Access Journals (Sweden)

    GUO Wei

    2014-10-01

    Full Text Available Despite different opinions on its definition and classification in the past, a consensus has gradually been reached regarding the naming, classification, and clinical diagnosis of liver failure. The classification of liver failure is described, and the definition and natural history of severe exacerbation of hepatitis B are summarized. Antiviral treatment and artificial liver support in the early stage are beneficial for clinical outcomes and prognosis.

  16. Inflammatory status is different in relationship to insulin resistance in severely obese people and changes after bariatric surgery or diet-induced weight loss.

    Science.gov (United States)

    Ballesteros-Pomar, M D; Calleja, S; Díez-Rodríguez, R; Calleja-Fernández, A; Vidal-Casariego, A; Nuñez-Alonso, A; Cano-Rodríguez, I; Olcoz-Goñi, J L

    2014-11-01

    To assess if insulin resistance is related to a different inflammatory status (especially lymphocyte subpopulations) in severely obese people and to evaluate changes after weight loss either following a very-low calorie diet (VLCD) or bariatric surgery. Severely obese patients were consecutively recruited in our Obesity Unit. Blood lymphocyte subpopulations and inflammatory parameters were measured baseline, after a VLCD during 6 weeks and one year after biliopancreatic diversion. Insulin resistance was evaluated by Homeostasis Model Assessment (HOMA) index. After excluding diabetic patients, 58 patients were studied. HOMA index classified 63.8% of them as insulin resistant (IR). Serum baseline levels of inflammatory cytokines were not significantly different between IR and insulinsensitive (IS) patients but, regarding lymphocyte subpopulations, Natural Killer (NK) cells were higher in IR patients [(305.0 (136.7) vs. 235.0 (80.7) cells/µL, p=0.047]. NK cells showed a significant positive correlation with HOMA index (r=0.484, p=0.000) and with the carbohydrate content of the diet (r=0.420, p=0.001). After VLCD, NK cells significantly decreased, but only in IR patients and in those losing more than 10% of their initial weight. After biliopancreatic diversion, total and CD8 T Lymphocytes, B lymphocytes and NK cells also decreased but only in IR individuals. NK cells are significantly increased in IR severely obese people in respect to IS, suggesting a slightly different immune status in these patients with a probable dietary relationship. Weight loss could reverse this increase either after VLCD or after bariatric surgery. © J. A. Barth Verlag in Georg Thieme Verlag KG Stuttgart · New York.

  17. Attitudes to body weight, weight gain and eating behavior in pregnancy.

    Science.gov (United States)

    Abraham, S; King, W; Llewellyn-Jones, D

    1994-12-01

    The eating behavior and attitudes to body weight of 100 healthy women were studied 3 days after the birth of their first child. During pregnancy women 'watch their weight' and use a range of methods of weight control which include cigarette smoking and inducing vomiting. During pregnancy 41 women reported weight control problems and 20 women considered their weight and eating problems to be greater than at any previous time. Picking was the most common unwanted behavior. Binge eating was experienced by 44 women, nine of whom reported it to be a 'severe' problem. Although women were ambivalent about being weighed at each antenatal visit, 81 recommended weighing once each month. The women held differing opinions on the effects of breastfeeding on body weight and on the need for nutritional supplements during pregnancy. Women reporting 'disordered eating' were more likely to have antenatal complications and give birth to low birthweight babies. The results suggest good obstetric care should include a history of the woman's eating behavior and body weight.

  18. Classification of refrigerants; Classification des fluides frigorigenes

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2001-07-01

    This document was made from the US standard ANSI/ASHRAE 34 published in 2001 and entitled 'designation and safety classification of refrigerants'. This classification allows to clearly organize in an international way the overall refrigerants used in the world thanks to a codification of the refrigerants in correspondence with their chemical composition. This note explains this codification: prefix, suffixes (hydrocarbons and derived fluids, azeotropic and non-azeotropic mixtures, various organic compounds, non-organic compounds), safety classification (toxicity, flammability, case of mixtures). (J.S.)

  19. Sound classification of dwellings in the Nordic countries – Differences and similarities between the five national schemes

    DEFF Research Database (Denmark)

    Rasmussen, Birgit

    2012-01-01

    having several similarities. In 2012, status is that number and denotations of classes for dwellings are identical in the Nordic countries, but the structures of the standards and several details are quite different. Also the issues dealt with are different. Examples of differences are sound insulation...... for classification of such buildings. This paper presents and compares the main class criteria for sound insulation of dwellings and summarizes differences and similarities in criteria and in structures of standards. Classification schemes for dwellings also exist in several other countries in Europe......In all five Nordic countries, sound classification schemes for dwellings have been published in national standards being implemented and revised gradually since the late 1990s. The national classification criteria for dwellings originate from a common Nordic INSTA-B proposal from the 1990s, thus...

  20. Investigating the use of support vector machine classification on structural brain images of preterm-born teenagers as a biological marker.

    Directory of Open Access Journals (Sweden)

    Carlton Chu

    Full Text Available Preterm birth has been shown to induce an altered developmental trajectory of brain structure and function. With the aid support vector machine (SVM classification methods we aimed to investigate whether MRI data, collected in adolescence, could be used to predict whether an individual had been born preterm or at term. To this end we collected T1-weighted anatomical MRI data from 143 individuals (69 controls, mean age 14.6y. The inclusion criteria for those born preterm were birth weight ≤ 1500g and gestational age < 37w. A linear SVM was trained on the grey matter segment of MR images in two different ways. First, all the individuals were used for training and classification was performed by the leave-one-out method, yielding 93% correct classification (sensitivity = 0.905, specificity = 0.942. Separately, a random half of the available data were used for training twice and each time the other, unseen, half of the data was classified, resulting 86% and 91% accurate classifications. Both gestational age (R = -0.24, p<0.04 and birth weight (R = -0.51, p < 0.001 correlated with the distance to decision boundary within the group of individuals born preterm. Statistically significant correlations were also found between IQ (R = -0.30, p < 0.001 and the distance to decision boundary. Those born small for gestational age did not form a separate subgroup in these analyses. The high rate of correct classification by the SVM motivates further investigation. The long-term goal is to automatically and non-invasively predict the outcome of preterm-born individuals on an individual basis using as early a scan as possible.

  1. Knee Joint Vibration Signal Analysis with Matching Pursuit Decomposition and Dynamic Weighted Classifier Fusion

    Directory of Open Access Journals (Sweden)

    Suxian Cai

    2013-01-01

    detected with the fixed threshold in the time domain. To perform a better classification over the data set of 89 VAG signals, we applied a novel classifier fusion system based on the dynamic weighted fusion (DWF method to ameliorate the classification performance. For comparison, a single leastsquares support vector machine (LS-SVM and the Bagging ensemble were used for the classification task as well. The results in terms of overall accuracy in percentage and area under the receiver operating characteristic curve obtained with the DWF-based classifier fusion method reached 88.76% and 0.9515, respectively, which demonstrated the effectiveness and superiority of the DWF method with two distinct features for the VAG signal analysis.

  2. International proposal for an acoustic classification scheme for dwellings

    DEFF Research Database (Denmark)

    Rasmussen, Birgit

    2014-01-01

    Acoustic classification schemes specify different quality levels for acoustic conditions. Regulations and classification schemes for dwellings typically include criteria for airborne and impact sound insulation, façade sound insulation and service equipment noise. However, although important...... classes, implying also trade barriers. Thus, a harmonized classification scheme would be useful, and the European COST Action TU0901 "Integrating and Harmonizing Sound Insulation Aspects in Sustainable Urban Housing Constructions", running 2009-2013 with members from 32 countries, including three overseas...... for quality of life, information about acoustic conditions is rarely available, neither for new or existing housing. Regulatory acoustic requirements will, if enforced, ensure a corresponding quality for new dwellings, but satisfactory conditions for occupants are not guaranteed. Consequently, several...

  3. A case of severe anorexia, excessive weight loss and high peptide YY levels after sleeve gastrectomy.

    Science.gov (United States)

    Pucci, Andrea; Cheung, Wui Hang; Jones, Jenny; Manning, Sean; Kingett, Helen; Adamo, Marco; Elkalaawy, Mohamed; Jenkinson, Andrew; Finer, Nicholas; Doyle, Jacqueline; Hashemi, Majid; Batterham, Rachel L

    2015-01-01

    Sleeve gastrectomy (SG) is the second most commonly performed bariatric procedure worldwide. Altered circulating gut hormones have been suggested to contribute post-operatively to appetite suppression, decreased caloric intake and weight reduction. In the present study, we report a 22-year-old woman who underwent laparoscopic SG for obesity (BMI 46 kg/m(2)). Post-operatively, she reported marked appetite reduction, which resulted in excessive weight loss (1-year post-SG: BMI 22 kg/m(2), weight loss 52%, >99th centile of 1-year percentage of weight loss from 453 SG patients). Gastrointestinal (GI) imaging, GI physiology/motility studies and endoscopy revealed no anatomical cause for her symptoms, and psychological assessments excluded an eating disorder. Despite nutritional supplements and anti-emetics, her weight loss continued (BMI 19 kg/m(2)), and she required nasogastric feeding. A random gut hormone assessment revealed high plasma peptide YY (PYY) levels. She underwent a 3 h meal study following an overnight fast to assess her subjective appetite and circulating gut hormone levels. Her fasted nausea scores were high, with low hunger, and these worsened with nutrient ingestion. Compared to ten other post-SG female patients, her fasted circulating PYY and nutrient-stimulated PYY and active glucagon-like peptide 1 (GLP1) levels were markedly elevated. Octreotide treatment was associated with suppressed circulating PYY and GLP1 levels, increased appetite, increased caloric intake and weight gain (BMI 22 kg/m(2) after 6 months). The present case highlights the value of measuring gut hormones in patients following bariatric surgery who present with anorexia and excessive weight loss and suggests that octreotide treatment can produce symptomatic relief and weight regain in this setting. Roux-en-Y gastric bypass and SG produce marked sustained weight reduction. However, there is a marked individual variability in this reduction, and post-operative weight loss

  4. Classification of polynomial integrable systems of mixed scalar and vector evolution equations: I

    International Nuclear Information System (INIS)

    Tsuchida, Takayuki; Wolf, Thomas

    2005-01-01

    We perform a classification of integrable systems of mixed scalar and vector evolution equations with respect to higher symmetries. We consider polynomial systems that are homogeneous under a suitable weighting of variables. This paper deals with the KdV weighting, the Burgers (or potential KdV or modified KdV) weighting, the Ibragimov-Shabat weighting and two unfamiliar weightings. The case of other weightings will be studied in a subsequent paper. Making an ansatz for undetermined coefficients and using a computer package for solving bilinear algebraic systems, we give the complete lists of second-order systems with a third-order or a fourth-order symmetry and third-order systems with a fifth-order symmetry. For all but a few systems in the lists, we show that the system (or, at least a subsystem of it) admits either a Lax representation or a linearizing transformation. A thorough comparison with recent work of Foursov and Olver is made

  5. Classification of polynomial integrable systems of mixed scalar and vector evolution equations: I

    Energy Technology Data Exchange (ETDEWEB)

    Tsuchida, Takayuki [Department of Physics, Kwansei Gakuin University, 2-1 Gakuen, Sanda 669-1337 (Japan); Wolf, Thomas [Department of Mathematics, Brock University, St Catharines, ON L2S 3A1 (Canada)

    2005-09-02

    We perform a classification of integrable systems of mixed scalar and vector evolution equations with respect to higher symmetries. We consider polynomial systems that are homogeneous under a suitable weighting of variables. This paper deals with the KdV weighting, the Burgers (or potential KdV or modified KdV) weighting, the Ibragimov-Shabat weighting and two unfamiliar weightings. The case of other weightings will be studied in a subsequent paper. Making an ansatz for undetermined coefficients and using a computer package for solving bilinear algebraic systems, we give the complete lists of second-order systems with a third-order or a fourth-order symmetry and third-order systems with a fifth-order symmetry. For all but a few systems in the lists, we show that the system (or, at least a subsystem of it) admits either a Lax representation or a linearizing transformation. A thorough comparison with recent work of Foursov and Olver is made.

  6. An ordinal classification approach for CTG categorization.

    Science.gov (United States)

    Georgoulas, George; Karvelis, Petros; Gavrilis, Dimitris; Stylios, Chrysostomos D; Nikolakopoulos, George

    2017-07-01

    Evaluation of cardiotocogram (CTG) is a standard approach employed during pregnancy and delivery. But, its interpretation requires high level expertise to decide whether the recording is Normal, Suspicious or Pathological. Therefore, a number of attempts have been carried out over the past three decades for development automated sophisticated systems. These systems are usually (multiclass) classification systems that assign a category to the respective CTG. However most of these systems usually do not take into consideration the natural ordering of the categories associated with CTG recordings. In this work, an algorithm that explicitly takes into consideration the ordering of CTG categories, based on binary decomposition method, is investigated. Achieved results, using as a base classifier the C4.5 decision tree classifier, prove that the ordinal classification approach is marginally better than the traditional multiclass classification approach, which utilizes the standard C4.5 algorithm for several performance criteria.

  7. PRED-CLASS: cascading neural networks for generalized protein classification and genome-wide applications.

    Science.gov (United States)

    Pasquier, C; Promponas, V J; Hamodrakas, S J

    2001-08-15

    A cascading system of hierarchical, artificial neural networks (named PRED-CLASS) is presented for the generalized classification of proteins into four distinct classes-transmembrane, fibrous, globular, and mixed-from information solely encoded in their amino acid sequences. The architecture of the individual component networks is kept very simple, reducing the number of free parameters (network synaptic weights) for faster training, improved generalization, and the avoidance of data overfitting. Capturing information from as few as 50 protein sequences spread among the four target classes (6 transmembrane, 10 fibrous, 13 globular, and 17 mixed), PRED-CLASS was able to obtain 371 correct predictions out of a set of 387 proteins (success rate approximately 96%) unambiguously assigned into one of the target classes. The application of PRED-CLASS to several test sets and complete proteomes of several organisms demonstrates that such a method could serve as a valuable tool in the annotation of genomic open reading frames with no functional assignment or as a preliminary step in fold recognition and ab initio structure prediction methods. Detailed results obtained for various data sets and completed genomes, along with a web sever running the PRED-CLASS algorithm, can be accessed over the World Wide Web at http://o2.biol.uoa.gr/PRED-CLASS.

  8. Computer Aided Design for Soil Classification Relational Database ...

    African Journals Online (AJOL)

    unique firstlady

    engineering, several developers were asked what rules they applied to identify ... classification is actually a part of all good science. As Michalski ... by a large number of soil scientists. .... and use. The calculus relational database processing is.

  9. Feature generation and representations for protein-protein interaction classification.

    Science.gov (United States)

    Lan, Man; Tan, Chew Lim; Su, Jian

    2009-10-01

    Automatic detecting protein-protein interaction (PPI) relevant articles is a crucial step for large-scale biological database curation. The previous work adopted POS tagging, shallow parsing and sentence splitting techniques, but they achieved worse performance than the simple bag-of-words representation. In this paper, we generated and investigated multiple types of feature representations in order to further improve the performance of PPI text classification task. Besides the traditional domain-independent bag-of-words approach and the term weighting methods, we also explored other domain-dependent features, i.e. protein-protein interaction trigger keywords, protein named entities and the advanced ways of incorporating Natural Language Processing (NLP) output. The integration of these multiple features has been evaluated on the BioCreAtIvE II corpus. The experimental results showed that both the advanced way of using NLP output and the integration of bag-of-words and NLP output improved the performance of text classification. Specifically, in comparison with the best performance achieved in the BioCreAtIvE II IAS, the feature-level and classifier-level integration of multiple features improved the performance of classification 2.71% and 3.95%, respectively.

  10. Land-Use and Land-Cover Mapping Using a Gradable Classification Method

    Directory of Open Access Journals (Sweden)

    Keigo Kitada

    2012-05-01

    Full Text Available Conventional spectral-based classification methods have significant limitations in the digital classification of urban land-use and land-cover classes from high-resolution remotely sensed data because of the lack of consideration given to the spatial properties of images. To recognize the complex distribution of urban features in high-resolution image data, texture information consisting of a group of pixels should be considered. Lacunarity is an index used to characterize different texture appearances. It is often reported that the land-use and land-cover in urban areas can be effectively classified using the lacunarity index with high-resolution images. However, the applicability of the maximum-likelihood approach for hybrid analysis has not been reported. A more effective approach that employs the original spectral data and lacunarity index can be expected to improve the accuracy of the classification. A new classification procedure referred to as “gradable classification method” is proposed in this study. This method improves the classification accuracy in incremental steps. The proposed classification approach integrates several classification maps created from original images and lacunarity maps, which consist of lacnarity values, to create a new classification map. The results of this study confirm the suitability of the gradable classification approach, which produced a higher overall accuracy (68% and kappa coefficient (0.64 than those (65% and 0.60, respectively obtained with the maximum-likelihood approach.

  11. Lie Group Classification of a Generalized Lane-Emden Type System in Two Dimensions

    Directory of Open Access Journals (Sweden)

    Motlatsi Molati

    2012-01-01

    Full Text Available The aim of this work is to perform a complete Lie symmetry classification of a generalized Lane-Emden type system in two dimensions which models many physical phenomena in biological and physical sciences. The classical approach of group classification is employed for classification. We show that several cases arise in classifying the arbitrary parameters, the forms of which include amongst others the power law nonlinearity, and exponential and quadratic forms.

  12. Weight self-regulation process in adolescence: the relationship between control weight attitudes, behaviors and body weight status

    Directory of Open Access Journals (Sweden)

    Jordi ePich

    2015-05-01

    Full Text Available Adolescents’ self-control weight behaviors were assessed (n= 1961; 12-17 years old; 2007-2008 in the Balearic Islands, Spain. The study analyzed the relationships between body weight status, body image and self-weight concern, and actual attempts to lose weight by restrained eating and/or increased exercising. In terms of regulatory focus theory (RFT, we considered that efforts to lose or to maintain weight (successful or failed would be motivated either by a promotion focus (to show an attractive body, a prevention focus (to avoid social rejection of fatness, or both. Results showed that 41% of overweight boys and 25% of obese boys stated that they had never made any attempt to lose weight, and 13% and 4% in females. Around half of overweight boys and around a quarter of obese boys stated that they were Not at all concerned about weight gain, and girls’ percentages decreased to 13% and 11% respectively. By contrast 57% of normal weight girls monitored their weight and stated that they had tried to slim at least once. Weight self-regulation in females attempted to combine diet and exercise, while boys relied almost exclusively on exercise. Apparent lack of consciousness of body weight status among overweight boys, and more important, subsequent absence of behaviors to reduce their weight clearly challenges efforts to prevent obesity. We argue that several causes may be involved in this outcome, including unconscious emotional (self-defense and cognitive (dissonance mechanisms driven by perceived social stigmatization of obesity. The active participation of social values of male and female body image (strong vs. pretty and the existence of social habituation to overweight are suggested. A better knowledge of psychosocial mechanisms underlying adolescent weight self-control may improve obesity epidemics.

  13. Decision theory for discrimination-aware classification

    KAUST Repository

    Kamiran, Faisal

    2012-12-01

    Social discrimination (e.g., against females) arising from data mining techniques is a growing concern worldwide. In recent years, several methods have been proposed for making classifiers learned over discriminatory data discriminationaware. However, these methods suffer from two major shortcomings: (1) They require either modifying the discriminatory data or tweaking a specific classification algorithm and (2) They are not flexible w.r.t. discrimination control and multiple sensitive attribute handling. In this paper, we present two solutions for discrimination-aware classification that neither require data modification nor classifier tweaking. Our first and second solutions exploit, respectively, the reject option of probabilistic classifier(s) and the disagreement region of general classifier ensembles to reduce discrimination. We relate both solutions with decision theory for better understanding of the process. Our experiments using real-world datasets demonstrate that our solutions outperform existing state-ofthe-art methods, especially at low discrimination which is a significant advantage. The superior performance coupled with flexible control over discrimination and easy applicability to multiple sensitive attributes makes our solutions an important step forward in practical discrimination-aware classification. © 2012 IEEE.

  14. Detection of hepatocellular carcinoma in gadoxetic acid-enhanced MRI and diffusion-weighted MRI with respect to the severity of liver cirrhosis

    International Nuclear Information System (INIS)

    Kim, Ah Yeong; Kim, Young Kon; Lee, Min Woo; Park, Min Jung; Hwang, Jiyoung; Lee, Mi Hee; Lee, Jae Won

    2012-01-01

    Background As gadoxetic acid-enhanced magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) have been widely used for the evaluation of hepatocellular carcinoma (HCC), it is clinically relevant to determine the diagnostic efficacy of gadoxetic acid-enhanced MRI and DWI for detection of HCCs with respect to the severity of liver cirrhosis. Purpose To compare the diagnostic accuracy and sensitivity of gadoxetic acid-enhanced MRI and DWI for detection of HCCs with respect to the severity of liver cirrhosis. Material and Methods A total of 189 patients with 240 HCCs (≤3.0 cm) (Child-Pugh A, 81 patients with 90 HCCs; Child-Pugh B, 65 patients with 85 HCCs; Child-Pugh C, 43 patients with 65 HCCs) underwent DWI and gadoxetic acid-enhanced MRI at 3.0 T. A gadoxetic acid set (dynamic and hepatobiliary phase plus T2-weighted image) and DWI set (DWI plus unenhanced MRIs) for each Child-Pugh class were analyzed independently by two observers for detecting HCCs using receiver-operating characteristic analysis. The diagnostic accuracy and sensitivity were calculated. Results There was a trend toward decreased diagnostic accuracy for gadoxetic acid and DWI set with respect to the severity of cirrhosis (Child-Pugh A [mean 0.974, 0.961], B [mean 0.904, 0.863], C [mean 0.779, 0.760]). For both observers, the sensitivities of both image sets were highest in Child-Pugh class A (mean 95.6%, 93.9%), followed by class B (mean 83.0%, 77.1%), and class C (mean 60.6%, 60.0%) (P < 0.05). Conclusion In HCC detection, the diagnostic accuracy and sensitivity for gadoxetic acid-enhanced MRI and DWI were highest in Child-Pugh class A, followed by Child-Pugh class B, and Child-Pugh class C, indicating a tendency toward decreased diagnostic capability with the severity of cirrhosis

  15. Classification of hydrocephalus: critical analysis of classification categories and advantages of "Multi-categorical Hydrocephalus Classification" (Mc HC).

    Science.gov (United States)

    Oi, Shizuo

    2011-10-01

    Hydrocephalus is a complex pathophysiology with disturbed cerebrospinal fluid (CSF) circulation. There are numerous numbers of classification trials published focusing on various criteria, such as associated anomalies/underlying lesions, CSF circulation/intracranial pressure patterns, clinical features, and other categories. However, no definitive classification exists comprehensively to cover the variety of these aspects. The new classification of hydrocephalus, "Multi-categorical Hydrocephalus Classification" (Mc HC), was invented and developed to cover the entire aspects of hydrocephalus with all considerable classification items and categories. Ten categories include "Mc HC" category I: onset (age, phase), II: cause, III: underlying lesion, IV: symptomatology, V: pathophysiology 1-CSF circulation, VI: pathophysiology 2-ICP dynamics, VII: chronology, VII: post-shunt, VIII: post-endoscopic third ventriculostomy, and X: others. From a 100-year search of publication related to the classification of hydrocephalus, 14 representative publications were reviewed and divided into the 10 categories. The Baumkuchen classification graph made from the round o'clock classification demonstrated the historical tendency of deviation to the categories in pathophysiology, either CSF or ICP dynamics. In the preliminary clinical application, it was concluded that "Mc HC" is extremely effective in expressing the individual state with various categories in the past and present condition or among the compatible cases of hydrocephalus along with the possible chronological change in the future.

  16. Serum Concentration of Leptin in Pregnant Adolescents Correlated with Gestational Weight Gain, Postpartum Weight Retention and Newborn Weight/Length

    OpenAIRE

    Reyna Sámano; Hugo Martínez-Rojano; Gabriela Chico-Barba; Estela Godínez-Martínez; Bernarda Sánchez-Jiménez; Diana Montiel-Ojeda; Maricruz Tolentino

    2017-01-01

    Introduction: Gestational weight gain is an important modifiable factor known to influence fetal outcomes including birth weight and adiposity. Leptin is normally correlated with adiposity and is also known to increase throughout pregnancy, as the placenta becomes a source of leptin synthesis. Several studies have reported positive correlations between cord blood leptin level and either birthweight or size for gestational age, as well as body mass index (BMI). Objective: To determine the corr...

  17. A Classification-based Review Recommender

    Science.gov (United States)

    O'Mahony, Michael P.; Smyth, Barry

    Many online stores encourage their users to submit product/service reviews in order to guide future purchasing decisions. These reviews are often listed alongside product recommendations but, to date, limited attention has been paid as to how best to present these reviews to the end-user. In this paper, we describe a supervised classification approach that is designed to identify and recommend the most helpful product reviews. Using the TripAdvisor service as a case study, we compare the performance of several classification techniques using a range of features derived from hotel reviews. We then describe how these classifiers can be used as the basis for a practical recommender that automatically suggests the mosthelpful contrasting reviews to end-users. We present an empirical evaluation which shows that our approach achieves a statistically significant improvement over alternative review ranking schemes.

  18. Classification

    Science.gov (United States)

    Clary, Renee; Wandersee, James

    2013-01-01

    In this article, Renee Clary and James Wandersee describe the beginnings of "Classification," which lies at the very heart of science and depends upon pattern recognition. Clary and Wandersee approach patterns by first telling the story of the "Linnaean classification system," introduced by Carl Linnacus (1707-1778), who is…

  19. Convolutional deep belief network with feature encoding for classification of neuroblastoma histological images

    Directory of Open Access Journals (Sweden)

    Soheila Gheisari

    2018-01-01

    Full Text Available Background: Neuroblastoma is the most common extracranial solid tumor in children younger than 5 years old. Optimal management of neuroblastic tumors depends on many factors including histopathological classification. The gold standard for classification of neuroblastoma histological images is visual microscopic assessment. In this study, we propose and evaluate a deep learning approach to classify high-resolution digital images of neuroblastoma histology into five different classes determined by the Shimada classification. Subjects and Methods: We apply a combination of convolutional deep belief network (CDBN with feature encoding algorithm that automatically classifies digital images of neuroblastoma histology into five different classes. We design a three-layer CDBN to extract high-level features from neuroblastoma histological images and combine with a feature encoding model to extract features that are highly discriminative in the classification task. The extracted features are classified into five different classes using a support vector machine classifier. Data: We constructed a dataset of 1043 neuroblastoma histological images derived from Aperio scanner from 125 patients representing different classes of neuroblastoma tumors. Results: The weighted average F-measure of 86.01% was obtained from the selected high-level features, outperforming state-of-the-art methods. Conclusion: The proposed computer-aided classification system, which uses the combination of deep architecture and feature encoding to learn high-level features, is highly effective in the classification of neuroblastoma histological images.

  20. Hierarchical vs non-hierarchical audio indexation and classification for video genres

    Science.gov (United States)

    Dammak, Nouha; BenAyed, Yassine

    2018-04-01

    In this paper, Support Vector Machines (SVMs) are used for segmenting and indexing video genres based on only audio features extracted at block level, which has a prominent asset by capturing local temporal information. The main contribution of our study is to show the wide effect on the classification accuracies while using an hierarchical categorization structure based on Mel Frequency Cepstral Coefficients (MFCC) audio descriptor. In fact, the classification consists in three common video genres: sports videos, music clips and news scenes. The sub-classification may divide each genre into several multi-speaker and multi-dialect sub-genres. The validation of this approach was carried out on over 360 minutes of video span yielding a classification accuracy of over 99%.

  1. Association between gastric cancer and the Kyoto classification of gastritis.

    Science.gov (United States)

    Shichijo, Satoki; Hirata, Yoshihiro; Niikura, Ryota; Hayakawa, Yoku; Yamada, Atsuo; Koike, Kazuhiko

    2017-09-01

    Histological gastritis is associated with gastric cancer, but its diagnosis requires biopsy. Many classifications of endoscopic gastritis are available, but not all are useful for risk stratification of gastric cancer. The Kyoto Classification of Gastritis was proposed at the 85th Congress of the Japan Gastroenterological Endoscopy Society. This cross-sectional study evaluated the usefulness of the Kyoto Classification of Gastritis for risk stratification of gastric cancer. From August 2013 to September 2014, esophagogastroduodenoscopy was performed and the gastric findings evaluated according to the Kyoto Classification of Gastritis in a total of 4062 patients. The following five endoscopic findings were selected based on previous reports: atrophy, intestinal metaplasia, enlarged folds, nodularity, and diffuse redness. A total of 3392 patients (1746 [51%] men and 1646 [49%] women) were analyzed. Among them, 107 gastric cancers were diagnosed. Atrophy was found in 2585 (78%) and intestinal metaplasia in 924 (27%). Enlarged folds, nodularity, and diffuse redness were found in 197 (5.8%), 22 (0.6%), and 573 (17%), respectively. In univariate analyses, the severity of atrophy, intestinal metaplasia, diffuse redness, age, and male sex were associated with gastric cancer. In a multivariate analysis, atrophy and male sex were found to be independent risk factors. Younger age and severe atrophy were determined to be associated with diffuse-type gastric cancer. Endoscopic detection of atrophy was associated with the risk of gastric cancer. Thus, patients with severe atrophy should be examined carefully and may require intensive follow-up. © 2017 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

  2. Kardashev’s classification at 50+: A fine vehicle with room for improvement

    Directory of Open Access Journals (Sweden)

    Ćirković M.M.

    2015-01-01

    Full Text Available We review the history and status of the famous classification of extraterrestrial civilizations given by the great Russian astrophysicist Nikolai Semenovich Kardashev, roughly half a century after it has been proposed. While Kardashev’s classification (or Kardashev’s scale has often been seen as oversimplified, and multiple improvements, refinements, and alternatives to it have been suggested, it is still one of the major tools for serious theoretical investigation of SETI issues. During these 50+ years, several attempts at modifying or reforming the classification have been made; we review some of them here, together with presenting some of the scenarios which present difficulties to the standard version. Recent results in both theoretical and observational SETI studies, especially the ˆG infrared survey (2014-2015, have persuasively shown that the emphasis on detectability inherent in Kardashev’s classification obtains new significance and freshness. Several new movements and conceptual frameworks, such as the Dysonian SETI, tally extremely well with these developments. So, the apparent simplicity of the classification is highly deceptive: Kardashev’s work offers a wealth of still insufficiently studied methodological and epistemological ramifications and it remains, in both letter and spirit, perhaps the worthiest legacy of the SETI “founding fathers”. [Projekat Ministarstva nauke Republike Srbije, br. ON176021

  3. Validation of a new classification system for interprosthetic femoral fractures.

    Science.gov (United States)

    Pires, Robinson Esteves Santos; Silveira, Marcelo Peixoto Sena; Resende, Alessandra Regina da Silva; Junior, Egidio Oliveira Santana; Campos, Tulio Vinicius Oliveira; Santos, Leandro Emilio Nascimento; Balbachevsky, Daniel; Andrade, Marco Antônio Percope de

    2017-07-01

    Interprosthetic femoral fracture (IFF) incidence is gradually increasing as the population is progressively ageing. However, treatment remains challenging due to several contributing factors, such as poor bone quality, patient comorbidities, small interprosthetic fragment, and prostheses instability. An effective and specific classification system is essential to optimize treatment management, therefore diminishing complication rates. This study aims to validate a previously described classification system for interprosthetic femoral fractures. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. Local Kernel for Brains Classification in Schizophrenia

    Science.gov (United States)

    Castellani, U.; Rossato, E.; Murino, V.; Bellani, M.; Rambaldelli, G.; Tansella, M.; Brambilla, P.

    In this paper a novel framework for brain classification is proposed in the context of mental health research. A learning by example method is introduced by combining local measurements with non linear Support Vector Machine. Instead of considering a voxel-by-voxel comparison between patients and controls, we focus on landmark points which are characterized by local region descriptors, namely Scale Invariance Feature Transform (SIFT). Then, matching is obtained by introducing the local kernel for which the samples are represented by unordered set of features. Moreover, a new weighting approach is proposed to take into account the discriminative relevance of the detected groups of features. Experiments have been performed including a set of 54 patients with schizophrenia and 54 normal controls on which region of interest (ROI) have been manually traced by experts. Preliminary results on Dorso-lateral PreFrontal Cortex (DLPFC) region are promising since up to 75% of successful classification rate has been obtained with this technique and the performance has improved up to 85% when the subjects have been stratified by sex.

  5. Classifier fusion for VoIP attacks classification

    Science.gov (United States)

    Safarik, Jakub; Rezac, Filip

    2017-05-01

    SIP is one of the most successful protocols in the field of IP telephony communication. It establishes and manages VoIP calls. As the number of SIP implementation rises, we can expect a higher number of attacks on the communication system in the near future. This work aims at malicious SIP traffic classification. A number of various machine learning algorithms have been developed for attack classification. The paper presents a comparison of current research and the use of classifier fusion method leading to a potential decrease in classification error rate. Use of classifier combination makes a more robust solution without difficulties that may affect single algorithms. Different voting schemes, combination rules, and classifiers are discussed to improve the overall performance. All classifiers have been trained on real malicious traffic. The concept of traffic monitoring depends on the network of honeypot nodes. These honeypots run in several networks spread in different locations. Separation of honeypots allows us to gain an independent and trustworthy attack information.

  6. A Study on the Requisite Information for Severe Accident Management

    Energy Technology Data Exchange (ETDEWEB)

    Park, Sunhee; Ahn, Kwang-Il; Kim, Jae-Hwan [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

    2016-10-15

    Related this research on arranging the requisite information for severe accident management, the documents of various forms in each country as well as the domestic literature are secured and analyzed. The analyzed information is arranged up to a detailed level. For the secured documents, the issued organizations and the issued purpose are diverse. Thus, the contents of the secured documents are also diverse according to the reactor type, and the purpose and standards of the classification are also diverse. Moreover, terminologies with same meaning are not unified. These various documents are analyzed to arrange the requisite information for severe accident management. Based on the documents of a related severe accident, the major information was analyzed. The information is different according to the reactor type, classification standard, and classification standard of the safety function. Thus the information is classified variously. In this study, based on the analysis results of the documents described these information, the major information and parameters are examined as safety function. And the results of parameters and information including the safety function and the detail information are induced.

  7. Video based object representation and classification using multiple covariance matrices.

    Science.gov (United States)

    Zhang, Yurong; Liu, Quan

    2017-01-01

    Video based object recognition and classification has been widely studied in computer vision and image processing area. One main issue of this task is to develop an effective representation for video. This problem can generally be formulated as image set representation. In this paper, we present a new method called Multiple Covariance Discriminative Learning (MCDL) for image set representation and classification problem. The core idea of MCDL is to represent an image set using multiple covariance matrices with each covariance matrix representing one cluster of images. Firstly, we use the Nonnegative Matrix Factorization (NMF) method to do image clustering within each image set, and then adopt Covariance Discriminative Learning on each cluster (subset) of images. At last, we adopt KLDA and nearest neighborhood classification method for image set classification. Promising experimental results on several datasets show the effectiveness of our MCDL method.

  8. Desert plains classification based on Geomorphometrical parameters (Case study: Aghda, Yazd)

    Science.gov (United States)

    Tazeh, mahdi; Kalantari, Saeideh

    2013-04-01

    This research focuses on plains. There are several tremendous methods and classification which presented for plain classification. One of The natural resource based classification which is mostly using in Iran, classified plains into three types, Erosional Pediment, Denudation Pediment Aggradational Piedmont. The qualitative and quantitative factors to differentiate them from each other are also used appropriately. In this study effective Geomorphometrical parameters in differentiate landforms were applied for plain. Geomorphometrical parameters are calculable and can be extracted using mathematical equations and the corresponding relations on digital elevation model. Geomorphometrical parameters used in this study included Percent of Slope, Plan Curvature, Profile Curvature, Minimum Curvature, the Maximum Curvature, Cross sectional Curvature, Longitudinal Curvature and Gaussian Curvature. The results indicated that the most important affecting Geomorphometrical parameters for plain and desert classifications includes: Percent of Slope, Minimum Curvature, Profile Curvature, and Longitudinal Curvature. Key Words: Plain, Geomorphometry, Classification, Biophysical, Yazd Khezarabad.

  9. Forensic age assessment by 3.0T MRI of the knee: proposal of a new MRI classification of ossification stages.

    Science.gov (United States)

    Vieth, Volker; Schulz, Ronald; Heindel, Walter; Pfeiffer, Heidi; Buerke, Boris; Schmeling, Andreas; Ottow, Christian

    2018-03-13

    To explore the possibility of determining majority via a morphology-based examination of the epiphyseal-diaphyseal fusion by 3.0 T magnetic resonance imaging (MRI), a prospective cross-sectional study developing and applying a new stage classification was conducted. 344 male and 350 female volunteers of German nationality between the ages of 12-24 years were scanned between May 2013 and June 2015. A 3.0 T MRI scanner was used, acquiring a T1-weighted (T1-w) turbo spin-echo sequence (TSE) and a T2-weighted (T2-w) TSE sequence with fat suppression by spectral pre-saturation with inversion recovery (SPIR). The gathered information was sifted and a five-stage classification was formulated as a hypothesis. The images were then assessed using this classification. The relevant statistics were defined, the intra- and interobserver agreements were determined, and the differences between the sexes were analysed. The application of the new classification made it possible to correctly assess majority in both sexes by the examination of the epiphyses of the knee joint. The intra- and interobserver agreement levels were very good (κ > 0.80). The Mann-Whitney-U Test implied significant sex-related differences for most stages. Applying the presented MRI classification, it is possible to determine the completion of the 18th year of life in either sex by 3.0 T MRI of the knee joint. • Based on prospective referential data a new MRI classification was formulated. • The setting allows assessment of the age of an individual's skeletal development. • The classification scheme allows the reliable determination of majority in both sexes. • The staging shows a high reproducibility for instructed and trained professional personnel. • The proposed classification is likely to be adaptable to other long bone epiphyses.

  10. Cloud field classification based on textural features

    Science.gov (United States)

    Sengupta, Sailes Kumar

    1989-01-01

    An essential component in global climate research is accurate cloud cover and type determination. Of the two approaches to texture-based classification (statistical and textural), only the former is effective in the classification of natural scenes such as land, ocean, and atmosphere. In the statistical approach that was adopted, parameters characterizing the stochastic properties of the spatial distribution of grey levels in an image are estimated and then used as features for cloud classification. Two types of textural measures were used. One is based on the distribution of the grey level difference vector (GLDV), and the other on a set of textural features derived from the MaxMin cooccurrence matrix (MMCM). The GLDV method looks at the difference D of grey levels at pixels separated by a horizontal distance d and computes several statistics based on this distribution. These are then used as features in subsequent classification. The MaxMin tectural features on the other hand are based on the MMCM, a matrix whose (I,J)th entry give the relative frequency of occurrences of the grey level pair (I,J) that are consecutive and thresholded local extremes separated by a given pixel distance d. Textural measures are then computed based on this matrix in much the same manner as is done in texture computation using the grey level cooccurrence matrix. The database consists of 37 cloud field scenes from LANDSAT imagery using a near IR visible channel. The classification algorithm used is the well known Stepwise Discriminant Analysis. The overall accuracy was estimated by the percentage or correct classifications in each case. It turns out that both types of classifiers, at their best combination of features, and at any given spatial resolution give approximately the same classification accuracy. A neural network based classifier with a feed forward architecture and a back propagation training algorithm is used to increase the classification accuracy, using these two classes

  11. Support Vector Machines for Hyperspectral Remote Sensing Classification

    Science.gov (United States)

    Gualtieri, J. Anthony; Cromp, R. F.

    1998-01-01

    The Support Vector Machine provides a new way to design classification algorithms which learn from examples (supervised learning) and generalize when applied to new data. We demonstrate its success on a difficult classification problem from hyperspectral remote sensing, where we obtain performances of 96%, and 87% correct for a 4 class problem, and a 16 class problem respectively. These results are somewhat better than other recent results on the same data. A key feature of this classifier is its ability to use high-dimensional data without the usual recourse to a feature selection step to reduce the dimensionality of the data. For this application, this is important, as hyperspectral data consists of several hundred contiguous spectral channels for each exemplar. We provide an introduction to this new approach, and demonstrate its application to classification of an agriculture scene.

  12. Fast-HPLC Fingerprinting to Discriminate Olive Oil from Other Edible Vegetable Oils by Multivariate Classification Methods.

    Science.gov (United States)

    Jiménez-Carvelo, Ana M; González-Casado, Antonio; Pérez-Castaño, Estefanía; Cuadros-Rodríguez, Luis

    2017-03-01

    A new analytical method for the differentiation of olive oil from other vegetable oils using reversed-phase LC and applying chemometric techniques was developed. A 3 cm short column was used to obtain the chromatographic fingerprint of the methyl-transesterified fraction of each vegetable oil. The chromatographic analysis took only 4 min. The multivariate classification methods used were k-nearest neighbors, partial least-squares (PLS) discriminant analysis, one-class PLS, support vector machine classification, and soft independent modeling of class analogies. The discrimination of olive oil from other vegetable edible oils was evaluated by several classification quality metrics. Several strategies for the classification of the olive oil were used: one input-class, two input-class, and pseudo two input-class.

  13. The revised WHO dengue case classification: does the system need to be modified?

    Science.gov (United States)

    Hadinegoro, Sri Rezeki S

    2012-05-01

    There has been considerable debate regarding the value of both the 1997 and 2009 World Health Organization (WHO) dengue case classification criteria for its diagnosis and management. Differentiation between classic dengue fever (DF) and dengue haemorrhagic fever (DHF) or severe dengue is a key aspect of dengue case classification. The geographic expansion of dengue and its increased incidence in older age groups have contributed to the limited applicability of the 1997 case definitions. Clinical experience of dengue suggests that the illness presents as a spectrum of disease instead of distinct phases. However, despite the rigid grouping of dengue into DF, DHF and dengue shock syndrome (DSS), overlap between the different manifestations has often been observed, which has affected clinical management and triage of patients. The findings of the DENCO study evaluating the 1997 case definitions formed the basis of the revised 2009 WHO case definitions, which classified the illness into dengue with and without warning signs and severe dengue. Although the revised scheme is more sensitive to the diagnosis of severe dengue, and beneficial to triage and case management, there remain issues with its applicability. It is considered by many to be too broad, requiring more specific definition of warning signs. Quantitative research into the predictive value of these warning signs on patient outcomes and the cost-effectiveness of the new classification system is required to ascertain whether the new classification system requires further modification, or whether elements of both classification systems can be combined.

  14. Tailoring dietary approaches for weight loss.

    Science.gov (United States)

    Gardner, C D

    2012-07-01

    Although the 'Low-Fat' diet was the predominant public health recommendation for weight loss and weight control for the past several decades, the obesity epidemic continued to grow during this time period. An alternative 'low-carbohydrate' (Low-Carb) approach, although originally dismissed and even vilified, was comparatively tested in a series of studies over the past decade, and has been found in general to be as effective, if not more, as the Low-Fat approach for weight loss and for several related metabolic health measures. From a glass half full perspective, this suggests that there is more than one choice for a dietary approach to lose weight, and that Low-Fat and Low-Carb diets may be equally effective. From a glass half empty perspective, the average amount of weight lost on either of these two dietary approaches under the conditions studied, particularly when followed beyond 1 year, has been modest at best and negligible at worst, suggesting that the two approaches may be equally ineffective. One could resign themselves at this point to focusing on calories and energy intake restriction, regardless of macronutrient distributions. However, before throwing out the half-glass of water, it is worthwhile to consider that focusing on average results may mask important subgroup successes and failures. In all weight-loss studies, without exception, the range of individual differences in weight change within any particular diet groups is orders of magnitude greater than the average group differences between diet groups. Several studies have now reported that adults with greater insulin resistance are more successful with weight loss on a lower-carbohydrate diet compared with a lower-fat diet, whereas adults with greater insulin sensitivity are equally or more successful with weight loss on a lower-fat diet compared with a lower-carbohydrate diet. Other preliminary findings suggest that there may be some promise with matching individuals with certain genotypes to

  15. Yarn-dyed fabric defect classification based on convolutional neural network

    Science.gov (United States)

    Jing, Junfeng; Dong, Amei; Li, Pengfei; Zhang, Kaibing

    2017-09-01

    Considering that manual inspection of the yarn-dyed fabric can be time consuming and inefficient, we propose a yarn-dyed fabric defect classification method by using a convolutional neural network (CNN) based on a modified AlexNet. CNN shows powerful ability in performing feature extraction and fusion by simulating the learning mechanism of human brain. The local response normalization layers in AlexNet are replaced by the batch normalization layers, which can enhance both the computational efficiency and classification accuracy. In the training process of the network, the characteristics of the defect are extracted step by step and the essential features of the image can be obtained from the fusion of the edge details with several convolution operations. Then the max-pooling layers, the dropout layers, and the fully connected layers are employed in the classification model to reduce the computation cost and extract more precise features of the defective fabric. Finally, the results of the defect classification are predicted by the softmax function. The experimental results show promising performance with an acceptable average classification rate and strong robustness on yarn-dyed fabric defect classification.

  16. Sound classification of dwellings - Comparison of schemes in Europe

    DEFF Research Database (Denmark)

    Rasmussen, Birgit

    2009-01-01

    National sound classification schemes for dwellings exist in nine countries in Europe, and proposals are under preparation in more countries. The schemes specify class criteria concerning several acoustic aspects, the main criteria being about airborne and impact sound insulation between dwellings......, facade sound insulation and installation noise. The quality classes reflect dierent levels of acoustical comfort. The paper presents and compares the sound classification schemes in Europe. The schemes have been implemented and revised gradually since the 1990es. However, due to lack of coordination...

  17. Prediction model to predict critical weight loss in patients with head and neck cancer during (chemo)radiotherapy.

    Science.gov (United States)

    Langius, Jacqueline A E; Twisk, Jos; Kampman, Martine; Doornaert, Patricia; Kramer, Mark H H; Weijs, Peter J M; Leemans, C René

    2016-01-01

    Patients with head and neck cancer (HNC) frequently encounter weight loss with multiple negative outcomes as a consequence. Adequate treatment is best achieved by early identification of patients at risk for critical weight loss. The objective of this study was to detect predictive factors for critical weight loss in patients with HNC receiving (chemo)radiotherapy ((C)RT). In this cohort study, 910 patients with HNC were included receiving RT (±surgery/concurrent chemotherapy) with curative intent. Body weight was measured at the start and end of (C)RT. Logistic regression and classification and regression tree (CART) analyses were used to analyse predictive factors for critical weight loss (defined as >5%) during (C)RT. Possible predictors included gender, age, WHO performance status, tumour location, TNM classification, treatment modality, RT technique (three-dimensional conformal RT (3D-RT) vs intensity-modulated RT (IMRT)), total dose on the primary tumour and RT on the elective or macroscopic lymph nodes. At the end of (C)RT, mean weight loss was 5.1±4.9%. Fifty percent of patients had critical weight loss during (C)RT. The main predictors for critical weight loss during (C)RT by both logistic and CART analyses were RT on the lymph nodes, higher RT dose on the primary tumour, receiving 3D-RT instead of IMRT, and younger age. Critical weight loss during (C)RT was prevalent in half of HNC patients. To predict critical weight loss, a practical prediction tree for adequate nutritional advice was developed, including the risk factors RT to the neck, higher RT dose, 3D-RT, and younger age. Copyright © 2015 Elsevier Ltd. All rights reserved.

  18. Body weight and ADHD: examining the role of self-regulation.

    Directory of Open Access Journals (Sweden)

    Zia Choudhry

    Full Text Available Attention-Deficit/Hyperactivity Disorder (ADHD is a complex and heterogeneous childhood disorder that often coexists with other psychiatric and somatic disorders. Recently, a link between ADHD and body weight dysregulation has been reported and often interpreted as impaired self-regulation that is shared between the two conditions. The objective of this study is to investigate the relation between body weight/BMI and cognitive, emotional and motor characteristics in children with ADHD.284 ADHD children were stratified by weight status/BMI according to WHO classification and compared with regard to their neurocognitive characteristics, motivational style, and motor profile as assessed by a comprehensive battery of tests. All comparisons were adjusted for demographic characteristics of relevance including, socioeconomic status (SES.Both Obese and overweight ADHD children exhibited significantly lower SES compared to normal weight ADHD children. No significant differences were observed between the three groups with regards to their neurocognitive, emotional and motor profile.Our findings provide evidence that differences in weight/BMI are not accounted for by cognitive, motivational and motor profiles. Socio-economic characteristics are strongly associated with overweight and obesity in ADHD children and may inform strategies aimed at promoting healthier weight.

  19. Fast Parallel Image Registration on CPU and GPU for Diagnostic Classification of Alzheimer's Disease

    Directory of Open Access Journals (Sweden)

    Denis P Shamonin

    2014-01-01

    Full Text Available Nonrigid image registration is an important, but time-consuming taskin medical image analysis. In typical neuroimaging studies, multipleimage registrations are performed, i.e. for atlas-based segmentationor template construction. Faster image registration routines wouldtherefore be beneficial.In this paper we explore acceleration of the image registrationpackage elastix by a combination of several techniques: iparallelization on the CPU, to speed up the cost function derivativecalculation; ii parallelization on the GPU building on andextending the OpenCL framework from ITKv4, to speed up the Gaussianpyramid computation and the image resampling step; iii exploitationof certain properties of the B-spline transformation model; ivfurther software optimizations.The accelerated registration tool is employed in a study ondiagnostic classification of Alzheimer's disease and cognitivelynormal controls based on T1-weighted MRI. We selected 299participants from the publicly available Alzheimer's DiseaseNeuroimaging Initiative database. Classification is performed with asupport vector machine based on gray matter volumes as a marker foratrophy. We evaluated two types of strategies (voxel-wise andregion-wise that heavily rely on nonrigid image registration.Parallelization and optimization resulted in an acceleration factorof 4-5x on an 8-core machine. Using OpenCL a speedup factor of ~2was realized for computation of the Gaussian pyramids, and 15-60 forthe resampling step, for larger images. The voxel-wise and theregion-wise classification methods had an area under thereceiver operator characteristic curve of 88% and 90%,respectively, both for standard and accelerated registration.We conclude that the image registration package elastix wassubstantially accelerated, with nearly identical results to thenon-optimized version. The new functionality will become availablein the next release of elastix as open source under the BSD license.

  20. [Study on biopharmaceutics classification system for Chinese materia medica of extract of Huanglian].

    Science.gov (United States)

    Liu, Yang; Yin, Xiu-Wen; Wang, Zi-Yu; Li, Xue-Lian; Pan, Meng; Li, Yan-Ping; Dong, Ling

    2017-11-01

    One of the advantages of biopharmaceutics classification system of Chinese materia medica (CMMBCS) is expanding the classification research level from single ingredient to multi-components of Chinese herb, and from multi-components research to holistic research of the Chinese materia medica. In present paper, the alkaloids of extract of huanglian were chosen as the main research object to explore their change rules in solubility and intestinal permeability of single-component and multi-components, and to determine the biopharmaceutical classification of extract of Huanglian from holistic level. The typical shake-flask method and HPLC were used to detect the solubility of single ingredient of alkaloids from extract of huanglian. The quantitative research of alkaloids in intestinal absorption was measured in single-pass intestinal perfusion experiment while permeability coefficient of extract of huanglian was calculated by self-defined weight coefficient method. Copyright© by the Chinese Pharmaceutical Association.

  1. Prevalence and severity of menopause symptoms and associated factors across menopause status in Korean women.

    Science.gov (United States)

    Yim, Gyeyoon; Ahn, Younjhin; Chang, Yoosoo; Ryu, Seungho; Lim, Joong-Yeon; Kang, Danbee; Choi, Eun-Kyung; Ahn, Jiin; Choi, Yuni; Cho, Juhee; Park, Hyun-Young

    2015-10-01

    The present study investigated the prevalence and severity of menopause symptoms experienced by Korean women aged 44 to 56 years and their associated factors. A cross-sectional study was performed on 2,201 women aged 44 to 56 years in health checkup centers between November 2012 and March 2013. The 29-item Menopause-Specific Quality of Life Questionnaire was used to assess vasomotor, psychosocial, physical, and sexual symptoms related to menopause. The guidelines for the classification of reproductive aging stages proposed at the Stages of Reproductive Aging Workshop were used. Multivariable linear regression analyses were performed to identify factors associated with severity of menopause symptoms. Among participants, 42.6% were premenopausal, 36.7% were perimenopausal, and 20.7% were postmenopausal. Although physical symptoms were the most severe menopause symptoms experienced by premenopausal and perimenopausal women, postmenopausal women reported sexual symptoms as the most bothersome. The mean scores for each domain increased from the premenopausal period through the postmenopausal period (P for trend menopause symptoms (P menopause than inactive women. Postmenopausal women experience the most severe symptoms. Obesity and physical activity are the main modifiable factors associated with symptom severity. Further studies are needed to examine the effects of physical activity promotion and weight control interventions on preventing menopause symptoms in Korean women.

  2. Overview of Four Functional Classification Systems Commonly Used in Cerebral Palsy

    Directory of Open Access Journals (Sweden)

    Andrea Paulson

    2017-04-01

    Full Text Available Cerebral palsy (CP is the most common physical disability in childhood. CP comprises a heterogeneous group of disorders that can result in spasticity, dystonia, muscle contractures, weakness and coordination difficulty that ultimately affects the ability to control movements. Traditionally, CP has been classified using a combination of the motor type and the topographical distribution, as well as subjective severity level. Imprecise terms such as these tell very little about what a person is able to do functionally and can impair clear communication between providers. More recently, classification systems have been created employing a simple ordinal grading system of functional performance. These systems allow a more precise discussion between providers, as well as better subject stratification for research. The goal of this review is to describe four common functional classification systems for cerebral palsy: the Gross Motor Function Classification System (GMFCS, the Manual Ability Classification System (MACS, the Communication Function Classification System (CFCS, and the Eating and Drinking Ability Classification System (EDACS. These measures are all standardized, reliable, and complementary to one another.

  3. Hand eczema classification

    DEFF Research Database (Denmark)

    Diepgen, T L; Andersen, Klaus Ejner; Brandao, F M

    2008-01-01

    of the disease is rarely evidence based, and a classification system for different subdiagnoses of hand eczema is not agreed upon. Randomized controlled trials investigating the treatment of hand eczema are called for. For this, as well as for clinical purposes, a generally accepted classification system...... A classification system for hand eczema is proposed. Conclusions It is suggested that this classification be used in clinical work and in clinical trials....

  4. A comprehensive simulation study on classification of RNA-Seq data.

    Directory of Open Access Journals (Sweden)

    Gökmen Zararsız

    Full Text Available RNA sequencing (RNA-Seq is a powerful technique for the gene-expression profiling of organisms that uses the capabilities of next-generation sequencing technologies. Developing gene-expression-based classification algorithms is an emerging powerful method for diagnosis, disease classification and monitoring at molecular level, as well as providing potential markers of diseases. Most of the statistical methods proposed for the classification of gene-expression data are either based on a continuous scale (eg. microarray data or require a normal distribution assumption. Hence, these methods cannot be directly applied to RNA-Seq data since they violate both data structure and distributional assumptions. However, it is possible to apply these algorithms with appropriate modifications to RNA-Seq data. One way is to develop count-based classifiers, such as Poisson linear discriminant analysis and negative binomial linear discriminant analysis. Another way is to bring the data closer to microarrays and apply microarray-based classifiers. In this study, we compared several classifiers including PLDA with and without power transformation, NBLDA, single SVM, bagging SVM (bagSVM, classification and regression trees (CART, and random forests (RF. We also examined the effect of several parameters such as overdispersion, sample size, number of genes, number of classes, differential-expression rate, and the transformation method on model performances. A comprehensive simulation study is conducted and the results are compared with the results of two miRNA and two mRNA experimental datasets. The results revealed that increasing the sample size, differential-expression rate and decreasing the dispersion parameter and number of groups lead to an increase in classification accuracy. Similar with differential-expression studies, the classification of RNA-Seq data requires careful attention when handling data overdispersion. We conclude that, as a count

  5. Classification of the web

    DEFF Research Database (Denmark)

    Mai, Jens Erik

    2004-01-01

    This paper discusses the challenges faced by investigations into the classification of the Web and outlines inquiries that are needed to use principles for bibliographic classification to construct classifications of the Web. This paper suggests that the classification of the Web meets challenges...... that call for inquiries into the theoretical foundation of bibliographic classification theory....

  6. Security classification of information

    Energy Technology Data Exchange (ETDEWEB)

    Quist, A.S.

    1993-04-01

    This document is the second of a planned four-volume work that comprehensively discusses the security classification of information. The main focus of Volume 2 is on the principles for classification of information. Included herein are descriptions of the two major types of information that governments classify for national security reasons (subjective and objective information), guidance to use when determining whether information under consideration for classification is controlled by the government (a necessary requirement for classification to be effective), information disclosure risks and benefits (the benefits and costs of classification), standards to use when balancing information disclosure risks and benefits, guidance for assigning classification levels (Top Secret, Secret, or Confidential) to classified information, guidance for determining how long information should be classified (classification duration), classification of associations of information, classification of compilations of information, and principles for declassifying and downgrading information. Rules or principles of certain areas of our legal system (e.g., trade secret law) are sometimes mentioned to .provide added support to some of those classification principles.

  7. Understanding the use of standardized nursing terminology and classification systems in published research: A case study using the International Classification for Nursing Practice(®).

    Science.gov (United States)

    Strudwick, Gillian; Hardiker, Nicholas R

    2016-10-01

    In the era of evidenced based healthcare, nursing is required to demonstrate that care provided by nurses is associated with optimal patient outcomes, and a high degree of quality and safety. The use of standardized nursing terminologies and classification systems are a way that nursing documentation can be leveraged to generate evidence related to nursing practice. Several widely-reported nursing specific terminologies and classifications systems currently exist including the Clinical Care Classification System, International Classification for Nursing Practice(®), Nursing Intervention Classification, Nursing Outcome Classification, Omaha System, Perioperative Nursing Data Set and NANDA International. However, the influence of these systems on demonstrating the value of nursing and the professions' impact on quality, safety and patient outcomes in published research is relatively unknown. This paper seeks to understand the use of standardized nursing terminology and classification systems in published research, using the International Classification for Nursing Practice(®) as a case study. A systematic review of international published empirical studies on, or using, the International Classification for Nursing Practice(®) were completed using Medline and the Cumulative Index for Nursing and Allied Health Literature. Since 2006, 38 studies have been published on the International Classification for Nursing Practice(®). The main objectives of the published studies have been to validate the appropriateness of the classification system for particular care areas or populations, further develop the classification system, or utilize it to support the generation of new nursing knowledge. To date, most studies have focused on the classification system itself, and a lesser number of studies have used the system to generate information about the outcomes of nursing practice. Based on the published literature that features the International Classification for Nursing

  8. [Current aspects of harmonization of classification of occupational hearing loss].

    Science.gov (United States)

    Pankova, V B; Sinëva, E L; Tavartkiladze, G A; Fedina, I N; Preobrazhenskaia, E A; Mukhamedova, G R

    2013-01-01

    The objective of the present work was to develop unified criteria for the evaluation of the severity of noise-induced hearing loss. Current approaches to taking expert decisions based on the results of medical examination of the patients with impaired hearing are substantially different due to the considerable difference between the criteria for the estimation of hearing envisaged by the international classification, occupational pathology classification, and the national system of medico-social expertise. We undertook an attempt to correct and harmonize the existing criteria for the estimation of severity of occupational hearing loss based on the integration of all the three classification in order to "reduce them to a common denominator" and thereby to ensure the basis for the unified diagnostic and expert decisions for the cases of hearing impairment of any etiology. The project proposed in this paper makes it possible to use unified criteria for the assessment of the degree of hearing loss caused by occupational noises for diagnostic purposes and expertise compatible with the internationally accepted approaches.

  9. Comparisons and Selections of Features and Classifiers for Short Text Classification

    Science.gov (United States)

    Wang, Ye; Zhou, Zhi; Jin, Shan; Liu, Debin; Lu, Mi

    2017-10-01

    Short text is considerably different from traditional long text documents due to its shortness and conciseness, which somehow hinders the applications of conventional machine learning and data mining algorithms in short text classification. According to traditional artificial intelligence methods, we divide short text classification into three steps, namely preprocessing, feature selection and classifier comparison. In this paper, we have illustrated step-by-step how we approach our goals. Specifically, in feature selection, we compared the performance and robustness of the four methods of one-hot encoding, tf-idf weighting, word2vec and paragraph2vec, and in the classification part, we deliberately chose and compared Naive Bayes, Logistic Regression, Support Vector Machine, K-nearest Neighbor and Decision Tree as our classifiers. Then, we compared and analysed the classifiers horizontally with each other and vertically with feature selections. Regarding the datasets, we crawled more than 400,000 short text files from Shanghai and Shenzhen Stock Exchanges and manually labeled them into two classes, the big and the small. There are eight labels in the big class, and 59 labels in the small class.

  10. Added sugars: Definitions, classifications, metabolism and health implications

    Directory of Open Access Journals (Sweden)

    Tailane SCAPIN

    Full Text Available ABSTRACT The sugars added to foods have been featured in recent scientific research, including the publication of the World Health Organization recommendation to limit consumption of added sugars, based on studies on weight gain and dental caries. However, it is possible that there is evidence of an association between excessive consumption and other pathologies, but scientific studies have yet to investigate these associations. Moreover, there is no consensus on the descriptions and definitions of these sugars, with several terms and components used to designate them. In Brazil, there are few studies investigating added sugars, identifying a lack of discussion on this subject. This paper presents a literature review of sugars added to foods, from their definitions and classifications to the metabolism and health effects. The search was performed without limiting dates in the following databases: Web of Science, Scopus, PubMed and SciELO, as well as in national and international official sites. Keywords in Portuguese and English related to sugars added to foods were used, in combination with terms related to systematic review and meta-analysis studies, in order to find research linking added sugars consumption with health damage. The literature indicates that there is a relationship between excessive consumption of added sugars and various health outcomes, including weight gain, type 2 diabetes Mellitus, cancer, and cardiovascular diseases. The different descriptions of sugars in foods may confuse both food consumers and researchers, since each term includes different components. Thus, it is suggested to use the standardized term “added sugar” as the most suitable term for the broader population to understand, because it indicates that those sugars are not natural food components.

  11. The stratification of severity of acute radiation proctopathy after radiotherapy for cervical carcinoma using diffusion-weighted MRI

    Energy Technology Data Exchange (ETDEWEB)

    Li, Xiang Sheng, E-mail: lxsheng500@163.com [Department of Radiology, Air Force General Hospital of People' s Liberation Army, Beijing 100142 (China); Fang, Hong, E-mail: hongfang196808@sina.com [Department of Radiology, Air Force General Hospital of People' s Liberation Army, Beijing 100142 (China); Song, Yunlong, E-mail: yunlongsong010@sina.com [Department of Radiology, Air Force General Hospital of People' s Liberation Army, Beijing 100142 (China); Li, Dechang, E-mail: dechangli1972@sina.com [Department of Pathology, Air Force General Hospital of People' s Liberation Army, Beijing 100142 (China); Wang, Yingjie, E-mail: wangyj19710813@sina.com [Department of Radiotherapy, Air Force General Hospital of People' s Liberation Army, Beijing 100142 (China); Zhu, Hongxian, E-mail: hongxian0102@sina.cn [Department of Radiology, Air Force General Hospital of People' s Liberation Army, Beijing 100142 (China); Meng, Limin, E-mail: liminmeng1977@sina.com [Department of Radiology, Air Force General Hospital of People' s Liberation Army, Beijing 100142 (China); Wang, Ping, E-mail: pingwang1978@sina.com [Department of Radiology, Air Force General Hospital of People' s Liberation Army, Beijing 100142 (China); Wang, Dong, E-mail: dongwang1964@sina.com [Department of Radiology, Air Force General Hospital of People' s Liberation Army, Beijing 100142 (China); Fan, Hongxia, E-mail: fanhongxia1975@sina.com [Department of Radiology, Air Force General Hospital of People' s Liberation Army, Beijing 100142 (China)

    2017-02-15

    Objective: To determine whether diffusion-weighted imaging (DWI) can be used for quantitatively evaluating severity of acute radiation proctopathy after radiotherapy for cervical carcinoma. Materials and methods: One hundred and twenty-four patients with cervical carcinoma underwent MR examination including DWI before and after radiotherapy. Acute radiation proctopathy was classified into three groups (grade 0, grade I–II and grade III–IV) according to Toxicity Criteria of the Radiation Therapy Oncology Group (RTOG). The pretreatment ADC (ADC{sub pre}), ADC after treatment (ADC{sub post}) and ADC change (ΔADC) were compared among three groups. In addition, acute radiation proctopathy was classified into good-prognosis group and poor-prognosis group. ADC{sub pre}, ADC{sub post} and ΔADC were compared between two groups. For DWI parameter that had significant difference, discriminatory capability of the parameter was determined using receiver operating characteristics (ROC) analysis. Results: ADC{sub post} and ΔADC were higher in grade I–II group than in grade 0 group (p < 0.05), yielding a sensitivity of 79.3% and specificity of 69.4% for ADC{sub post}, and 85.1%, 72.3% for ΔADC for discrimination between two groups. ADC{sub post} and ΔADC were higher in grade III–IV group than in grade I–II group (p < 0.05), yielding a sensitivity of 80.3% and specificity of 72.5% for ADC{sub post}, and 84.1%, 74.5% for ΔADC for discrimination between two groups. ADC{sub post} and ΔADC were higher in poor-prognosis group than in good-prognosis group (p < 0.05), yielding a sensitivity of 79.5% and specificity of 73.4% for ADC{sub post}, and 87.2%, 78.3% for ΔADC for discrimination between two groups. Conclusion: Diffusion-weighted MRI can be used for quantitative stratification of severity of acute radiation proctopathy, which serves as an important basis for appropriate timely adjustment of radiotherapy for cervical carcinoma in order to maximally reduce the

  12. Are the determinants of vertebral endplate changes and severe disc degeneration in the lumbar spine the same? A magnetic resonance imaging study in middle-aged male workers

    Directory of Open Access Journals (Sweden)

    Korpelainen Raija

    2008-04-01

    Full Text Available Abstract Background Modic changes are bone marrow lesions visible in magnetic resonance imaging (MRI, and they are assumed to be associated with symptomatic intervertebral disc disease, especially changes located at L5-S1. Only limited information exists about the determinants of Modic changes. The objective of this study was to evaluate the determinants of vertebral endplate (Modic changes, and whether they are similar for Modic changes and severe disc degeneration focusing on L5-S1 level. Methods 228 middle-aged male workers (159 train engineers and 69 sedentary factory workers from northern Finland underwent sagittal T1- and T2-weighted MRI. Modic changes and disc degeneration were analyzed from the scans. The participants responded to a questionnaire including items of occupational history and lifestyle factors. Logistic regression analysis was used to evaluate the associations between selected determinants (age, lifetime exercise, weight-related factors, fat percentage, smoking, alcohol use, lifetime whole-body vibration and Modic type I and II changes, and severe disc degeneration (= grade V on Pfirrmann's classification. Results The prevalences of the Modic changes and severe disc degeneration were similar in the occupational groups. Age was significantly associated with all degenerative changes. In the age-adjusted analyses, only weight-related determinants (BMI, waist circumference were associated with type II changes. Exposure to whole-body vibration, besides age, was the only significant determinant for severe disc degeneration. In the multivariate model, BMI was associated with type II changes at L5-S1 (OR 2.75 per one SD = 3 unit increment in BMI, and vibration exposure with severe disc degeneration at L5-S1 (OR 1.08 per one SD = 11-year increment in vibration exposure. Conclusion Besides age, weight-related factors seem important in the pathogenesis of Modic changes, whereas whole-body vibration was the only significant determinant

  13. Hazard classification methodology

    International Nuclear Information System (INIS)

    Brereton, S.J.

    1996-01-01

    This document outlines the hazard classification methodology used to determine the hazard classification of the NIF LTAB, OAB, and the support facilities on the basis of radionuclides and chemicals. The hazard classification determines the safety analysis requirements for a facility

  14. Comparison between Possibilistic c-Means (PCM and Artificial Neural Network (ANN Classification Algorithms in Land use/ Land cover Classification

    Directory of Open Access Journals (Sweden)

    Ganchimeg Ganbold

    2017-03-01

    Full Text Available There are several statistical classification algorithms available for landuse/land cover classification. However, each has a certain bias orcompromise. Some methods like the parallel piped approach in supervisedclassification, cannot classify continuous regions within a feature. Onthe other hand, while unsupervised classification method takes maximumadvantage of spectral variability in an image, the maximally separableclusters in spectral space may not do much for our perception of importantclasses in a given study area. In this research, the output of an ANNalgorithm was compared with the Possibilistic c-Means an improvementof the fuzzy c-Means on both moderate resolutions Landsat8 and a highresolution Formosat 2 images. The Formosat 2 image comes with an8m spectral resolution on the multispectral data. This multispectral imagedata was resampled to 10m in order to maintain a uniform ratio of1:3 against Landsat 8 image. Six classes were chosen for analysis including:Dense forest, eucalyptus, water, grassland, wheat and riverine sand. Using a standard false color composite (FCC, the six features reflecteddifferently in the infrared region with wheat producing the brightestpixel values. Signature collection per class was therefore easily obtainedfor all classifications. The output of both ANN and FCM, were analyzedseparately for accuracy and an error matrix generated to assess the qualityand accuracy of the classification algorithms. When you compare theresults of the two methods on a per-class-basis, ANN had a crisperoutput compared to PCM which yielded clusters with pixels especiallyon the moderate resolution Landsat 8 imagery.

  15. Pre-pregnancy body mass index and gestational weight gain in Thai pregnant women as risks for low birth weight and macrosomia.

    Science.gov (United States)

    Pongcharoen, Tippawan; Gowachirapant, Sueppong; Wecharak, Purisa; Sangket, Natnaree; Winichagoon, Pattanee

    2016-12-01

    Maternal pre-pregnancy body mass index (BMI) and gestational weight gain (GWG) have been reported to be associated with pregnancy outcomes. Due to the nutrition transition in Thailand, the double burden of malnutrition is increasing and this may have negative consequences on birth outcomes. This study aimed to investigate the relationship between pre-pregnancy BMI and GWG with the risks of low birth weight and macrosomia. We performed a secondary analysis of data obtained from an iodine supplementation trial in mildly iodine-deficient Thai pregnant women. Pre-pregnancy BMI was classified using the WHO classification. GWG was categorized using the IOM recommendation. Binary and multinomial logistic regressions were performed. Among 378 pregnant women, the prevalence of pre-pregnancy underweight (BMI=25 kg/m2) were 17.2% and 14.3%, respectively. Normal weight women had the highest median GWG [15.0 (12.0, 19.0) kg] when compared to overweight women [13.2 (9.0, 16.3) kg]. Forty-one percent of women had excessive GWG, while 23% of women gained weight inadequately. Women with a high pre-pregnancy BMI had a 7-fold higher risk of having a macrosomic infant. Women who had excessive GWG were 8 times more likely to deliver a newborn with macrosomia. Both high pre-pregnancy maternal weight and excessive weight gain during pregnancy increase risk of infant macrosomia. Therefore, maintaining normal body weight before and throughout pregnancy should be recommended in order to reduce the risk of excessive infant birth weight and its associated complications.

  16. Brief Report: Concurrent Validity of Autism Symptom Severity Measures

    Science.gov (United States)

    Reszka, Stephanie S.; Boyd, Brian A.; McBee, Matthew; Hume, Kara A.; Odom, Samuel L.

    2014-01-01

    The autism spectrum disorder (ASD) diagnostic classifications, according to the DSM-5, include a severity rating. Several screening and/or diagnostic measures, such as the autism diagnostic and observation schedule (ADOS), Childhood Autism Rating Scale (CARS) and social responsiveness scale (SRS) (teacher and parent versions), include an…

  17. Weight loss methods and changes in eating habits among successful weight losers.

    Science.gov (United States)

    Soini, Sirpa; Mustajoki, Pertti; Eriksson, Johan G

    2016-01-01

    Changes in several lifestyle related factors are required for successful long-term weight loss. Identification of these factors is of major importance from a public health point of view. This study was based upon findings from the Finnish Weight Control Registry (FWCR), a web-based registry. In total, 316 people were recruited and 184 met the study inclusion criteria. The aims of this study were to assess means and typical changes in eating habits associated with successful long-term weight loss. Half of the participants (48%) reported that they lost weight slowly primarily with dietary changes. Self-weighing frequency was high, 92% was weighing themselves at least once a week during the weight loss phase, and 75% during the maintenance phase. Dietary aspects associated with successful weight loss and weight maintenance included an increase in intake of vegetables, a reduction in frequency of eating candies and fast food, regular meal frequency and application of the Plate model. Both slow and fast weight loss may lead to successful long-term results and weight maintenance. A decrease in energy intake was achieved by reducing intake of energy-dense food, applying the Plate model and by regular meal frequency. Key messages Successful long-term weight loss is associated with a reduction in intake of energy-dense food. A more regular meal frequency and a high frequency of self-weighing seem to be helpful.

  18. Ethnic variation in validity of classification of overweight and obesity using self-reported weight and height in American women and men: the Third National Health and Nutrition Examination Survey

    Directory of Open Access Journals (Sweden)

    Sempos Christopher T

    2005-10-01

    Full Text Available Abstract Background Few data have been published on the validity of classification of overweight and obesity based on self-reported weight in representative samples of Hispanic as compared to other American populations despite the wide use of such data. Objective To test the null hypothesis that ethnicity is unrelated to bias of mean body mass index (BMI and to sensitivity of overweight or obesity (BMI >= 25 kg/m2 derived from self-reported (SR versus measured weight and height using measured BMI as the gold standard. Design Cross-sectional survey of a large national sample, the Third National Health and Nutrition Examination Survey (NHANES III conducted in 1988–1994. Participants American men and women aged 20 years and over (n = 15,025. Measurements SR height, weight, cigarette smoking, health status, and socio-demographic variables from home interview and measured weight and height. Results In women and Mexican American (MA men SR BMI underestimated true prevalence rates of overweight or obesity. For other men, no consistent difference was seen. Sensitivity of SR was similar in non-Hispanic European Americans (EA and non-Hispanic African Americans (AA but much lower in MA. Prevalence of obesity (BMI >= 30 kg/m2 is consistently underestimated by self-report, the gap being greater for MA than for other women, but similar for MA and other men. The mean difference between self-reported and measured BMI was greater in MA (men -0.37, women -0.76 kg/m2 than in non-Hispanic EA (men -0.22, women -0.62 kg/m2. In a regression model with the difference between self-reported and measured BMI as the dependent variable, MA ethnicity was a significant (p Conclusion Under-estimation of the prevalence of overweight or obesity based on height and weight self-reported at interview varied significantly among ethnic groups independent of other variables.

  19. Automated egg grading system using computer vision: Investigation on weight measure versus shape parameters

    Science.gov (United States)

    Nasir, Ahmad Fakhri Ab; Suhaila Sabarudin, Siti; Majeed, Anwar P. P. Abdul; Ghani, Ahmad Shahrizan Abdul

    2018-04-01

    Chicken egg is a source of food of high demand by humans. Human operators cannot work perfectly and continuously when conducting egg grading. Instead of an egg grading system using weight measure, an automatic system for egg grading using computer vision (using egg shape parameter) can be used to improve the productivity of egg grading. However, early hypothesis has indicated that more number of egg classes will change when using egg shape parameter compared with using weight measure. This paper presents the comparison of egg classification by the two above-mentioned methods. Firstly, 120 images of chicken eggs of various grades (A–D) produced in Malaysia are captured. Then, the egg images are processed using image pre-processing techniques, such as image cropping, smoothing and segmentation. Thereafter, eight egg shape features, including area, major axis length, minor axis length, volume, diameter and perimeter, are extracted. Lastly, feature selection (information gain ratio) and feature extraction (principal component analysis) are performed using k-nearest neighbour classifier in the classification process. Two methods, namely, supervised learning (using weight measure as graded by egg supplier) and unsupervised learning (using egg shape parameters as graded by ourselves), are conducted to execute the experiment. Clustering results reveal many changes in egg classes after performing shape-based grading. On average, the best recognition results using shape-based grading label is 94.16% while using weight-based label is 44.17%. As conclusion, automated egg grading system using computer vision is better by implementing shape-based features since it uses image meanwhile the weight parameter is more suitable by using weight grading system.

  20. Tongue Images Classification Based on Constrained High Dispersal Network

    Directory of Open Access Journals (Sweden)

    Dan Meng

    2017-01-01

    Full Text Available Computer aided tongue diagnosis has a great potential to play important roles in traditional Chinese medicine (TCM. However, the majority of the existing tongue image analyses and classification methods are based on the low-level features, which may not provide a holistic view of the tongue. Inspired by deep convolutional neural network (CNN, we propose a novel feature extraction framework called constrained high dispersal neural networks (CHDNet to extract unbiased features and reduce human labor for tongue diagnosis in TCM. Previous CNN models have mostly focused on learning convolutional filters and adapting weights between them, but these models have two major issues: redundancy and insufficient capability in handling unbalanced sample distribution. We introduce high dispersal and local response normalization operation to address the issue of redundancy. We also add multiscale feature analysis to avoid the problem of sensitivity to deformation. Our proposed CHDNet learns high-level features and provides more classification information during training time, which may result in higher accuracy when predicting testing samples. We tested the proposed method on a set of 267 gastritis patients and a control group of 48 healthy volunteers. Test results show that CHDNet is a promising method in tongue image classification for the TCM study.

  1. The Predisposing Factors between Dental Caries and Deviations from Normal Weight.

    Science.gov (United States)

    Chopra, Amandeep; Rao, Nanak Chand; Gupta, Nidhi; Vashisth, Shelja; Lakhanpal, Manav

    2015-04-01

    Dental caries and deviations from normal weight are two conditions which share several broadly predisposing factors. So it's important to understand any relationship between dental state and body weight if either is to be managed appropriately. The study was done to find out the correlation between body mass index (BMI), diet, and dental caries among 12-15-year-old schoolgoing children in Panchkula District. A multistage sample of 12-15-year-old school children (n = 810) in Panchkula district, Haryana was considered. Child demographic details and diet history for 5 days was recorded. Data regarding dental caries status was collected using World Health Organization (1997) format. BMI was calculated and categorized according to the World Health Organization classification system for BMI. The data were subjected to statistical analysis using chi-square test and binomial regression developed using the Statistical Package for Social Sciences (SPSS) 20.0. The mean Decayed Missing Filled Teeth (DMFT) score was found to be 1.72 with decayed, missing, and filled teeth to be 1.22, 0.04, and 0.44, respectively. When the sample was assessed based on type of diet, it was found that vegetarians had higher mean DMFT (1.72) as compared to children having mixed diet. Overweight children had highest DMFT (3.21) which was followed by underweight (2.31) and obese children (2.23). Binomial regression revealed that females were 1.293 times at risk of developing caries as compared to males. Fair and poor Simplified-Oral Hygiene Index (OHI-S) showed 3.920 and 4.297 times risk of developing caries as compared to good oral hygiene, respectively. Upper high socioeconomic status (SES) is at most risk of developing caries. Underweight, overweight, and obese are at 2.7, 2.5, and 3 times risk of developing caries as compared to children with normal BMI, respectively. Dental caries and deviations from normal weight are two conditions which share several broadly predisposing factors such as diet

  2. Artificial intelligence in sports on the example of weight training.

    Science.gov (United States)

    Novatchkov, Hristo; Baca, Arnold

    2013-01-01

    The overall goal of the present study was to illustrate the potential of artificial intelligence (AI) techniques in sports on the example of weight training. The research focused in particular on the implementation of pattern recognition methods for the evaluation of performed exercises on training machines. The data acquisition was carried out using way and cable force sensors attached to various weight machines, thereby enabling the measurement of essential displacement and force determinants during training. On the basis of the gathered data, it was consequently possible to deduce other significant characteristics like time periods or movement velocities. These parameters were applied for the development of intelligent methods adapted from conventional machine learning concepts, allowing an automatic assessment of the exercise technique and providing individuals with appropriate feedback. In practice, the implementation of such techniques could be crucial for the investigation of the quality of the execution, the assistance of athletes but also coaches, the training optimization and for prevention purposes. For the current study, the data was based on measurements from 15 rather inexperienced participants, performing 3-5 sets of 10-12 repetitions on a leg press machine. The initially preprocessed data was used for the extraction of significant features, on which supervised modeling methods were applied. Professional trainers were involved in the assessment and classification processes by analyzing the video recorded executions. The so far obtained modeling results showed good performance and prediction outcomes, indicating the feasibility and potency of AI techniques in assessing performances on weight training equipment automatically and providing sportsmen with prompt advice. Key pointsArtificial intelligence is a promising field for sport-related analysis.Implementations integrating pattern recognition techniques enable the automatic evaluation of data

  3. MRI histogram analysis enables objective and continuous classification of intervertebral disc degeneration.

    Science.gov (United States)

    Waldenberg, Christian; Hebelka, Hanna; Brisby, Helena; Lagerstrand, Kerstin Magdalena

    2018-05-01

    Magnetic resonance imaging (MRI) is the best diagnostic imaging method for low back pain. However, the technique is currently not utilized in its full capacity, often failing to depict painful intervertebral discs (IVDs), potentially due to the rough degeneration classification system used clinically today. MR image histograms, which reflect the IVD heterogeneity, may offer sensitive imaging biomarkers for IVD degeneration classification. This study investigates the feasibility of using histogram analysis as means of objective and continuous grading of IVD degeneration. Forty-nine IVDs in ten low back pain patients (six males, 25-69 years) were examined with MRI (T2-weighted images and T2-maps). Each IVD was semi-automatically segmented on three mid-sagittal slices. Histogram features of the IVD were extracted from the defined regions of interest and correlated to Pfirrmann grade. Both T2-weighted images and T2-maps displayed similar histogram features. Histograms of well-hydrated IVDs displayed two separate peaks, representing annulus fibrosus and nucleus pulposus. Degenerated IVDs displayed decreased peak separation, where the separation was shown to correlate strongly with Pfirrmann grade (P histogram appearances. Histogram features correlated well with IVD degeneration, suggesting that IVD histogram analysis is a suitable tool for objective and continuous IVD degeneration classification. As histogram analysis revealed IVD heterogeneity, it may be a clinical tool for characterization of regional IVD degeneration effects. To elucidate the usefulness of histogram analysis in patient management, IVD histogram features between asymptomatic and symptomatic individuals needs to be compared.

  4. Fluid mechanics based classification of the respiratory efficiency of several nasal cavities.

    Science.gov (United States)

    Lintermann, Andreas; Meinke, Matthias; Schröder, Wolfgang

    2013-11-01

    The flow in the human nasal cavity is of great importance to understand rhinologic pathologies like impaired respiration or heating capabilities, a diminished sense of taste and smell, and the presence of dry mucous membranes. To numerically analyze this flow problem a highly efficient and scalable Thermal Lattice-BGK (TLBGK) solver is used, which is very well suited for flows in intricate geometries. The generation of the computational mesh is completely automatic and highly parallelized such that it can be executed efficiently on High Performance Computers (HPCs). An evaluation of the functionality of nasal cavities is based on an analysis of pressure drop, secondary flow structures, wall-shear stress distributions, and temperature variations from the nostrils to the pharynx. The results of the flow fields of three completely different nasal cavities allow their classification into ability groups and support the a priori decision process on surgical interventions. © 2013 Elsevier Ltd. All rights reserved.

  5. Tremor entities and their classification: an update.

    Science.gov (United States)

    Gövert, Felix; Deuschl, Günther

    2015-08-01

    This review focuses on important new findings in the field of tremor and illustrates the consequences for the current definition and classification of tremor. Since 1998 when the consensus criteria for tremor were proposed, new variants of tremors and new diagnostic methods were discovered that have changed particularly the concepts of essential tremor and dystonic tremor. Accumulating evidence exists that essential tremor is not a single entity rather different conditions that share the common symptom action tremor without other major abnormalities. Tremor is a common feature in patients with adult-onset focal dystonia and may involve several different body parts and forms of tremor. Recent advances, in particular, in the field of genetics, suggest that dystonic tremor may even be present without overt dystonia. Monosymptomatic asymmetric rest and postural tremor has been further delineated, and apart from tremor-dominant Parkinson's disease, there are several rare conditions including rest and action tremor with normal dopamine transporter imaging (scans without evidence of dopaminergic deficit) and essential tremor with tremor at rest. Increasing knowledge in the last decades changed the view on tremors and highlights several caveats in the current tremor classification. Given the ambiguous assignment between tremor phenomenology and tremor etiology, a more cautious definition of tremors on the basis of clinical assessment data is needed.

  6. Excess body weight in children may increase the length of hospital stay

    Directory of Open Access Journals (Sweden)

    Maria Teresa Bechere Fernandes

    2015-02-01

    Full Text Available OBJECTIVES: To investigate the prevalence of excess body weight in the pediatric ward of University Hospital and to test both the association between initial nutritional diagnosis and the length of stay and the in-hospital variation in nutritional status. METHODS: Retrospective cohort study based on information entered in clinical records from University Hospital. The data were collected from a convenience sample of 91 cases among children aged one to 10 years admitted to the hospital in 2009. The data that characterize the sample are presented in a descriptive manner. Additionally, we performed a multivariate linear regression analysis adjusted for age and gender. RESULTS: Nutritional classification at baseline showed that 87.8% of the children had a normal weight and that 8.9% had excess weight. The linear regression models showed that the average weight loss z-score of the children with excess weight compared with the group with normal weight was −0.48 (p = 0.018 and that their length of stay was 2.37 days longer on average compared with that of the normal-weight group (p = 0.047. CONCLUSIONS: The length of stay and loss of weight at the hospital may be greater among children with excess weight than among children with normal weight.

  7. A Classification System for Hospital-Based Infection Outbreaks

    Directory of Open Access Journals (Sweden)

    Paul S. Ganney

    2010-01-01

    Full Text Available Outbreaks of infection within semi-closed environments such as hospitals, whether inherent in the environment (such as Clostridium difficile (C.Diff or Methicillinresistant Staphylococcus aureus (MRSA or imported from the wider community (such as Norwalk-like viruses (NLVs, are difficult to manage. As part of our work on modelling such outbreaks, we have developed a classification system to describe the impact of a particular outbreak upon an organization. This classification system may then be used in comparing appropriate computer models to real outbreaks, as well as in comparing different real outbreaks in, for example, the comparison of differing management and containment techniques and strategies. Data from NLV outbreaks in the Hull and East Yorkshire Hospitals NHS Trust (the Trust over several previous years are analysed and classified, both for infection within staff (where the end of infection date may not be known and within patients (where it generally is known. A classification system consisting of seven elements is described, along with a goodness-of-fit method for comparing a new classification to previously known ones, for use in evaluating a simulation against history and thereby determining how ‘realistic’ (or otherwise it is.

  8. A classification system for hospital-based infection outbreaks.

    Science.gov (United States)

    Ganney, Paul S; Madeo, Maurice; Phillips, Roger

    2010-12-01

    Outbreaks of infection within semi-closed environments such as hospitals, whether inherent in the environment (such as Clostridium difficile (C.Diff) or Methicillin-resistant Staphylococcus aureus (MRSA) or imported from the wider community (such as Norwalk-like viruses (NLVs)), are difficult to manage. As part of our work on modelling such outbreaks, we have developed a classification system to describe the impact of a particular outbreak upon an organization. This classification system may then be used in comparing appropriate computer models to real outbreaks, as well as in comparing different real outbreaks in, for example, the comparison of differing management and containment techniques and strategies. Data from NLV outbreaks in the Hull and East Yorkshire Hospitals NHS Trust (the Trust) over several previous years are analysed and classified, both for infection within staff (where the end of infection date may not be known) and within patients (where it generally is known). A classification system consisting of seven elements is described, along with a goodness-of-fit method for comparing a new classification to previously known ones, for use in evaluating a simulation against history and thereby determining how 'realistic' (or otherwise) it is.

  9. Classification, disease, and diagnosis.

    Science.gov (United States)

    Jutel, Annemarie

    2011-01-01

    Classification shapes medicine and guides its practice. Understanding classification must be part of the quest to better understand the social context and implications of diagnosis. Classifications are part of the human work that provides a foundation for the recognition and study of illness: deciding how the vast expanse of nature can be partitioned into meaningful chunks, stabilizing and structuring what is otherwise disordered. This article explores the aims of classification, their embodiment in medical diagnosis, and the historical traditions of medical classification. It provides a brief overview of the aims and principles of classification and their relevance to contemporary medicine. It also demonstrates how classifications operate as social framing devices that enable and disable communication, assert and refute authority, and are important items for sociological study.

  10. Added value of prone CT in the assessment of honeycombing and classification of usual interstitial pneumonia pattern.

    Science.gov (United States)

    Kim, Minjae; Lee, Sang Min; Song, Jae-Woo; Do, Kyung-Hyun; Lee, Hyun Joo; Lim, Soyeoun; Choe, Jooae; Park, Kye Jin; Park, Hyo Jung; Kim, Hwa Jung; Seo, Joon Beom

    2017-06-01

    To retrospectively investigate whether prone CT improves identification of honeycombing and classification of UIP patterns in terms of interobserver agreement and accuracy using pathological results as a reference standard. Institutional review board approval with waiver of patients' informed consent requirement was obtained. HRCTs of 86 patients with pathologically proven UIP, NSIP and chronic HP between January 2011 and April 2015 were evaluated by 8 observers. Observers were asked to review supine only set and supine and prone combined set and determine the presence of honeycombing and UIP classification (UIP, possible UIP, inconsistent with UIP). The diagnosis was regarded as correct when UIP pattern on CT corresponded to pathological UIP. Interobserver agreement of honeycombing identification among radiologists was only fair on the supine and combined set (weighted κ=0.31 and 0.34). Additional review of prone images demonstrated a significant improvement in interobserver agreement (weighted κ) of UIP classification from 0.25 to 0.33. Prone CT conferred a significant improvement in interobserver agreement of UIP classification for trainee radiologists (from 0.10 to 0.34) while no improvement was found for board-certified radiologists (from 0.35 to 0.31). There were no significant differences in the accuracy of UIP pattern with reference to pathological results between the supine and combined set (78.8% (145/184) and 81.3% (179/220), P=0.612). Additional review of prone CT can improve overall interobserver agreement of UIP classification among radiologists with variable experiences, particularly for less experienced radiologists, while no improvement was found in honeycombing identification. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. What is new in genetics and osteogenesis imperfecta classification?

    Directory of Open Access Journals (Sweden)

    Eugênia R. Valadares

    2014-12-01

    Full Text Available OBJECTIVE: Literature review of new genes related to osteogenesis imperfecta (OI and update of its classification. SOURCES: Literature review in the PubMed and OMIM databases, followed by selection of relevant references. SUMMARY OF THE FINDINGS: In 1979, Sillence et al. developed a classification of OI subtypes based on clinical features and disease severity: OI type I, mild, common, with blue sclera; OI type II, perinatal lethal form; OI type III, severe and progressively deforming, with normal sclera; and OI type IV, moderate severity with normal sclera. Approximately 90% of individuals with OI are heterozygous for mutations in the COL1A1 and COL1A2 genes, with dominant pattern of inheritance or sporadic mutations. After 2006, mutations were identified in the CRTAP, FKBP10, LEPRE1, PLOD2, PPIB, SERPINF1, SERPINH1, SP7, WNT1, BMP1, and TMEM38B genes, associated with recessive OI and mutation in the IFITM5 gene associated with dominant OI. Mutations in PLS3 were recently identified in families with osteoporosis and fractures, with X-linked inheritance pattern. In addition to the genetic complexity of the molecular basis of OI, extensive phenotypic variability resulting from individual loci has also been documented. CONCLUSIONS: Considering the discovery of new genes and limited genotype-phenotype correlation, the use of next-generation sequencing tools has become useful in molecular studies of OI cases. The recommendation of the Nosology Group of the International Society of Skeletal Dysplasias is to maintain the classification of Sillence as the prototypical form, universally accepted to classify the degree of severity in OI, while maintaining it free from direct molecular reference.

  12. A simplified classification system for partially edentulous spaces

    Directory of Open Access Journals (Sweden)

    Bhandari Aruna J, Bhandari Akshay J

    2014-04-01

    Full Text Available Background: There is no single universally employed classification system that will specify the exact edentulous situation. Several classification systems exist to group the situation and avoid confusion. Classifications based on edentulous areas, finished restored prostheses, type of direct retainers or fulcrum lines are there. Some are based depending on the placement of the implants. Widely accepted Kennedy Applegate classification does not give any idea about length, span or number of teeth missing. Rule 6 governing the application of Kennedy method states that additional edentulous areas are referred as modification number 1,2 etc. Rule 7 states that extent of the modification is not considered; only the number of edentulous areas is considered. Hence there is a need to modify the Kennedy –Applegate System. Aims: This new classification system is an attempt to modify Kennedy –Applegate System so as to give the exact idea about missing teeth, space, span, side and areas of partially edentulous arches. Methods and Material: This system will provide the information regarding Maxillary or Mandibular partially edentulous arches, Left or Right side, length of the edentulous space, number of teeth missing and whether there will be tooth borne or tooth – tissue borne prosthesis. Conclusions: This classification is easy for application, communication and will also help to design the removable cast partial denture in a better logical and systematic way. Also, this system will give the idea of the edentulous status and the number of missing teeth in fixed, hybrid or implant prosthesis.

  13. Compositorial 'Weight' & 'Luminance'

    NARCIS (Netherlands)

    Koenderink, Jan; van Doorn, Andrea J.; Gegenfurtner, Karl

    2017-01-01

    Compositorial weight might be understood as an operational definition of salience. It is not a psychophysical entity, but holds a key position between psychophysics and aesthetics. Several factors ranging over raw photometric/colorimetric parameters, various kinds of psychophysical contrast, image

  14. Assessment of PANC3 Score in Predicting Severity of Acute ...

    African Journals Online (AJOL)

    2017-05-18

    May 18, 2017 ... us in predicting severity at the time of admission but these are time consuming or .... and Acute pancreatitis classification working group)[3] to assess the severity of ... belonged to 30–45 years age group, with mean age of.

  15. Severe cerebral hypovolemia on perfusion CT and lower body weight are associated with parenchymal haemorrhage after thrombolysis

    Energy Technology Data Exchange (ETDEWEB)

    Tsetsou, S.; Eskandari, A.; Michel, P. [Centre Hospitalier Universitaire Vaudois and University of Lausanne CHUV, Department of Neurology, Lausanne (Switzerland); Amiguet, M. [Centre Hospitalier Universitaire Vaudois and University of Lausanne, Institute of Social and Preventive Medicine, Lausanne (Switzerland); Meuli, R.; Maeder, P. [Centre Hospitalier Universitaire Vaudois and University of Lausanne, Department of Radiology, Lausanne (Switzerland); Jiang, B.; Wintermark, M. [Stanford University and Medical Center, Department of Radiology, Neuroradiology Division, Stanford, CA (United States)

    2017-01-15

    Haemorrhagic transformation of acute ischemic stroke (AIS) and particularly parenchymal haemorrhage (PH) remains a feared complication of intravenous thrombolysis (IVT). We aimed to identify clinical and perfusion CT (PCT) variables which are independently associated with PHs. In this observational cohort study, based on the Acute Stroke Registry Analysis of Lausanne (ASTRAL) from 2003 to December 2013, we selected patients with AIS involving the middle cerebral artery (MCA) territory who were thrombolysed within 4.5 h of symptoms' onset and who had a good quality baseline PCT at the beginning of IVT. In addition to demographic, clinical, laboratory and non-contrast CT data, volumes of salvageable tissue and ischemic core on PCT, as well as absolute CBF and CBV values within the ischemic regions were compared in patients with and without PH in multivariate analysis. Of the 190 included patients, 24 (12.6%) presented a PH (11 had PH1 and 13 had PH2). In multivariate analysis of the clinical and radiological variables, the lowest CBV in the core and lower body weight was both significantly associated with PH (p = 0.009 and p = 0.024, respectively). In thrombolysed MCA strokes, maximal hypoperfusion severity depicted by lowest CBV values in the core region and lower body weight are independently correlated with PH. This information, if confirmed in other case series, may add to the stratification of revascularisation decisions in patients with a perceived high PH risk. (orig.)

  16. Classification of scintigrams on the base of an automatic analysis

    International Nuclear Information System (INIS)

    Vidyukov, V.I.; Kasatkin, Yu.N.; Kal'nitskaya, E.F.; Mironov, S.P.; Rotenberg, E.M.

    1980-01-01

    The stages of drawing a discriminative system based on self-education for an automatic analysis of scintigrams have been considered. The results of the classification of 240 scintigrams of the liver into ''normal'', ''diffuse lesions'', ''focal lesions'' have been evaluated by medical experts and computer. The accuracy of the computerized classification was 91.7%, that of the experts-85%. The automatic analysis methods of scintigrams of the liver have been realized using the specialized MDS system of data processing. The quality of the discriminative system has been assessed on 125 scintigrams. The accuracy of the classification is equal to 89.6%. The employment of the self-education; methods permitted one to single out two subclasses depending on the severity of diffuse lesions

  17. Standard classification: Physics

    International Nuclear Information System (INIS)

    1977-01-01

    This is a draft standard classification of physics. The conception is based on the physics part of the systematic catalogue of the Bayerische Staatsbibliothek and on the classification given in standard textbooks. The ICSU-AB classification now used worldwide by physics information services was not taken into account. (BJ) [de

  18. A Spectral-Texture Kernel-Based Classification Method for Hyperspectral Images

    Directory of Open Access Journals (Sweden)

    Yi Wang

    2016-11-01

    Full Text Available Classification of hyperspectral images always suffers from high dimensionality and very limited labeled samples. Recently, the spectral-spatial classification has attracted considerable attention and can achieve higher classification accuracy and smoother classification maps. In this paper, a novel spectral-spatial classification method for hyperspectral images by using kernel methods is investigated. For a given hyperspectral image, the principle component analysis (PCA transform is first performed. Then, the first principle component of the input image is segmented into non-overlapping homogeneous regions by using the entropy rate superpixel (ERS algorithm. Next, the local spectral histogram model is applied to each homogeneous region to obtain the corresponding texture features. Because this step is performed within each homogenous region, instead of within a fixed-size image window, the obtained local texture features in the image are more accurate, which can effectively benefit the improvement of classification accuracy. In the following step, a contextual spectral-texture kernel is constructed by combining spectral information in the image and the extracted texture information using the linearity property of the kernel methods. Finally, the classification map is achieved by the support vector machines (SVM classifier using the proposed spectral-texture kernel. Experiments on two benchmark airborne hyperspectral datasets demonstrate that our method can effectively improve classification accuracies, even though only a very limited training sample is available. Specifically, our method can achieve from 8.26% to 15.1% higher in terms of overall accuracy than the traditional SVM classifier. The performance of our method was further compared to several state-of-the-art classification methods of hyperspectral images using objective quantitative measures and a visual qualitative evaluation.

  19. CLASS-PAIR-GUIDED MULTIPLE KERNEL LEARNING OF INTEGRATING HETEROGENEOUS FEATURES FOR CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    Q. Wang

    2017-10-01

    Full Text Available In recent years, many studies on remote sensing image classification have shown that using multiple features from different data sources can effectively improve the classification accuracy. As a very powerful means of learning, multiple kernel learning (MKL can conveniently be embedded in a variety of characteristics. The conventional combined kernel learned by MKL can be regarded as the compromise of all basic kernels for all classes in classification. It is the best of the whole, but not optimal for each specific class. For this problem, this paper proposes a class-pair-guided MKL method to integrate the heterogeneous features (HFs from multispectral image (MSI and light detection and ranging (LiDAR data. In particular, the one-against-one strategy is adopted, which converts multiclass classification problem to a plurality of two-class classification problem. Then, we select the best kernel from pre-constructed basic kernels set for each class-pair by kernel alignment (KA in the process of classification. The advantage of the proposed method is that only the best kernel for the classification of any two classes can be retained, which leads to greatly enhanced discriminability. Experiments are conducted on two real data sets, and the experimental results show that the proposed method achieves the best performance in terms of classification accuracies in integrating the HFs for classification when compared with several state-of-the-art algorithms.

  20. Consensus classification of posterior cortical atrophy.

    Science.gov (United States)

    Crutch, Sebastian J; Schott, Jonathan M; Rabinovici, Gil D; Murray, Melissa; Snowden, Julie S; van der Flier, Wiesje M; Dickerson, Bradford C; Vandenberghe, Rik; Ahmed, Samrah; Bak, Thomas H; Boeve, Bradley F; Butler, Christopher; Cappa, Stefano F; Ceccaldi, Mathieu; de Souza, Leonardo Cruz; Dubois, Bruno; Felician, Olivier; Galasko, Douglas; Graff-Radford, Jonathan; Graff-Radford, Neill R; Hof, Patrick R; Krolak-Salmon, Pierre; Lehmann, Manja; Magnin, Eloi; Mendez, Mario F; Nestor, Peter J; Onyike, Chiadi U; Pelak, Victoria S; Pijnenburg, Yolande; Primativo, Silvia; Rossor, Martin N; Ryan, Natalie S; Scheltens, Philip; Shakespeare, Timothy J; Suárez González, Aida; Tang-Wai, David F; Yong, Keir X X; Carrillo, Maria; Fox, Nick C

    2017-08-01

    A classification framework for posterior cortical atrophy (PCA) is proposed to improve the uniformity of definition of the syndrome in a variety of research settings. Consensus statements about PCA were developed through a detailed literature review, the formation of an international multidisciplinary working party which convened on four occasions, and a Web-based quantitative survey regarding symptom frequency and the conceptualization of PCA. A three-level classification framework for PCA is described comprising both syndrome- and disease-level descriptions. Classification level 1 (PCA) defines the core clinical, cognitive, and neuroimaging features and exclusion criteria of the clinico-radiological syndrome. Classification level 2 (PCA-pure, PCA-plus) establishes whether, in addition to the core PCA syndrome, the core features of any other neurodegenerative syndromes are present. Classification level 3 (PCA attributable to AD [PCA-AD], Lewy body disease [PCA-LBD], corticobasal degeneration [PCA-CBD], prion disease [PCA-prion]) provides a more formal determination of the underlying cause of the PCA syndrome, based on available pathophysiological biomarker evidence. The issue of additional syndrome-level descriptors is discussed in relation to the challenges of defining stages of syndrome severity and characterizing phenotypic heterogeneity within the PCA spectrum. There was strong agreement regarding the definition of the core clinico-radiological syndrome, meaning that the current consensus statement should be regarded as a refinement, development, and extension of previous single-center PCA criteria rather than any wholesale alteration or redescription of the syndrome. The framework and terminology may facilitate the interpretation of research data across studies, be applicable across a broad range of research scenarios (e.g., behavioral interventions, pharmacological trials), and provide a foundation for future collaborative work. Copyright © 2017 The Authors

  1. Quantitative evaluation of hyperintensity on T1-weighted MRI in liver cirrhosis : correlation with child-pugh classification and hepatic encephalopathy

    International Nuclear Information System (INIS)

    Eun, Hyo Won; Choi, Hye Young; Lee, Sun Wha; Yi, Sun Young

    1999-01-01

    To investigate the differences in signal changes in the globus pallidus and white matter, as seen on T1-weighted MR brain images, and to determine whether these differences can be used as an indicator of subclinical hepatic encephalopathy. A total of 25 cases of liver cirrhosis were evaluated and as a control group, 20 subjects were also studied. Using a 1.5T MRI scannet, brain MR images were obtained, and the differences in signal intensity in both the globus pallidus and thalamus and in both white and gray matter were then quantified using the contrast to noise ratio(CNR). On the basis of the Child-Pugh classification, 25patients with liver cirrhosis were divided into three groups, with eight in group A, eight in B, and nine in C. Using clinical criteria, hepatic encephalopathy was diagnosed in seven of the 25 patients. There after, CNRs(CNR1 and CNR2) were conpared between the control and cirrhotic groups and between cirrhotic groups with or without hepatic encephalopathy. In the control group, mean values were 3.2±5.9 for CNR1 and 8.4±8.0 for CNR2. In the cirrhotic group, these values were 10.6±9.0 for CNR1 and 9.8±6.4 for CNR2. A statistically significant difference was noted between normal and cirrhotic groups only for CNR1(p<0.05). CNR values in patients with liver cirrhosis were 8.5±11.5 for CNR1 and 11.7±8.7 for CNR2 in the Child A group, 10.4±5.1 for CNR1 and 9.3±3.2 for CNR2 in the B group, and 12.8±9.7 for CNR1 and 8.7±6.5 for CNR2 in the C group. There was no significant difference in mean CNRI values between patients with or without hepatic encephalopathy. Differences in signal intensities in the globus pallidus and white matter, as seen on T1-weighted MR brain images, cannot be used as an indicator of hepatic encephalopathy in patients with liver cirrhosis

  2. Quantitative evaluation of hyperintensity on T1-weighted MRI in liver cirrhosis : correlation with child-pugh classification and hepatic encephalopathy

    Energy Technology Data Exchange (ETDEWEB)

    Eun, Hyo Won; Choi, Hye Young; Lee, Sun Wha; Yi, Sun Young [Ewha Womans Univ. College of Medicine, Seoul (Korea, Republic of)

    1999-11-01

    To investigate the differences in signal changes in the globus pallidus and white matter, as seen on T1-weighted MR brain images, and to determine whether these differences can be used as an indicator of subclinical hepatic encephalopathy. A total of 25 cases of liver cirrhosis were evaluated and as a control group, 20 subjects were also studied. Using a 1.5T MRI scannet, brain MR images were obtained, and the differences in signal intensity in both the globus pallidus and thalamus and in both white and gray matter were then quantified using the contrast to noise ratio(CNR). On the basis of the Child-Pugh classification, 25patients with liver cirrhosis were divided into three groups, with eight in group A, eight in B, and nine in C. Using clinical criteria, hepatic encephalopathy was diagnosed in seven of the 25 patients. There after, CNRs(CNR1 and CNR2) were conpared between the control and cirrhotic groups and between cirrhotic groups with or without hepatic encephalopathy. In the control group, mean values were 3.2{+-}5.9 for CNR1 and 8.4{+-}8.0 for CNR2. In the cirrhotic group, these values were 10.6{+-}9.0 for CNR1 and 9.8{+-}6.4 for CNR2. A statistically significant difference was noted between normal and cirrhotic groups only for CNR1(p<0.05). CNR values in patients with liver cirrhosis were 8.5{+-}11.5 for CNR1 and 11.7{+-}8.7 for CNR2 in the Child A group, 10.4{+-}5.1 for CNR1 and 9.3{+-}3.2 for CNR2 in the B group, and 12.8{+-}9.7 for CNR1 and 8.7{+-}6.5 for CNR2 in the C group. There was no significant difference in mean CNRI values between patients with or without hepatic encephalopathy. Differences in signal intensities in the globus pallidus and white matter, as seen on T1-weighted MR brain images, cannot be used as an indicator of hepatic encephalopathy in patients with liver cirrhosis.

  3. Learning semantic histopathological representation for basal cell carcinoma classification

    Science.gov (United States)

    Gutiérrez, Ricardo; Rueda, Andrea; Romero, Eduardo

    2013-03-01

    Diagnosis of a histopathology glass slide is a complex process that involves accurate recognition of several structures, their function in the tissue and their relation with other structures. The way in which the pathologist represents the image content and the relations between those objects yields a better and accurate diagnoses. Therefore, an appropriate semantic representation of the image content will be useful in several analysis tasks such as cancer classification, tissue retrieval and histopahological image analysis, among others. Nevertheless, to automatically recognize those structures and extract their inner semantic meaning are still very challenging tasks. In this paper we introduce a new semantic representation that allows to describe histopathological concepts suitable for classification. The approach herein identify local concepts using a dictionary learning approach, i.e., the algorithm learns the most representative atoms from a set of random sampled patches, and then models the spatial relations among them by counting the co-occurrence between atoms, while penalizing the spatial distance. The proposed approach was compared with a bag-of-features representation in a tissue classification task. For this purpose, 240 histological microscopical fields of view, 24 per tissue class, were collected. Those images fed a Support Vector Machine classifier per class, using 120 images as train set and the remaining ones for testing, maintaining the same proportion of each concept in the train and test sets. The obtained classification results, averaged from 100 random partitions of training and test sets, shows that our approach is more sensitive in average than the bag-of-features representation in almost 6%.

  4. Detection and classification of focal liver lesions in patients with colorectal cancer: Retrospective comparison of diffusion-weighted MR imaging and multi-slice CT

    International Nuclear Information System (INIS)

    Eiber, Matthias; Fingerle, Alexander A.; Brügel, Melanie; Gaa, Jochen; Rummeny, Ernst J.; Holzapfel, Konstantin

    2012-01-01

    Objectives: To compare the diagnostic performance of diffusion-weighted MR imaging (DWI) with multi-slice CT (MS-CT) in the detection and classification of focal liver lesions in patients with colorectal cancer. Methods: In a retrospective study 68 patients who underwent DWI at 1.5 T (b-values of 50, 300 and 600 s/mm 2 ) and contrast-enhanced MS-CT were analysed by two radiologists blinded to the clinical results. Imaging results were correlated with intraoperative surgical and ultrasound findings (n = 24), imaging follow-up or PET (n = 44). Sensitivity of DWI and MS-CT in detection of focal liver lesions was compared on a per-lesion and a per-segment basis. Receiver operator-characteristic (ROC) curves to determine the diagnostic performance and the sensitivities of correctly identifying liver metastases on a segmental base were calculated. Results: For lesion detection, DWI was significantly superior to MS-CT both on a per-lesion (difference in sensitivities for reader 1 and 2 22.65% and 19.06%, p < 0.0001) and a per-segment basis (16.86% and 11.76%, p < 0.0001). Especially lesions smaller than 10 mm were better detected with DWI compared to MS-CT (difference 41.10% and 29.45%, p < 0.0001). ROC-analysis showed superiority for lesions classification (p < 0.0001) of DWI (AUC: 0.949 and 0.951) as compared to MS-CT (AUC: 0.879 and 0.892, p < 0.0001 and p = 0.005). DWI was able to filter out metastatic segments with a higher sensitivity (88.2 and 86.5%) compared to MS-CT (68.0 and 67.4%, p < 0.0001 and p = 0.005, respectively). Conclusion: Compared to MS-CT DWI is both more sensitive in the detection of liver lesions and more accurate in determining the extent of metastatic disease in patients with colorectal cancer and therefore might help to optimize therapeutic management in those patients.

  5. Weight bearing or non-weight bearing after surgically fixed ankle fractures, the WOW! Study: study protocol for a randomized controlled trial.

    Science.gov (United States)

    Briet, Jan Paul; Houwert, Roderick M; Smeeing, Diederik P J; Pawiroredjo, Janity S; Kelder, Johannes C; Lansink, Koen W; Leenen, Luke P H; van der Zwaal, Peer; van Zutphen, Stephan W A M; Hoogendoorn, Jochem M; van Heijl, Mark; Verleisdonk, Egbert J M M; van Lammeren, Guus W; Segers, Michiel J; Hietbrink, Falco

    2015-04-18

    The optimal post-operative care regimen after surgically fixed Lauge Hansen supination exorotation injuries remains to be established. This study compares whether unprotected weight bearing as tolerated is superior to protected weight bearing and unprotected non-weight bearing in terms of functional outcome and safety. The WOW! Study is a prospective multicenter clinical trial. Patients between 18 and 65 years of age with a Lauge Hansen supination exorotation type 2, 3 or 4 ankle fractures requiring surgical treatment are eligible for inclusion. An expert panel validates the classification and inclusion eligibility. After surgery, patients are randomized to either the 1) unprotected non-weight-bearing, 2) protected weight-bearing, or 3) unprotected weight-bearing group. The primary outcome measure is ankle-specific disability measured by the Olerud-Molander ankle score. Secondary outcomes are 1) quality of life (e.g., return to work and resumption of sport), 2) complications, 3) range of motion, 4) calf wasting, and 5) maximum pressure load after 3 months and 1 year. This trial is designed to compare the effectiveness and safety of unprotected weight bearing with two commonly used post-operative treatment regimens after internal fixation of specified, intrinsically stable but displaced ankle fractures. An expert panel has been established to evaluate every potential subject, which ensures that every patient is strictly screened according to the inclusion and exclusion criteria and that there is a clear indication for surgical fixation. The WOW! Study is registered in the Dutch Trial Register ( NTR3727 ). Date of registration: 28-11-2012.

  6. Cataract in small animals: classification and treatment

    Directory of Open Access Journals (Sweden)

    Fahiano Montiani Ferreira

    1997-02-01

    Full Text Available Cataract means any opacity present in the lens, lens capsule or both. The opacities may vary in size, location, shape and rate of progression. By slit-lamp biomicroscopy it is possible to examine them with precision, determining its exact location and peculiarities, resulting in a safe, accurate diagnosis. Due to its variable origin and appearance, several methods of classification have been used. Classification by aetiology, grade of maturity, location and age of the patients are presented in this review. Surgical removal is the only effective therapy for this disease. Among the surgical techniques available to this day, endocapsular phacoemulsification excells for its better results, despite of its high cost, if compared to classical intra and extra capsular facectomies.

  7. A renewed perspective on agroforestry concepts and classification.

    Science.gov (United States)

    Torquebiau, E F

    2000-11-01

    Agroforestry, the association of trees with farming practices, is progressively becoming a recognized land-use discipline. However, it is still perceived by some scientists, technicians and farmers as a sort of environmental fashion which does not deserve credit. The peculiar history of agroforestry and the complex relationships between agriculture and forestry explain some misunderstandings about the concepts and classification of agroforestry and reveal that, contrarily to common perception, agroforestry is closer to agriculture than to forestry. Based on field experience from several countries, a structural classification of agroforestry into six simple categories is proposed: crops under tree cover, agroforests, agroforestry in a linear arrangement, animal agroforestry, sequential agroforestry and minor agroforestry techniques. It is argued that this pragmatic classification encompasses all major agroforestry associations and allows simultaneous agroforestry to be clearly differentiated from sequential agroforestry, two categories showing contrasting ecological tree-crop interactions. It can also contribute to a betterment of the image of agroforestry and lead to a simplification of its definition.

  8. Social Media Text Classification by Enhancing Well-Formed Text Trained Model

    Directory of Open Access Journals (Sweden)

    Phat Jotikabukkana

    2016-09-01

    Full Text Available Social media are a powerful communication tool in our era of digital information. The large amount of user-generated data is a useful novel source of data, even though it is not easy to extract the treasures from this vast and noisy trove. Since classification is an important part of text mining, many techniques have been proposed to classify this kind of information. We developed an effective technique of social media text classification by semi-supervised learning utilizing an online news source consisting of well-formed text. The computer first automatically extracts news categories, well-categorized by publishers, as classes for topic classification. A bag of words taken from news articles provides the initial keywords related to their category in the form of word vectors. The principal task is to retrieve a set of new productive keywords. Term Frequency-Inverse Document Frequency weighting (TF-IDF and Word Article Matrix (WAM are used as main methods. A modification of WAM is recomputed until it becomes the most effective model for social media text classification. The key success factor was enhancing our model with effective keywords from social media. A promising result of 99.50% accuracy was achieved, with more than 98.5% of Precision, Recall, and F-measure after updating the model three times.

  9. Classification bias in commercial business lists for retail food stores in the U.S.

    Science.gov (United States)

    2012-01-01

    Background Aspects of the food environment such as the availability of different types of food stores have recently emerged as key modifiable factors that may contribute to the increased prevalence of obesity. Given that many of these studies have derived their results based on secondary datasets and the relationship of food stores with individual weight outcomes has been reported to vary by store type, it is important to understand the extent to which often-used secondary data correctly classify food stores. We evaluated the classification bias of food stores in Dun & Bradstreet (D&B) and InfoUSA commercial business lists. Methods We performed a full census in 274 randomly selected census tracts in the Chicago metropolitan area and collected detailed store attributes inside stores for classification. Store attributes were compared by classification match status and store type. Systematic classification bias by census tract characteristics was assessed in multivariate regression. Results D&B had a higher classification match rate than InfoUSA for supermarkets and grocery stores, while InfoUSA was higher for convenience stores. Both lists were more likely to correctly classify large supermarkets, grocery stores, and convenience stores with more cash registers and different types of service counters (supermarkets and grocery stores only). The likelihood of a correct classification match for supermarkets and grocery stores did not vary systemically by tract characteristics whereas convenience stores were more likely to be misclassified in predominately Black tracts. Conclusion Researches can rely on classification of food stores in commercial datasets for supermarkets and grocery stores whereas classifications for convenience and specialty food stores are subject to some systematic bias by neighborhood racial/ethnic composition. PMID:22512874

  10. Classification bias in commercial business lists for retail food stores in the U.S.

    Directory of Open Access Journals (Sweden)

    Han Euna

    2012-04-01

    Full Text Available Abstract Background Aspects of the food environment such as the availability of different types of food stores have recently emerged as key modifiable factors that may contribute to the increased prevalence of obesity. Given that many of these studies have derived their results based on secondary datasets and the relationship of food stores with individual weight outcomes has been reported to vary by store type, it is important to understand the extent to which often-used secondary data correctly classify food stores. We evaluated the classification bias of food stores in Dun & Bradstreet (D&B and InfoUSA commercial business lists. Methods We performed a full census in 274 randomly selected census tracts in the Chicago metropolitan area and collected detailed store attributes inside stores for classification. Store attributes were compared by classification match status and store type. Systematic classification bias by census tract characteristics was assessed in multivariate regression. Results D&B had a higher classification match rate than InfoUSA for supermarkets and grocery stores, while InfoUSA was higher for convenience stores. Both lists were more likely to correctly classify large supermarkets, grocery stores, and convenience stores with more cash registers and different types of service counters (supermarkets and grocery stores only. The likelihood of a correct classification match for supermarkets and grocery stores did not vary systemically by tract characteristics whereas convenience stores were more likely to be misclassified in predominately Black tracts. Conclusion Researches can rely on classification of food stores in commercial datasets for supermarkets and grocery stores whereas classifications for convenience and specialty food stores are subject to some systematic bias by neighborhood racial/ethnic composition.

  11. Classification bias in commercial business lists for retail food stores in the U.S.

    Science.gov (United States)

    Han, Euna; Powell, Lisa M; Zenk, Shannon N; Rimkus, Leah; Ohri-Vachaspati, Punam; Chaloupka, Frank J

    2012-04-18

    Aspects of the food environment such as the availability of different types of food stores have recently emerged as key modifiable factors that may contribute to the increased prevalence of obesity. Given that many of these studies have derived their results based on secondary datasets and the relationship of food stores with individual weight outcomes has been reported to vary by store type, it is important to understand the extent to which often-used secondary data correctly classify food stores. We evaluated the classification bias of food stores in Dun & Bradstreet (D&B) and InfoUSA commercial business lists. We performed a full census in 274 randomly selected census tracts in the Chicago metropolitan area and collected detailed store attributes inside stores for classification. Store attributes were compared by classification match status and store type. Systematic classification bias by census tract characteristics was assessed in multivariate regression. D&B had a higher classification match rate than InfoUSA for supermarkets and grocery stores, while InfoUSA was higher for convenience stores. Both lists were more likely to correctly classify large supermarkets, grocery stores, and convenience stores with more cash registers and different types of service counters (supermarkets and grocery stores only). The likelihood of a correct classification match for supermarkets and grocery stores did not vary systemically by tract characteristics whereas convenience stores were more likely to be misclassified in predominately Black tracts. Researches can rely on classification of food stores in commercial datasets for supermarkets and grocery stores whereas classifications for convenience and specialty food stores are subject to some systematic bias by neighborhood racial/ethnic composition.

  12. Deep-learnt classification of light curves

    DEFF Research Database (Denmark)

    Mahabal, Ashish; Gieseke, Fabian; Pai, Akshay Sadananda Uppinakudru

    2017-01-01

    is to derive statistical features from the time series and to use machine learning methods, generally supervised, to separate objects into a few of the standard classes. In this work, we transform the time series to two-dimensional light curve representations in order to classify them using modern deep......Astronomy light curves are sparse, gappy, and heteroscedastic. As a result standard time series methods regularly used for financial and similar datasets are of little help and astronomers are usually left to their own instruments and techniques to classify light curves. A common approach...... learning techniques. In particular, we show that convolutional neural networks based classifiers work well for broad characterization and classification. We use labeled datasets of periodic variables from CRTS survey and show how this opens doors for a quick classification of diverse classes with several...

  13. Prediction and classification of respiratory motion

    CERN Document Server

    Lee, Suk Jin

    2014-01-01

    This book describes recent radiotherapy technologies including tools for measuring target position during radiotherapy and tracking-based delivery systems. This book presents a customized prediction of respiratory motion with clustering from multiple patient interactions. The proposed method contributes to the improvement of patient treatments by considering breathing pattern for the accurate dose calculation in radiotherapy systems. Real-time tumor-tracking, where the prediction of irregularities becomes relevant, has yet to be clinically established. The statistical quantitative modeling for irregular breathing classification, in which commercial respiration traces are retrospectively categorized into several classes based on breathing pattern are discussed as well. The proposed statistical classification may provide clinical advantages to adjust the dose rate before and during the external beam radiotherapy for minimizing the safety margin. In the first chapter following the Introduction  to this book, we...

  14. Fault Tolerant Neural Network for ECG Signal Classification Systems

    Directory of Open Access Journals (Sweden)

    MERAH, M.

    2011-08-01

    Full Text Available The aim of this paper is to apply a new robust hardware Artificial Neural Network (ANN for ECG classification systems. This ANN includes a penalization criterion which makes the performances in terms of robustness. Specifically, in this method, the ANN weights are normalized using the auto-prune method. Simulations performed on the MIT ? BIH ECG signals, have shown that significant robustness improvements are obtained regarding potential hardware artificial neuron failures. Moreover, we show that the proposed design achieves better generalization performances, compared to the standard back-propagation algorithm.

  15. Appropriateness of clinical severity classification of new WHO childhood pneumonia guidance: a multi-hospital, retrospective, cohort study.

    Science.gov (United States)

    Agweyu, Ambrose; Lilford, Richard J; English, Mike

    2018-01-01

    Management of pneumonia in many low-income and middle-income countries is based on WHO guidelines that classify children according to clinical signs that define thresholds of risk. We aimed to establish whether some children categorised as eligible for outpatient treatment might have a risk of death warranting their treatment in hospital. We did a retrospective cohort study of children aged 2-59 months admitted to one of 14 hospitals in Kenya with pneumonia between March 1, 2014, and Feb 29, 2016, before revised WHO pneumonia guidelines were adopted in the country. We modelled associations with inpatient mortality using logistic regression and calculated absolute risks of mortality for presenting clinical features among children who would, as part of revised WHO pneumonia guidelines, be eligible for outpatient treatment (non-severe pneumonia). We assessed 16 162 children who were admitted to hospital in this period. 832 (5%) of 16 031 children died. Among groups defined according to new WHO guidelines, 321 (3%) of 11 788 patients with non-severe pneumonia died compared with 488 (14%) of 3434 patients with severe pneumonia. Three characteristics were strongly associated with death of children retrospectively classified as having non-severe pneumonia: severe pallor (adjusted risk ratio 5·9, 95% CI 5·1-6·8), mild to moderate pallor (3·4, 3·0-3·8), and weight-for-age Z score (WAZ) less than -3 SD (3·8, 3·4-4·3). Additional factors that were independently associated with death were: WAZ less than -2 to -3 SD, age younger than 12 months, lower chest wall indrawing, respiratory rate of 70 breaths per min or more, female sex, admission to hospital in a malaria endemic region, moderate dehydration, and an axillary temperature of 39°C or more. In settings of high mortality, WAZ less than -3 SD or any degree of pallor among children with non-severe pneumonia was associated with a clinically important risk of death. Our data suggest that admission to hospital

  16. An efficient rhythmic component expression and weighting synthesis strategy for classifying motor imagery EEG in a brain computer interface

    Science.gov (United States)

    Wang, Tao; He, Bin

    2004-03-01

    The recognition of mental states during motor imagery tasks is crucial for EEG-based brain computer interface research. We have developed a new algorithm by means of frequency decomposition and weighting synthesis strategy for recognizing imagined right- and left-hand movements. A frequency range from 5 to 25 Hz was divided into 20 band bins for each trial, and the corresponding envelopes of filtered EEG signals for each trial were extracted as a measure of instantaneous power at each frequency band. The dimensionality of the feature space was reduced from 200 (corresponding to 2 s) to 3 by down-sampling of envelopes of the feature signals, and subsequently applying principal component analysis. The linear discriminate analysis algorithm was then used to classify the features, due to its generalization capability. Each frequency band bin was weighted by a function determined according to the classification accuracy during the training process. The present classification algorithm was applied to a dataset of nine human subjects, and achieved a success rate of classification of 90% in training and 77% in testing. The present promising results suggest that the present classification algorithm can be used in initiating a general-purpose mental state recognition based on motor imagery tasks.

  17. Relationship between Affective Symptoms and Malnutrition Severity in Severe Anorexia Nervosa

    Science.gov (United States)

    Mattar, Lama; Huas, Caroline; group, EVHAN; Godart, Nathalie

    2012-01-01

    Background Very few studies have investigated the relationship between malnutrition and psychological symptoms in Anorexia Nervosa (AN). They have used only body weight or body mass index (BMI) for the nutritional assessment and did not always report on medication, or if they did, it was not included in the analysis of results, and they did not include confounding factors such as duration of illness, AN subtype or age. The present study investigates this relationship using indicators other than BMI/weight, among which body composition and biological markers, also considering potential confounders related to depression and anxiety. Methods 155 AN patients, (DSM-IV) were included consecutively upon admission to inpatient treatment. Depression, anxiety, obsessive behaviours and social functioning were measured using various scales. Nutritional status was measured using BMI, severity of weight loss, body composition, and albumin and prealbumin levels. Results No correlation was found between BMI at inclusion, fat-free mass index, fat mass index, and severity of weight loss and any of the psychometric scores. Age and medication are the only factors that affect the psychological scores. None of the psychological scores were explained by the nutritional indicators with the exception of albumin levels which was negatively linked to the LSAS fear score (p = 0.024; beta = −0.225). Only the use of antidepressants explained the variability in BDI scores (p = 0.029; beta = 0.228) and anxiolytic use explained the variability in HADs depression scores (p = 0.037; beta = 0.216). Conclusion The present study is a pioneer investigation of various nutritional markers in relation to psychological symptoms in severely malnourished AN patients. The clinical hypothesis that malnutrition partly causes depression and anxiety symptoms in AN in acute phase is not confirmed, and future studies are needed to back up our results. PMID:23185320

  18. Automated classification of cell morphology by coherence-controlled holographic microscopy

    Science.gov (United States)

    Strbkova, Lenka; Zicha, Daniel; Vesely, Pavel; Chmelik, Radim

    2017-08-01

    In the last few years, classification of cells by machine learning has become frequently used in biology. However, most of the approaches are based on morphometric (MO) features, which are not quantitative in terms of cell mass. This may result in poor classification accuracy. Here, we study the potential contribution of coherence-controlled holographic microscopy enabling quantitative phase imaging for the classification of cell morphologies. We compare our approach with the commonly used method based on MO features. We tested both classification approaches in an experiment with nutritionally deprived cancer tissue cells, while employing several supervised machine learning algorithms. Most of the classifiers provided higher performance when quantitative phase features were employed. Based on the results, it can be concluded that the quantitative phase features played an important role in improving the performance of the classification. The methodology could be valuable help in refining the monitoring of live cells in an automated fashion. We believe that coherence-controlled holographic microscopy, as a tool for quantitative phase imaging, offers all preconditions for the accurate automated analysis of live cell behavior while enabling noninvasive label-free imaging with sufficient contrast and high-spatiotemporal phase sensitivity.

  19. The paradox of atheoretical classification

    DEFF Research Database (Denmark)

    Hjørland, Birger

    2016-01-01

    A distinction can be made between “artificial classifications” and “natural classifications,” where artificial classifications may adequately serve some limited purposes, but natural classifications are overall most fruitful by allowing inference and thus many different purposes. There is strong...... support for the view that a natural classification should be based on a theory (and, of course, that the most fruitful theory provides the most fruitful classification). Nevertheless, atheoretical (or “descriptive”) classifications are often produced. Paradoxically, atheoretical classifications may...... be very successful. The best example of a successful “atheoretical” classification is probably the prestigious Diagnostic and Statistical Manual of Mental Disorders (DSM) since its third edition from 1980. Based on such successes one may ask: Should the claim that classifications ideally are natural...

  20. Application of Classification Methods for Forecasting Mid-Term Power Load Patterns

    Science.gov (United States)

    Piao, Minghao; Lee, Heon Gyu; Park, Jin Hyoung; Ryu, Keun Ho

    Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in long duration load profiles. The proposed approach in this paper consists of three stages: (i) data preprocessing: noise or outlier is removed and the continuous attribute-valued features are transformed to discrete values, (ii) cluster analysis: k-means clustering is used to create load pattern classes and the representative load profiles for each class and (iii) classification: we evaluated several supervised learning methods in order to select a suitable prediction method. According to the proposed methodology, power load measured from AMR (automatic meter reading) system, as well as customer indexes, were used as inputs for clustering. The output of clustering was the classification of representative load profiles (or classes). In order to evaluate the result of forecasting load patterns, the several classification methods were applied on a set of high voltage customers of the Korea power system and derived class labels from clustering and other features are used as input to produce classifiers. Lastly, the result of our experiments was presented.

  1. OmniGA: Optimized Omnivariate Decision Trees for Generalizable Classification Models

    KAUST Repository

    Magana-Mora, Arturo

    2017-06-14

    Classification problems from different domains vary in complexity, size, and imbalance of the number of samples from different classes. Although several classification models have been proposed, selecting the right model and parameters for a given classification task to achieve good performance is not trivial. Therefore, there is a constant interest in developing novel robust and efficient models suitable for a great variety of data. Here, we propose OmniGA, a framework for the optimization of omnivariate decision trees based on a parallel genetic algorithm, coupled with deep learning structure and ensemble learning methods. The performance of the OmniGA framework is evaluated on 12 different datasets taken mainly from biomedical problems and compared with the results obtained by several robust and commonly used machine-learning models with optimized parameters. The results show that OmniGA systematically outperformed these models for all the considered datasets, reducing the F score error in the range from 100% to 2.25%, compared to the best performing model. This demonstrates that OmniGA produces robust models with improved performance. OmniGA code and datasets are available at www.cbrc.kaust.edu.sa/omniga/.

  2. OmniGA: Optimized Omnivariate Decision Trees for Generalizable Classification Models

    KAUST Repository

    Magana-Mora, Arturo; Bajic, Vladimir B.

    2017-01-01

    Classification problems from different domains vary in complexity, size, and imbalance of the number of samples from different classes. Although several classification models have been proposed, selecting the right model and parameters for a given classification task to achieve good performance is not trivial. Therefore, there is a constant interest in developing novel robust and efficient models suitable for a great variety of data. Here, we propose OmniGA, a framework for the optimization of omnivariate decision trees based on a parallel genetic algorithm, coupled with deep learning structure and ensemble learning methods. The performance of the OmniGA framework is evaluated on 12 different datasets taken mainly from biomedical problems and compared with the results obtained by several robust and commonly used machine-learning models with optimized parameters. The results show that OmniGA systematically outperformed these models for all the considered datasets, reducing the F score error in the range from 100% to 2.25%, compared to the best performing model. This demonstrates that OmniGA produces robust models with improved performance. OmniGA code and datasets are available at www.cbrc.kaust.edu.sa/omniga/.

  3. Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease.

    Science.gov (United States)

    Shamonin, Denis P; Bron, Esther E; Lelieveldt, Boudewijn P F; Smits, Marion; Klein, Stefan; Staring, Marius

    2013-01-01

    Nonrigid image registration is an important, but time-consuming task in medical image analysis. In typical neuroimaging studies, multiple image registrations are performed, i.e., for atlas-based segmentation or template construction. Faster image registration routines would therefore be beneficial. In this paper we explore acceleration of the image registration package elastix by a combination of several techniques: (i) parallelization on the CPU, to speed up the cost function derivative calculation; (ii) parallelization on the GPU building on and extending the OpenCL framework from ITKv4, to speed up the Gaussian pyramid computation and the image resampling step; (iii) exploitation of certain properties of the B-spline transformation model; (iv) further software optimizations. The accelerated registration tool is employed in a study on diagnostic classification of Alzheimer's disease and cognitively normal controls based on T1-weighted MRI. We selected 299 participants from the publicly available Alzheimer's Disease Neuroimaging Initiative database. Classification is performed with a support vector machine based on gray matter volumes as a marker for atrophy. We evaluated two types of strategies (voxel-wise and region-wise) that heavily rely on nonrigid image registration. Parallelization and optimization resulted in an acceleration factor of 4-5x on an 8-core machine. Using OpenCL a speedup factor of 2 was realized for computation of the Gaussian pyramids, and 15-60 for the resampling step, for larger images. The voxel-wise and the region-wise classification methods had an area under the receiver operator characteristic curve of 88 and 90%, respectively, both for standard and accelerated registration. We conclude that the image registration package elastix was substantially accelerated, with nearly identical results to the non-optimized version. The new functionality will become available in the next release of elastix as open source under the BSD license.

  4. Misperception of weight status and associated factors among undergraduate students.

    Science.gov (United States)

    Mogre, Victor; Aleyira, Samuel; Nyaba, Rauf

    2015-01-01

    We compared participants' self-perception of their weight with the World Health Organisation (WHO) definition for BMI categories among undergraduate university students undertaking health related academic programmes in Ghana. Also, we investigated factors associated to the underestimation of weight status in this sample. This cross-sectional study was conducted among a sample of 368 undergraduate students. Anthropometric measurements of weight and height were measured with appropriate tools and computed into Body Mass Index (BMI) categorised based on WHO classifications. Waist and hip circumferences were also measured appropriately. Participants' self-perception of weight status was assessed by the question: How do you perceive your weight? (a) Underweight, (b) normal weight, (c) overweight, and (d) obese. The BMI-measured weight status was compared to the self-perceived weight status by cross-tabulation, Kappa statistics of agreement and χ(2) for trend analysis. Factors associated with misperception of weight status was measured using univariate and multivariable analysis. Thirteen percent (n=48) of the participants were overweight/obesity (BMI) and 31.5% had central obesity. Overall, 20.6% of the participants misperceived their weight status in which 78.9% of them underestimated it. Among overweight/obese participants, 41.7% self-perceived themselves accurately. Whereas 10.6% of normal weight participants underestimated their weight status, over half (58.3%) of overweight/obese participants did so. Factors that were associated with underestimation of weight status were having overweight/obesity (BMI) and central obesity. Underestimation of weight status was frequent. Health professionals and related government agencies should develop intervention programmes to empower young people to have accurate weight status perception. Copyright © 2015 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.

  5. Transporter Classification Database (TCDB)

    Data.gov (United States)

    U.S. Department of Health & Human Services — The Transporter Classification Database details a comprehensive classification system for membrane transport proteins known as the Transporter Classification (TC)...

  6. International Standards for Neurological Classification of Spinal Cord Injury:

    DEFF Research Database (Denmark)

    Kirshblum, S C; Biering-Sørensen, Fin; Betz, R

    2014-01-01

    The International Standards for the Neurological Classification of Spinal Cord Injury (ISNCSCI) is routinely used to determine levels of injury and to classify the severity of the injury. Questions are often posed to the International Standards Committee of the American Spinal Injury Association...

  7. Weight discrimination and bullying.

    Science.gov (United States)

    Puhl, Rebecca M; King, Kelly M

    2013-04-01

    Despite significant attention to the medical impacts of obesity, often ignored are the negative outcomes that obese children and adults experience as a result of stigma, bias, and discrimination. Obese individuals are frequently stigmatized because of their weight in many domains of daily life. Research spanning several decades has documented consistent weight bias and stigmatization in employment, health care, schools, the media, and interpersonal relationships. For overweight and obese youth, weight stigmatization translates into pervasive victimization, teasing, and bullying. Multiple adverse outcomes are associated with exposure to weight stigmatization, including depression, anxiety, low self-esteem, body dissatisfaction, suicidal ideation, poor academic performance, lower physical activity, maladaptive eating behaviors, and avoidance of health care. This review summarizes the nature and extent of weight stigmatization against overweight and obese individuals, as well as the resulting consequences that these experiences create for social, psychological, and physical health for children and adults who are targeted. Copyright © 2013 Elsevier Ltd. All rights reserved.

  8. Monitoring nanotechnology using patent classifications: an overview and comparison of nanotechnology classification schemes

    Energy Technology Data Exchange (ETDEWEB)

    Jürgens, Björn, E-mail: bjurgens@agenciaidea.es [Agency of Innovation and Development of Andalusia, CITPIA PATLIB Centre (Spain); Herrero-Solana, Victor, E-mail: victorhs@ugr.es [University of Granada, SCImago-UGR (SEJ036) (Spain)

    2017-04-15

    Patents are an essential information source used to monitor, track, and analyze nanotechnology. When it comes to search nanotechnology-related patents, a keyword search is often incomplete and struggles to cover such an interdisciplinary discipline. Patent classification schemes can reveal far better results since they are assigned by experts who classify the patent documents according to their technology. In this paper, we present the most important classifications to search nanotechnology patents and analyze how nanotechnology is covered in the main patent classification systems used in search systems nowadays: the International Patent Classification (IPC), the United States Patent Classification (USPC), and the Cooperative Patent Classification (CPC). We conclude that nanotechnology has a significantly better patent coverage in the CPC since considerable more nanotechnology documents were retrieved than by using other classifications, and thus, recommend its use for all professionals involved in nanotechnology patent searches.

  9. Monitoring nanotechnology using patent classifications: an overview and comparison of nanotechnology classification schemes

    International Nuclear Information System (INIS)

    Jürgens, Björn; Herrero-Solana, Victor

    2017-01-01

    Patents are an essential information source used to monitor, track, and analyze nanotechnology. When it comes to search nanotechnology-related patents, a keyword search is often incomplete and struggles to cover such an interdisciplinary discipline. Patent classification schemes can reveal far better results since they are assigned by experts who classify the patent documents according to their technology. In this paper, we present the most important classifications to search nanotechnology patents and analyze how nanotechnology is covered in the main patent classification systems used in search systems nowadays: the International Patent Classification (IPC), the United States Patent Classification (USPC), and the Cooperative Patent Classification (CPC). We conclude that nanotechnology has a significantly better patent coverage in the CPC since considerable more nanotechnology documents were retrieved than by using other classifications, and thus, recommend its use for all professionals involved in nanotechnology patent searches.

  10. Pancreatic neuroendocrine tumour: Correlation of apparent diffusion coefficient or WHO classification with recurrence-free survival.

    Science.gov (United States)

    Kim, Mimi; Kang, Tae Wook; Kim, Young Kon; Kim, Seong Hyun; Kwon, Wooil; Ha, Sang Yun; Ji, Sang A

    2016-03-01

    To evaluate the correlation between grade of pancreatic neuroendocrine tumours (pNETs) based on the 2010 World Health Organization (WHO) classification and the apparent diffusion coefficient (ADC), and to assess whether the ADC value and WHO classification can predict recurrence-free survival (RFS) after surgery for pNETs. This retrospective study was approved by the Institutional Review Board. The requirement for informed consent was waived. Between March 2009 and November 2014, forty-nine patients who underwent magnetic resonance (MR) imaging with diffusion-weighted image and subsequent surgery for single pNETs were included. Correlations among qualitative MR imaging findings, quantitative ADC values, and WHO classifications were assessed. An ordered logistic regression test was used to control for tumour size as a confounding factor. The association between ADC value (or WHO classification) and RFS was analysed. All tumors (n=49) were classified as low- (n=29, grade 1), intermediate- (n=17, grade 2), and high-grade (n=3, grade 3), respectively. The mean ADC of pNETs was moderately negatively correlated with WHO classification before and after adjustment for tumour size (ρ=-0.64, pcorrelated with WHO tumour grade, regardless of tumour size. However, the WHO tumour classification of pNET may be more suitable for predicting RFS than the ADC value. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  11. Severity of motor dysfunction in children with cerebral palsy seen in ...

    African Journals Online (AJOL)

    GMD) of varying degrees of severity. The Gross Motor Function Classification System (GMFCS) is widely used internationally to classify children with CP into functional severity levels. There are few reports on the use of GMFCS in Nigeria to ...

  12. Intra- and interobserver reliability of the Eaton classification for trapeziometacarpal arthritis: a systematic review.

    Science.gov (United States)

    Berger, Aaron J; Momeni, Arash; Ladd, Amy L

    2014-04-01

    Trapeziometacarpal, or thumb carpometacarpal (CMC), arthritis is a common problem with a variety of treatment options. Although widely used, the Eaton radiographic staging system for CMC arthritis is of questionable clinical utility, as disease severity does not predictably correlate with symptoms or treatment recommendations. A possible reason for this is that the classification itself may not be reliable, but the literature on this has not, to our knowledge, been systematically reviewed. We therefore performed a systematic review to determine the intra- and interobserver reliability of the Eaton staging system. We systematically reviewed English-language studies published between 1973 and 2013 to assess the degree of intra- and interobserver reliability of the Eaton classification for determining the stage of trapeziometacarpal joint arthritis and pantrapezial arthritis based on plain radiographic imaging. Search engines included: PubMed, Scopus(®), and CINAHL. Four studies, which included a total of 163 patients, met our inclusion criteria and were evaluated. The level of evidence of the studies included in this analysis was determined using the Oxford Centre for Evidence Based Medicine Levels of Evidence Classification by two independent observers. A limited number of studies have been performed to assess intra- and interobserver reliability of the Eaton classification system. The four studies included were determined to be Level 3b. These studies collectively indicate that the Eaton classification demonstrates poor to fair interobserver reliability (kappa values: 0.11-0.56) and fair to moderate intraobserver reliability (kappa values: 0.54-0.657). Review of the literature demonstrates that radiographs assist in the assessment of CMC joint disease, but there is not a reliable system for classification of disease severity. Currently, diagnosis and treatment of thumb CMC arthritis are based on the surgeon's qualitative assessment combining history, physical

  13. The Effectiveness of a Nondiet Multidisciplinary Weight Reduction Program for Severe Overweight Patients with Psychological Comorbidities

    Directory of Open Access Journals (Sweden)

    Bettina Bannert

    2011-01-01

    Full Text Available Objective. For successful sustainable weight reduction, a multimodal program including behaviour therapy is needed. Lifestyle modification is mostly used for obesity BMI 40 kg/m2 with psychological comorbidity. Research Methods and Procedere. A retrospective data analysis of 99 participants who passed the program based on moderate activity, healthy and regular food intake over metabolic rate and behaviour therapy was conducted. Results. 64 had a BMI >40 kg/m2 (mean value 49.99±8.74. The relative weight reduction was −6.9 ± 3.9%; (Friedman test P40 kg/m2 may achieve significant changes of weight reduction and psychological symptoms. However, the primary outcome should not be weight reduction. It is necessary to identify the benefits of lifestyle modification on changing risk profiles and emotional regulation of food intake.

  14. Adverse events following cervical manipulative therapy: consensus on classification among Dutch medical specialists, manual therapists, and patients.

    Science.gov (United States)

    Kranenburg, Hendrikus A; Lakke, Sandra E; Schmitt, Maarten A; Van der Schans, Cees P

    2017-12-01

    To obtain consensus-based agreement on a classification system of adverse events (AE) following cervical spinal manipulation. The classification system should be comprised of clear definitions, include patients' and clinicians' perspectives, and have an acceptable number of categories. Design : A three-round Delphi study. Participants : Thirty Dutch participants (medical specialists, manual therapists, and patients) participated in an online survey. Procedure : Participants inventoried AE and were asked about their preferences for either a three- or a four-category classification system. The identified AE were classified by two analysts following the International Classification of Functioning, Disability and Health (ICF), and the International Classification of Diseases and Related Health Problems (ICD-10). Participants were asked to classify the severity for all AE in relation to the time duration. Consensus occurred in a three-category classification system. There was strong consensus for 16 AE in all severities (no, minor, and major AE) and all three time durations [hours, days, weeks]. The 16 AE included anxiety, flushing, skin rash, fainting, dizziness, coma, altered sensation, muscle tenderness, pain, increased pain during movement, radiating pain, dislocation, fracture, transient ischemic attack, stroke, and death. Mild to strong consensus was reached for 13 AE. A consensus-based classification system of AE is established which includes patients' and clinicians' perspectives and has three categories. The classification comprises a precise description of potential AE in accordance with internationally accepted classifications. After international validation, clinicians and researchers may use this AE classification system to report AE in clinical practice and research.

  15. A framework for classification of prokaryotic protein kinases.

    Directory of Open Access Journals (Sweden)

    Nidhi Tyagi

    Full Text Available BACKGROUND: Overwhelming majority of the Serine/Threonine protein kinases identified by gleaning archaeal and eubacterial genomes could not be classified into any of the well known Hanks and Hunter subfamilies of protein kinases. This is owing to the development of Hanks and Hunter classification scheme based on eukaryotic protein kinases which are highly divergent from their prokaryotic homologues. A large dataset of prokaryotic Serine/Threonine protein kinases recognized from genomes of prokaryotes have been used to develop a classification framework for prokaryotic Ser/Thr protein kinases. METHODOLOGY/PRINCIPAL FINDINGS: We have used traditional sequence alignment and phylogenetic approaches and clustered the prokaryotic kinases which represent 72 subfamilies with at least 4 members in each. Such a clustering enables classification of prokaryotic Ser/Thr kinases and it can be used as a framework to classify newly identified prokaryotic Ser/Thr kinases. After series of searches in a comprehensive sequence database we recognized that 38 subfamilies of prokaryotic protein kinases are associated to a specific taxonomic level. For example 4, 6 and 3 subfamilies have been identified that are currently specific to phylum proteobacteria, cyanobacteria and actinobacteria respectively. Similarly subfamilies which are specific to an order, sub-order, class, family and genus have also been identified. In addition to these, we also identify organism-diverse subfamilies. Members of these clusters are from organisms of different taxonomic levels, such as archaea, bacteria, eukaryotes and viruses. CONCLUSION/SIGNIFICANCE: Interestingly, occurrence of several taxonomic level specific subfamilies of prokaryotic kinases contrasts with classification of eukaryotic protein kinases in which most of the popular subfamilies of eukaryotic protein kinases occur diversely in several eukaryotes. Many prokaryotic Ser/Thr kinases exhibit a wide variety of modular

  16. Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification

    Science.gov (United States)

    Hepner, George F.; Logan, Thomas; Ritter, Niles; Bryant, Nevin

    1990-01-01

    Recent research has shown an artificial neural network (ANN) to be capable of pattern recognition and the classification of image data. This paper examines the potential for the application of neural network computing to satellite image processing. A second objective is to provide a preliminary comparison and ANN classification. An artificial neural network can be trained to do land-cover classification of satellite imagery using selected sites representative of each class in a manner similar to conventional supervised classification. One of the major problems associated with recognition and classifications of pattern from remotely sensed data is the time and cost of developing a set of training sites. This reseach compares the use of an ANN back propagation classification procedure with a conventional supervised maximum likelihood classification procedure using a minimal training set. When using a minimal training set, the neural network is able to provide a land-cover classification superior to the classification derived from the conventional classification procedure. This research is the foundation for developing application parameters for further prototyping of software and hardware implementations for artificial neural networks in satellite image and geographic information processing.

  17. Small-scale classification schemes

    DEFF Research Database (Denmark)

    Hertzum, Morten

    2004-01-01

    Small-scale classification schemes are used extensively in the coordination of cooperative work. This study investigates the creation and use of a classification scheme for handling the system requirements during the redevelopment of a nation-wide information system. This requirements...... classification inherited a lot of its structure from the existing system and rendered requirements that transcended the framework laid out by the existing system almost invisible. As a result, the requirements classification became a defining element of the requirements-engineering process, though its main...... effects remained largely implicit. The requirements classification contributed to constraining the requirements-engineering process by supporting the software engineers in maintaining some level of control over the process. This way, the requirements classification provided the software engineers...

  18. Classification of e-government documents based on cooperative expression of word vectors

    Science.gov (United States)

    Fu, Qianqian; Liu, Hao; Wei, Zhiqiang

    2017-03-01

    The effective document classification is a powerful technique to deal with the huge amount of e-government documents automatically instead of accomplishing them manually. The word-to-vector (word2vec) model, which converts semantic word into low-dimensional vectors, could be successfully employed to classify the e-government documents. In this paper, we propose the cooperative expressions of word vector (Co-word-vector), whose multi-granularity of integration explores the possibility of modeling documents in the semantic space. Meanwhile, we also aim to improve the weighted continuous bag of words model based on word2vec model and distributed representation of topic-words based on LDA model. Furthermore, combining the two levels of word representation, performance result shows that our proposed method on the e-government document classification outperform than the traditional method.

  19. Cognitive-motivational deficits in ADHD: development of a classification system.

    Science.gov (United States)

    Gupta, Rashmi; Kar, Bhoomika R; Srinivasan, Narayanan

    2011-01-01

    The classification systems developed so far to detect attention deficit/hyperactivity disorder (ADHD) do not have high sensitivity and specificity. We have developed a classification system based on several neuropsychological tests that measure cognitive-motivational functions that are specifically impaired in ADHD children. A total of 240 (120 ADHD children and 120 healthy controls) children in the age range of 6-9 years and 32 Oppositional Defiant Disorder (ODD) children (aged 9 years) participated in the study. Stop-Signal, Task-Switching, Attentional Network, and Choice Delay tests were administered to all the participants. Receiver operating characteristic (ROC) analysis indicated that percentage choice of long-delay reward best classified the ADHD children from healthy controls. Single parameters were not helpful in making a differential classification of ADHD with ODD. Multinominal logistic regression (MLR) was performed with multiple parameters (data fusion) that produced improved overall classification accuracy. A combination of stop-signal reaction time, posterror-slowing, mean delay, switch cost, and percentage choice of long-delay reward produced an overall classification accuracy of 97.8%; with internal validation, the overall accuracy was 92.2%. Combining parameters from different tests of control functions not only enabled us to accurately classify ADHD children from healthy controls but also in making a differential classification with ODD. These results have implications for the theories of ADHD.

  20. MR relaxometry in chronic liver diseases: Comparison of T1 mapping, T2 mapping, and diffusion-weighted imaging for assessing cirrhosis diagnosis and severity

    Energy Technology Data Exchange (ETDEWEB)

    Cassinotto, Christophe, E-mail: christophe.cassinotto@chu-bordeaux.fr [Department of Diagnostic and Interventional Imaging, Hôpital Haut-Lévêque, Centre Hospitalier Universitaire et Université de Bordeaux, 1 Avenue de Magellan, 33604 Pessac (France); INSERM U1053, Université Bordeaux, Bordeaux (France); Feldis, Matthieu, E-mail: matthieu.feldis@chu-bordeaux.fr [Department of Diagnostic and Interventional Imaging, Hôpital Haut-Lévêque, Centre Hospitalier Universitaire et Université de Bordeaux, 1 Avenue de Magellan, 33604 Pessac (France); Vergniol, Julien, E-mail: julien.vergniol@chu-bordeaux.fr [Centre D’investigation de la Fibrose Hépatique, Hôpital Haut-Lévêque, Centre Hospitalier Universitaire de Bordeaux, 1 Avenue de Magellan, 33604 Pessac (France); Mouries, Amaury, E-mail: amaury.mouries@chu-bordeaux.fr [Department of Diagnostic and Interventional Imaging, Hôpital Haut-Lévêque, Centre Hospitalier Universitaire et Université de Bordeaux, 1 Avenue de Magellan, 33604 Pessac (France); Cochet, Hubert, E-mail: hubert.cochet@chu-bordeaux.fr [Department of Diagnostic and Interventional Imaging, Hôpital Haut-Lévêque, Centre Hospitalier Universitaire et Université de Bordeaux, 1 Avenue de Magellan, 33604 Pessac (France); and others

    2015-08-15

    Highlights: • The use of MR to classify cirrhosis in different stages is a new interesting field. • We compared liver and spleen T1 mapping, T2 mapping and diffusion-weighted imaging. • MR relaxometry using liver T1 mapping is accurate for the diagnosis of cirrhosis. • Liver T1 mapping shows that values increase with the severity of cirrhosis. • Diffusion-weighted imaging is less accurate than T1 mapping while T2 mapping is not reliable. - Abstract: Background: MR relaxometry has been extensively studied in the field of cardiac diseases, but its contribution to liver imaging is unclear. We aimed to compare liver and spleen T1 mapping, T2 mapping, and diffusion-weighted MR imaging (DWI) for assessing the diagnosis and severity of cirrhosis. Methods: We prospectively included 129 patients with normal (n = 40) and cirrhotic livers (n = 89) from May to September 2014. Non-enhanced liver T1 mapping, splenic T2 mapping, and liver and splenic DWI were measured and compared for assessing cirrhosis severity using Child-Pugh score, MELD score, and presence or not of large esophageal varices (EVs) and liver stiffness measurements using Fibroscan{sup ®} as reference. Results: Liver T1 mapping was the only variable demonstrating significant differences between normal patients (500 ± 79 ms), Child-Pugh A patients (574 ± 84 ms) and Child-Pugh B/C patients (690 ± 147 ms; all p-values <0.00001). Liver T1 mapping had a significant correlation with Child-Pugh score (Pearson's correlation coefficient of 0.46), MEDL score (0.30), and liver stiffness measurement (0.52). Areas under the receiver operating characteristic curves of liver T1 mapping for the diagnosis of cirrhosis (O.85; 95% confidence intervals (CI), 0.77–0.91), Child-Pugh B/C cirrhosis (0.87; 95%CI, 0.76–0.93), and large EVs (0.75; 95%CI, 0.63–0.83) were greater than that of spleen T2 mapping, liver and spleen DWI (all p-values < 0.01). Conclusion: Liver T1 mapping is a promising new diagnostic