WorldWideScience

Sample records for network health diagnosis

  1. Health Insurance Portability and Accountability Act-Compliant Ocular Telehealth Network for the Remote Diagnosis and Management of Diabetic Retinopathy

    Energy Technology Data Exchange (ETDEWEB)

    Li, Yaquin [University of Tennessee, Knoxville (UTK); Karnowski, Thomas Paul [ORNL; Tobin Jr, Kenneth William [ORNL; Giancardo, Luca [ORNL; Garg, Seema [University of North Carolina; Fox, Karen [Delta Health Alliance; Chaum, Edward [University of Tennessee, Knoxville (UTK)

    2011-01-01

    In this article, we present the design and implementation of a regional ocular telehealth network for remote assessment and management of diabetic retinopathy (DR), including the design requirements, network topology, protocol design, system work flow, graphics user interfaces, and performance evaluation. The Telemedical Retinal Image Analysis and Diagnosis Network is a computer-aided, image analysis telehealth paradigm for the diagnosis of DR and other retinal diseases using fundus images acquired from primary care end users delivering care to underserved patient populations in the mid-South and southeastern United States.

  2. Network Fault Diagnosis Using DSM

    Institute of Scientific and Technical Information of China (English)

    Jiang Hao; Yan Pu-liu; Chen Xiao; Wu Jing

    2004-01-01

    Difference similitude matrix (DSM) is effective in reducing information system with its higher reduction rate and higher validity. We use DSM method to analyze the fault data of computer networks and obtain the fault diagnosis rules. Through discretizing the relative value of fault data, we get the information system of the fault data. DSM method reduces the information system and gets the diagnosis rules. The simulation with the actual scenario shows that the fault diagnosis based on DSM can obtain few and effective rules.

  3. Communication networks of men facing a diagnosis of prostate cancer.

    Science.gov (United States)

    Brown, Dot; Oetzel, John; Henderson, Alison

    2016-11-01

    This study seeks to identify the factors that shape the communication networks of men who face a potential diagnosis of prostate cancer, and how these factors relate to their disclosure about their changing health status. Men facing a potential diagnosis of prostate cancer are in a challenging situation; the support benefits of disclosing their changing health status to others in their communication networks is set against a backdrop of the potential stigma and uncertainty of the diagnosis. All men on a prostate biopsy waiting list were eligible for inclusion in an exploratory and interpretive study. Semi-structured interviews with 40 men explored their network structures and disclosure of health information. Thematic analysis highlighted the factors which contributed to their network structures and their disclosure about their health status. Four network factors shaped men's perspectives about disclosing their health status: (1) tie strength, comprising both strong and weak ties; (2) knowledgeable others, with a focus on medical professionals in the family; (3) homophily, which included other individuals with a similar medical condition; and (4) geographical proximity, with a preference for face-to-face communication. Communication networks influence men's disclosure of their health status and in particular weak ties with medical knowledge have an important role. Men who use the potential for support in their networks may experience improved psychosocial outcomes. Using these four network factors-tie strength, knowledgeable others, homophily or geographical proximity-to forecast men's willingness to disclose helps identify men who lack potential support and so are at risk of poor psychosocial health. Those with few strong ties or knowledgeable others in their networks may be in the at-risk cohort. The support provided in communication networks complements formal medical care from nurses and other health professionals, and encouraging patients to use their

  4. Interaction between research and diagnosis and surveillance of avian influenza within the Caribbean animal health network (CaribVET).

    Science.gov (United States)

    Lefrançois, T; Hendrikx, P; Vachiéry, N; Ehrhardt, N; Millien, M; Gomez, L; Gouyet, L; Gerbier, G; Gongora, V; Shaw, J; Trotman, M

    2010-04-01

    The Caribbean region is considered to be at risk for avian influenza (AI) because of predominance of the backyard poultry system, important commercial poultry production, migratory birds and disparities in the surveillance systems. The Caribbean animal health network (CaribVET) has developed tools to implement AI surveillance in the region: (i) a regionally harmonized surveillance protocol, (ii) specific web pages for AI surveillance on http://www.caribvet.net, and (iii) a diagnostic network for the Caribbean including AI virus molecular diagnostic capability in Guadeloupe and technology transfer. Altogether 303 samples from four Caribbean countries were tested between June 2006 and March 2009 by real time PCR either for importation purposes or following clinical suspicion. Following AI H5N2 outbreaks in the Dominican Republic in 2007, a questionnaire was developed to collect data for risk analysis of AI spread in the region through fighting cocks. The infection pathway of Martinique commercial poultry sector by AI through introduction of infected cocks was designed and recommendations were provided to the Caribbean veterinary services to improve fighting cock movement controls and biosecurity measures. Altogether, these CaribVET activities contribute to strengthen surveillance of AI in the Caribbean region and may allow the development of research studies on AI risk analysis.

  5. Physical Health and Dual Diagnosis

    OpenAIRE

    Robson, Debbie; Keen, Sarah; Mauro, Pia

    2008-01-01

    The physical health of people with mental illness may be neglected for a variety of reasons. This paper looks at the common physical health problems experienced by people with a dual diagnosis of substance misuse and serious mental illness, and suggests ways of assessing and managing them.

  6. Failure diagnosis using deep belief learning based health state classification

    International Nuclear Information System (INIS)

    Tamilselvan, Prasanna; Wang, Pingfeng

    2013-01-01

    Effective health diagnosis provides multifarious benefits such as improved safety, improved reliability and reduced costs for operation and maintenance of complex engineered systems. This paper presents a novel multi-sensor health diagnosis method using deep belief network (DBN). DBN has recently become a popular approach in machine learning for its promised advantages such as fast inference and the ability to encode richer and higher order network structures. The DBN employs a hierarchical structure with multiple stacked restricted Boltzmann machines and works through a layer by layer successive learning process. The proposed multi-sensor health diagnosis methodology using DBN based state classification can be structured in three consecutive stages: first, defining health states and preprocessing sensory data for DBN training and testing; second, developing DBN based classification models for diagnosis of predefined health states; third, validating DBN classification models with testing sensory dataset. Health diagnosis using DBN based health state classification technique is compared with four existing diagnosis techniques. Benchmark classification problems and two engineering health diagnosis applications: aircraft engine health diagnosis and electric power transformer health diagnosis are employed to demonstrate the efficacy of the proposed approach

  7. Diagnosis method utilizing neural networks

    International Nuclear Information System (INIS)

    Watanabe, K.; Tamayama, K.

    1990-01-01

    Studies have been made on the technique of neural networks, which will be used to identify a cause of a small anomalous state in the reactor coolant system of the ATR (Advance Thermal Reactor). Three phases of analyses were carried out in this study. First, simulation for 100 seconds was made to determine how the plant parameters respond after the occurence of a transient decrease in reactivity, flow rate and temperature of feed water and increase in the steam flow rate and steam pressure, which would produce a decrease of water level in a steam drum of the ATR. Next, the simulation data was analysed utilizing an autoregressive model. From this analysis, a total of 36 coherency functions up to 0.5 Hz in each transient were computed among nine important and detectable plant parameters: neutron flux, flow rate of coolant, steam or feed water, water level in the steam drum, pressure and opening area of control valve in a steam pipe, feed water temperature and electrical power. Last, learning of neural networks composed of 96 input, 4-9 hidden and 5 output layer units was done by use of the generalized delta rule, namely a back-propagation algorithm. These convergent computations were continued as far as the difference between the desired outputs, 1 for direct cause or 0 for four other ones and actual outputs reached less than 10%. (1) Coherency functions were not governed by decreasing rate of reactivity in the range of 0.41x10 -2 dollar/s to 1.62x10 -2 dollar /s or by decreasing depth of the feed water temperature in the range of 3 deg C to 10 deg C or by a change of 10% or less in the three other causes. Change in coherency functions only depended on the type of cause. (2) The direct cause from the other four ones could be discriminated with 0.94+-0.01 of output level. A maximum of 0.06 output height was found among the other four causes. (3) Calculation load which is represented as products of learning times and numbers of the hidden units did not depend on the

  8. First diagnosis and management of incontinence in older people with and without dementia in primary care: a cohort study using The Health Improvement Network primary care database.

    Directory of Open Access Journals (Sweden)

    Robert L Grant

    2013-08-01

    Full Text Available Dementia is one of the most disabling and burdensome diseases. Incontinence in people with dementia is distressing, adds to carer burden, and influences decisions to relocate people to care homes. Successful and safe management of incontinence in people with dementia presents additional challenges. The aim of this study was to investigate the rates of first diagnosis in primary care of urinary and faecal incontinence among people aged 60-89 with dementia, and the use of medication or indwelling catheters for urinary incontinence.We extracted data on 54,816 people aged 60-89 with dementia and an age-gender stratified sample of 205,795 people without dementia from 2001 to 2010 from The Health Improvement Network (THIN, a United Kingdom primary care database. THIN includes data on patients and primary care consultations but does not identify care home residents. Rate ratios were adjusted for age, sex, and co-morbidity using multilevel Poisson regression. The rates of first diagnosis per 1,000 person-years at risk (95% confidence interval for urinary incontinence in the dementia cohort, among men and women, respectively, were 42.3 (40.9-43.8 and 33.5 (32.6-34.5. In the non-dementia cohort, the rates were 19.8 (19.4-20.3 and 18.6 (18.2-18.9. The rates of first diagnosis for faecal incontinence in the dementia cohort were 11.1 (10.4-11.9 and 10.1 (9.6-10.6. In the non-dementia cohort, the rates were 3.1 (2.9-3.3 and 3.6 (3.5-3.8. The adjusted rate ratio for first diagnosis of urinary incontinence was 3.2 (2.7-3.7 in men and 2.7 (2.3-3.2 in women, and for faecal incontinence was 6.0 (5.1-7.0 in men and 4.5 (3.8-5.2 in women. The adjusted rate ratio for pharmacological treatment of urinary incontinence was 2.2 (1.4-3.7 for both genders, and for indwelling urinary catheters was 1.6 (1.3-1.9 in men and 2.3 (1.9-2.8 in women.Compared with those without a dementia diagnosis, those with a dementia diagnosis have approximately three times the rate of

  9. Artificial neural network cardiopulmonary modeling and diagnosis

    Science.gov (United States)

    Kangas, Lars J.; Keller, Paul E.

    1997-01-01

    The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.

  10. [Health care networks].

    Science.gov (United States)

    Mendes, Eugênio Vilaça

    2010-08-01

    The demographic and epidemiologic transition resulting from aging and the increase of life expectation means an increment related to chronic conditions. The healthcare systems contemporary crisis is characterized by the organization of the focus on fragmented systems turned to the acute conditions care, in spite of the chronic conditions prevalence, and by the hierarchical structure without communication flow among the different health care levels. Brazil health care situation profile is now presenting a triple burden of diseases, due to the concomitant presence of infectious diseases, external causes and chronic diseases. The solution is to restore the consistence between the triple burden of diseases on the health situation and the current system of healthcare practice, with the implantation of health care networks. The conclusion is that there are evidences in the international literature on health care networks that these networks may improve the clinical quality, the sanitation results and the user's satisfaction and the reduction of healthcare systems costs.

  11. Application of artificial neural network for NHR fault diagnosis

    International Nuclear Information System (INIS)

    Yu Haitao; Zhang Liangju; Xu Xiangdong

    1999-01-01

    The author makes researches on 200 MW nuclear heating reactor (NHR) fault diagnosis system using artificial neural network, and use the tendency value and real value of the data under the accidents to train and test two BP networks respectively. The final diagnostic result is the combination of the results of the two networks. The compound system can enhance the accuracy and adaptability of the diagnosis comparing to the single network system

  12. Health Participatory Sensing Networks

    Directory of Open Access Journals (Sweden)

    Andrew Clarke

    2014-01-01

    Full Text Available The use of participatory sensing in relation to the capture of health-related data is rapidly becoming a possibility due to the widespread consumer adoption of emerging mobile computing technologies and sensing platforms. This has the potential to revolutionize data collection for population health, aspects of epidemiology, and health-related e-Science applications and as we will describe, provide new public health intervention capabilities, with the classifications and capabilities of such participatory sensing platforms only just beginning to be explored. Such a development will have important benefits for access to near real-time, large-scale, up to population-scale data collection. However, there are also numerous issues to be addressed first: provision of stringent anonymity and privacy within these methodologies, user interface issues, and the related issue of how to incentivize participants and address barriers/concerns over participation. To provide a step towards describing these aspects, in this paper we present a first classification of health participatory sensing models, a novel contribution to the literature, and provide a conceptual reference architecture for health participatory sensing networks (HPSNs and user interaction example case study.

  13. Heart Health - Heart Disease: Symptoms, Diagnosis, Treatment

    Science.gov (United States)

    ... Bar Home Current Issue Past Issues Cover Story Heart Health Heart Disease: Symptoms, Diagnosis, Treatment Past Issues / Winter 2009 ... of this page please turn Javascript on. Most heart attacks happen when a clot in the coronary ...

  14. MDD diagnosis based on partial-brain functional connection network

    Science.gov (United States)

    Yan, Gaoliang; Hu, Hailong; Zhao, Xiang; Zhang, Lin; Qu, Zehui; Li, Yantao

    2018-04-01

    Artificial intelligence (AI) is a hotspot in computer science research nowadays. To apply AI technology in all industries has been the developing direction for researchers. Major depressive disorder (MDD) is a common disease of serious mental disorders. The World Health Organization (WHO) reports that MDD is projected to become the second most common cause of death and disability by 2020. At present, the way of MDD diagnosis is single. Applying AI technology to MDD diagnosis and pathophysiological research will speed up the MDD research and improve the efficiency of MDD diagnosis. In this study, we select the higher degree of brain network functional connectivity by statistical methods. And our experiments show that the average accuracy of Logistic Regression (LR) classifier using feature filtering reaches 88.48%. Compared with other classification methods, both the efficiency and accuracy of this method are improved, which will greatly improve the process of MDD diagnose. In these experiments, we also define the brain regions associated with MDD, which plays a vital role in MDD pathophysiological research.

  15. Optimized Neural Network for Fault Diagnosis and Classification

    International Nuclear Information System (INIS)

    Elaraby, S.M.

    2005-01-01

    This paper presents a developed and implemented toolbox for optimizing neural network structure of fault diagnosis and classification. Evolutionary algorithm based on hierarchical genetic algorithm structure is used for optimization. The simplest feed-forward neural network architecture is selected. Developed toolbox has friendly user interface. Multiple solutions are generated. The performance and applicability of the proposed toolbox is verified with benchmark data patterns and accident diagnosis of Egyptian Second research reactor (ETRR-2)

  16. Possibilistic networks for uncertainty knowledge processing in student diagnosis

    Directory of Open Access Journals (Sweden)

    Adina COCU

    2006-12-01

    Full Text Available In this paper, a possibilistic network implementation for uncertain knowledge modeling of the diagnostic process is proposed as a means to achieve student diagnosis in intelligent tutoring system. This approach is proposed in the object oriented programming domain for diagnosis of students learning errors and misconception. In this expertise domain dependencies between data exist that are encoded in the structure of network. Also, it is available qualitative information about these data which are represented and interpreted with qualitative approach of possibility theory. The aim of student diagnosis system is to ensure an adapted support for the student and to sustain the student in personalized learning process and errors explanation.

  17. Application of artificial neural network in radiographic diagnosis

    International Nuclear Information System (INIS)

    Piraino, D.; Amartur, S.; Richmond, B.; Schils, J.; Belhobek, G.

    1990-01-01

    This paper reports on an artificial neural network trained to rate the likelihood of different bone neoplasms when given a standard description of a radiograph. A three-layer back propagation algorithm was trained with descriptions of examples of bone neoplasms obtained from standard radiographic textbooks. Fifteen bone neoplasms obtained from clinical material were used as unknowns to test the trained artificial neural network. The artificial neural network correctly rated the pathologic diagnosis as the most likely diagnosis in 10 of the 15 unknown cases

  18. Investigation of multimedia didactic courseware of network on image diagnosis

    International Nuclear Information System (INIS)

    Yang Xiaochun; Gong Jianping; Shen Junkang; Lu Zhian; Chen Guangqiang

    2001-01-01

    Objective: To investigate the methods of the design of multimedia didactic courseware of network on image diagnosis and its characteristic. Methods: Based on the teaching material of 'image diagnosis', the images were collected with computers and scanners, and processed with graphic software, and then the multimedia didactic courseware was designed with Frontpage. Results: The design of multimedia didactic courseware of network has been completed. Domain name has been applied. Part of the courseware has been passed to the website. Conclusion: Multimedia didactic courseware of network, with bright prospects, is superior in agility of didactic style, in abundance of content, and in timeliness of information

  19. Nuclear power plant fault-diagnosis using artificial neural networks

    International Nuclear Information System (INIS)

    Kim, Keehoon; Aljundi, T.L.; Bartlett, E.B.

    1992-01-01

    Artificial neural networks (ANNs) have been applied to various fields due to their fault and noise tolerance and generalization characteristics. As an application to nuclear engineering, we apply neural networks to the early recognition of nuclear power plant operational transients. If a transient or accident occurs, the network will advise the plant operators in a timely manner. More importantly, we investigate the ability of the network to provide a measure of the confidence level in its diagnosis. In this research an ANN is trained to diagnose the status of the San Onofre Nuclear Generation Station using data obtained from the plant's training simulator. Stacked generalization is then applied to predict the error in the ANN diagnosis. The data used consisted of 10 scenarios that include typical design basis accidents as well as less severe transients. The results show that the trained network is capable of diagnosing all 10 instabilities as well as providing a measure of the level of confidence in its diagnoses

  20. A network approach to orthodontic diagnosis.

    Science.gov (United States)

    Auconi, P; Caldarelli, G; Scala, A; Ierardo, G; Polimeni, A

    2011-11-01

    Network analysis, a recent advancement in complexity science, enables understanding of the properties of complex biological processes characterized by the interaction, adaptive regulation, and coordination of a large number of participating components. We applied network analysis to orthodontics to detect and visualize the most interconnected clinical, radiographic, and functional data pertaining to the orofacial system. The sample consisted of 104 individuals from 7 to 13 years of age in the mixed dentition phase without previous orthodontic intervention. The subjects were divided according to skeletal class; their clinical, radiographic, and functional features were represented as vertices (nodes) and links (edges) connecting them. Class II subjects exhibited few highly connected orthodontic features (hubs), while Class III patients showed a more compact network structure characterized by strong co-occurrence of normal and abnormal clinical, functional, and radiological features. Restricting our analysis to the highest correlations, we identified critical peculiarities of Class II and Class III malocclusions. The topology of the dentofacial system obtained by network analysis could allow orthodontists to visually evaluate and anticipate the co-occurrence of auxological anomalies during individual craniofacial growth and possibly localize reactive sites for a therapeutic approach to malocclusion. © 2011 John Wiley & Sons A/S.

  1. Neural networks and their application to nuclear power plant diagnosis

    International Nuclear Information System (INIS)

    Reifman, J.

    1997-01-01

    The authors present a survey of artificial neural network-based computer systems that have been proposed over the last decade for the detection and identification of component faults in thermal-hydraulic systems of nuclear power plants. The capabilities and advantages of applying neural networks as decision support systems for nuclear power plant operators and their inherent characteristics are discussed along with their limitations and drawbacks. The types of neural network structures used and their applications are described and the issues of process diagnosis and neural network-based diagnostic systems are identified. A total of thirty-four publications are reviewed

  2. Neural network diagnosis of avascular necrosis from magnetic resonance images

    Science.gov (United States)

    Manduca, Armando; Christy, Paul S.; Ehman, Richard L.

    1993-09-01

    We have explored the use of artificial neural networks to diagnose avascular necrosis (AVN) of the femoral head from magnetic resonance images. We have developed multi-layer perceptron networks, trained with conjugate gradient optimization, which diagnose AVN from single sagittal images of the femoral head with 100% accuracy on the training data and 97% accuracy on test data. These networks use only the raw image as input (with minimal preprocessing to average the images down to 32 X 32 size and to scale the input data values) and learn to extract their own features for the diagnosis decision. Various experiments with these networks are described.

  3. Social Networks and Health.

    Science.gov (United States)

    Perdiaris, Christos; Chardalias, Konstantinos; Magita, Andrianna; Mechili, Aggelos E; Diomidous, Marianna

    2015-01-01

    Nowadays the social networks have been developed into an advanced communications tool, which is important for all people to contact each other. These specific networks do offer lots of options as well as plenty of advantages and disadvantages. The social websites are many in number and titles, such as the facebook, the twitter, the bandoo etc. One of the most important function-mechanisms for the social network websites, are the marketing tools. The future goal is suggested to be the evolution of these programs. The development of these applications, which is going to lead into a new era for the social digital communication between the internet users, all around the globe.

  4. Hyper-connectivity of functional networks for brain disease diagnosis.

    Science.gov (United States)

    Jie, Biao; Wee, Chong-Yaw; Shen, Dinggang; Zhang, Daoqiang

    2016-08-01

    Exploring structural and functional interactions among various brain regions enables better understanding of pathological underpinnings of neurological disorders. Brain connectivity network, as a simplified representation of those structural and functional interactions, has been widely used for diagnosis and classification of neurodegenerative diseases, especially for Alzheimer's disease (AD) and its early stage - mild cognitive impairment (MCI). However, the conventional functional connectivity network is usually constructed based on the pairwise correlation among different brain regions and thus ignores their higher-order relationships. Such loss of high-order information could be important for disease diagnosis, since neurologically a brain region predominantly interacts with more than one other brain regions. Accordingly, in this paper, we propose a novel framework for estimating the hyper-connectivity network of brain functions and then use this hyper-network for brain disease diagnosis. Here, the functional connectivity hyper-network denotes a network where each of its edges representing the interactions among multiple brain regions (i.e., an edge can connect with more than two brain regions), which can be naturally represented by a hyper-graph. Specifically, we first construct connectivity hyper-networks from the resting-state fMRI (R-fMRI) time series by using sparse representation. Then, we extract three sets of brain-region specific features from the connectivity hyper-networks, and further exploit a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Finally, we use multi-kernel support vector machine (SVM) for classification. The experimental results on both MCI dataset and attention deficit hyperactivity disorder (ADHD) dataset demonstrate that, compared with the conventional connectivity network-based methods, the proposed method can not only improve the classification performance, but also help

  5. Software Health Management with Bayesian Networks

    Science.gov (United States)

    Mengshoel, Ole; Schumann, JOhann

    2011-01-01

    Most modern aircraft as well as other complex machinery is equipped with diagnostics systems for its major subsystems. During operation, sensors provide important information about the subsystem (e.g., the engine) and that information is used to detect and diagnose faults. Most of these systems focus on the monitoring of a mechanical, hydraulic, or electromechanical subsystem of the vehicle or machinery. Only recently, health management systems that monitor software have been developed. In this paper, we will discuss our approach of using Bayesian networks for Software Health Management (SWHM). We will discuss SWHM requirements, which make advanced reasoning capabilities for the detection and diagnosis important. Then we will present our approach to using Bayesian networks for the construction of health models that dynamically monitor a software system and is capable of detecting and diagnosing faults.

  6. Molecular network topology and reliability for multipurpose diagnosis

    Directory of Open Access Journals (Sweden)

    Jalil MA

    2011-10-01

    Full Text Available MA Jalil1, N Moongfangklang2,3, K Innate4, S Mitatha3, J Ali5, PP Yupapin41Ibnu Sina Institute of Fundamental Science Studies, Nanotechnology Research Alliance, University of Technology Malaysia, Johor Bahru, Malaysia; 2School of Information and Communication Technology, Phayao University, Phayao, Thailand; 3Hybrid Computing Research Laboratory, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand; 4Nanoscale Science and Engineering Research Alliance, Advanced Research Center for Photonics, Faculty of Science, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand; 5Institute of Advanced Photonics Science, Nanotechnology Research Alliance, University of Technology Malaysia, Johor Bahru, MalaysiaAbstract: This investigation proposes the use of molecular network topology for drug delivery and diagnosis network design. Three modules of molecular network topologies, such as bus, star, and ring networks, are designed and manipulated based on a micro- and nanoring resonator system. The transportation of the trapping molecules by light in the network is described and the theoretical background is reviewed. The quality of the network is analyzed and calculated in terms of signal transmission (ie, signal to noise ratio and crosstalk effects. Results obtained show that a bus network has advantages over star and ring networks, where the use of mesh networks is possible. In application, a thin film network can be fabricated in the form of a waveguide and embedded in artificial bone, which can be connected to the required drug targets. The particular drug/nutrient can be transported to the required targets via the particular network used.Keywords: molecular network, network reliability, network topology, drug network, multi-access network

  7. Gear Fault Diagnosis Based on BP Neural Network

    Science.gov (United States)

    Huang, Yongsheng; Huang, Ruoshi

    2018-03-01

    Gear transmission is more complex, widely used in machinery fields, which form of fault has some nonlinear characteristics. This paper uses BP neural network to train the gear of four typical failure modes, and achieves satisfactory results. Tested by using test data, test results have an agreement with the actual results. The results show that the BP neural network can effectively solve the complex state of gear fault in the gear fault diagnosis.

  8. Decision Support System for Hepatitis Disease Diagnosis using Bayesian Network

    Directory of Open Access Journals (Sweden)

    Shamshad Lakho

    2017-12-01

    Full Text Available Medical judgments are tough and challenging as the decisions are often based on the deficient and ambiguous information. Moreover, the result of decision process has direct effects on human lives. The act of human decision declines in emergency situations due to the complication, time limit, and high risks. Therefore, provision of medical diagnosis plays a dynamic role, specifically in the preliminary stage when a physician has limited diagnosis experience and identifies the directions to be taken for the treatment process. Computerized Decision Support Systems have brought a revolution in the medical diagnosis. These automatic systems support the diagnosticians in the course of diagnosis. The major role of Decision Support Systems is to support the medical personnel in decision-making procedures regarding disease diagnosis and treatment recommendation. The proposed system provides easy support in Hepatitis disease recognition. The system is developed using the Bayesian network model. The physician provides the input to the system in the form of symptoms stated by the patient. These signs and symptoms match with the casual relationships present in the knowledge model. The Bayesian network infers conclusion from the knowledge model and calculates the probability of occurrence of Hepatitis B, C and D disorders.

  9. Development of nuclear power plant diagnosis technique using neural networks

    International Nuclear Information System (INIS)

    Horiguchi, Masahiro; Fukawa, Naohiro; Nishimura, Kazuo

    1991-01-01

    A nuclear power plant diagnosis technique has been developed, called transient phenomena analysis, which employs neural network. The neural networks identify malfunctioning equipment by recognizing the pattern of main plant parameters, making it possible to locate the cause of an abnormality when a plant is in a transient state. In a case where some piece of equipment shows abnormal behavior, many plant parameters either directly or indirectly related to that equipment change simultaneously. When an abrupt change in a plant parameter is detected, changes in the 49 main plant parameters are classified into three types and a characteristic change pattern consisting of 49 data is defined. The neural networks then judge the cause of the abnormality from this pattern. This neural-network-based technique can recognize 100 patterns that are characterized by the causes of plant abnormality. (author)

  10. Privacy-Preserving Self-Helped Medical Diagnosis Scheme Based on Secure Two-Party Computation in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yi Sun

    2014-01-01

    Full Text Available With the continuing growth of wireless sensor networks in pervasive medical care, people pay more and more attention to privacy in medical monitoring, diagnosis, treatment, and patient care. On one hand, we expect the public health institutions to provide us with better service. On the other hand, we would not like to leak our personal health information to them. In order to balance this contradiction, in this paper we design a privacy-preserving self-helped medical diagnosis scheme based on secure two-party computation in wireless sensor networks so that patients can privately diagnose themselves by inputting a health card into a self-helped medical diagnosis ATM to obtain a diagnostic report just like drawing money from a bank ATM without revealing patients’ health information and doctors’ diagnostic skill. It makes secure self-helped disease diagnosis feasible and greatly benefits patients as well as relieving the heavy pressure of public health institutions.

  11. Combining morphometric features and convolutional networks fusion for glaucoma diagnosis

    Science.gov (United States)

    Perdomo, Oscar; Arevalo, John; González, Fabio A.

    2017-11-01

    Glaucoma is an eye condition that leads to loss of vision and blindness. Ophthalmoscopy exam evaluates the shape, color and proportion between the optic disc and physiologic cup, but the lack of agreement among experts is still the main diagnosis problem. The application of deep convolutional neural networks combined with automatic extraction of features such as: the cup-to-disc distance in the four quadrants, the perimeter, area, eccentricity, the major radio, the minor radio in optic disc and cup, in addition to all the ratios among the previous parameters may help with a better automatic grading of glaucoma. This paper presents a strategy to merge morphological features and deep convolutional neural networks as a novel methodology to support the glaucoma diagnosis in eye fundus images.

  12. To what extent does diagnosis matter? Dementia diagnosis, trouble interpretation and caregiving network dynamics.

    Science.gov (United States)

    Brossard, Baptiste; Carpentier, Normand

    2017-05-01

    Contemporary research into health and mental health treats diagnosis as a central step in understanding illness management and trajectory; consequently, in the last two decades, sociology of diagnosis has attained increasing influence within medical sociology. Deeply embedded in social constructionism, the set of research divides between those who focus on the social and historical construction of diagnoses as categories, and those who see diagnosis as a process. Regarding the latter, this approach explores the constitution of the medical production, highlighting how it constitutes a starting point for entering a 'sick role', for being labelled, for naming one's problem and by extension, for framing one's illness narrative. © 2016 Foundation for the Sociology of Health & Illness.

  13. CDC National Environmental Public Health Tracking Network (Tracking Network)

    Data.gov (United States)

    U.S. Department of Health & Human Services — The National Environmental Public Health Tracking Network is a system of integrated health, exposure, and hazard information and data from a variety of national,...

  14. [Linking: relationships between health professionals in the informal health networks].

    Science.gov (United States)

    Sarradon-Eck, A; Vega, A; Faure, M; Humbert-Gaudart, A; Lustman, M

    2008-07-01

    During the last years, the french health system has been developing formal health networks. So, it was necessary to study informal health networks as networks. More precisely, we studied the nature of relationships between various stakeholders around general practionners wich are commonly considering as the stakeholder of the health system private sector. Fieldwork (ethnography based on direct observations and interviews) was conducted between October 2002 and april 2004, in the South-East of France. Ten monographs of general practioner's offices were achieved in a rural area; then, we achieved fieldwork of the informal health networks identified. There is a cultural frame wich is common to all private professionals. This frame includes a triple ideal (teamwork built up the hospital model, independance, and an relational approach with patients). This frame does not square with the real practices. In fact, regulation mechanisms preserve the balance of relashionships between professionnal groups, by restricting/promoting exchanges and complex alliance strategies. These mecanisms include: (1) a few professionnal's rule as disponibility (to the patients and to the professionnals), as communication about patient, as patient's reference, as obligation to communicate between professionals; (2) some constraints such as territory superposition and competition with other professional groups; (3) some needs for: rileiving (of emotions and worries connected to work), sharing (decisions, responsabilities), of delegation (medical treatment, practices), protection against social and legal risk through the creation of trust relationships. These trust relationships are based on several logics (affinity, solidarity, similarity). The study shows the major place of the patient who is often the main organizer of his network, and even though he makes an important structuring work between medical staff, and an information transfer (on his diagnosis, on his treatment, and professionals

  15. Uncertainty management using bayesian networks in student knowledge diagnosis

    Directory of Open Access Journals (Sweden)

    Adina COCU

    2005-12-01

    Full Text Available In intelligent tutoring systems, student or user modeling implies dealing with imperfect and uncertain knowledge. One of the artificial intelligence techniques used for uncertainty management is that of Bayesian networks. This paradigm is recommended in the situation when exist dependencies between data and qualitative information about these data. In this work we present a student knowledge diagnosis model based on representation with Bayesian networks. The educational system incorporate a multimedia interface for accomplishes the testing tools. The results of testing sessions are represented and interpreted with probability theory in order to ensure an adapted support for the student. The aims of the computer assisted application that contains this diagnose module are to support the student in personalized learning process and errors explanation.

  16. Computer-aided diagnosis workstation and network system for chest diagnosis based on multislice CT images

    Science.gov (United States)

    Satoh, Hitoshi; Niki, Noboru; Eguchi, Kenji; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru

    2008-03-01

    Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images, a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification and a vertebra body analysis algorithm for quantitative evaluation of osteoporosis likelihood by using helical CT scanner for the lung cancer mass screening. The function to observe suspicious shadow in detail are provided in computer-aided diagnosis workstation with these screening algorithms. We also have developed the telemedicine network by using Web medical image conference system with the security improvement of images transmission, Biometric fingerprint authentication system and Biometric face authentication system. Biometric face authentication used on site of telemedicine makes "Encryption of file" and Success in login" effective. As a result, patients' private information is protected. Based on these diagnostic assistance methods, we have developed a new computer-aided workstation and a new telemedicine network that can display suspected lesions three-dimensionally in a short time. The results of this study indicate that our radiological information system without film by using computer-aided diagnosis workstation and our telemedicine network system can increase diagnostic speed, diagnostic accuracy and security improvement of medical information.

  17. Development of an accident diagnosis system using a dynamic neural network for nuclear power plants

    International Nuclear Information System (INIS)

    Lee, Seung Jun; Kim, Jong Hyun; Seong, Poong Hyun

    2004-01-01

    In this work, an accident diagnosis system using the dynamic neural network is developed. In order to help the plant operators to quickly identify the problem, perform diagnosis and initiate recovery actions ensuring the safety of the plant, many operator support system and accident diagnosis systems have been developed. Neural networks have been recognized as a good method to implement an accident diagnosis system. However, conventional accident diagnosis systems that used neural networks did not consider a time factor sufficiently. If the neural network could be trained according to time, it is possible to perform more efficient and detailed accidents analysis. Therefore, this work suggests a dynamic neural network which has different features from existing dynamic neural networks. And a simple accident diagnosis system is implemented in order to validate the dynamic neural network. After training of the prototype, several accident diagnoses were performed. The results show that the prototype can detect the accidents correctly with good performances

  18. Fault diagnosis method for nuclear power plants based on neural networks and voting fusion

    International Nuclear Information System (INIS)

    Zhou Gang; Ge Shengqi; Yang Li

    2010-01-01

    A new fault diagnosis method based on multiple neural networks (ANNs) and voting fusion for nuclear power plants (NPPs) was proposed in view of the shortcoming of single neural network fault diagnosis method. In this method, multiple neural networks that the types of neural networks were different were trained for the fault diagnosis of NPP. The operation parameters of NPP, which have important affect on the safety of NPP, were selected as the input variable of neural networks. The output of neural networks is fault patterns of NPP. The last results of diagnosis for NPP were obtained by fusing the diagnosing results of different neural networks by voting fusion. The typical operation patterns of NPP were diagnosed to demonstrate the effect of the proposed method. The results show that employing the proposed diagnosing method can improve the precision and reliability of fault diagnosis results of NPPs. (authors)

  19. [Public health impact of a remote diagnosis system implemented in regional and district hospitals in Paraguay].

    Science.gov (United States)

    Galván, Pedro; Velázquez, Miguel; Benítez, Gualberto; Ortellado, José; Rivas, Ronald; Barrios, Antonio; Hilario, Enrique

    2017-06-08

    Determine the viability of a remote diagnosis system implemented to provide health care to remote and scattered populations in Paraguay. The study was conducted in all regional and general hospitals in Paraguay, and in the main district hospitals in the country's 18 health regions. Clinical data, tomographic images, sonography, and electrocardiograms (ECGs) of patients who needed a diagnosis by a specialized physician were entered into the system. This information was sent to specialists in diagnostic imaging and in cardiology for remote diagnosis and the report was then forwarded to the hospitals connected to the system. The cost-benefit and impact of the remote diagnosis tool was analyzed from the perspective of the National Health System. Between January 2014 and May 2015, a total of 34 096 remote diagnoses were made in 25 hospitals in the Ministry of Health's telemedicine system. The average unit cost of remote diagnosis was US$2.6 per ECG, tomography, and sonography, while the unit cost of "face-to-face" diagnosis was US$11.8 per ECG, US$68.6 per tomography, and US$21.5 per sonography. As a result of remote diagnosis, unit costs were 4.5 times lower for ECGs; 26.4 times lower for tomography, and 8.3 times lower for sonography. In monetary terms, implementation of the remote diagnosis system during the 16 months of the study led to average savings of US$2 420 037. Paraguay has a remote diagnosis system for electrocardiography, tomography, and sonography, using low-cost information and communications technologies (ICTs) based on free software that is scalable to other types of remote diagnostic studies of interest for public health. Implementation of remote diagnosis helped to strengthen the integrated network of health services and programs, enabling professionals to optimize their time and productivity, while improving quality, increasing access and equity, and reducing costs.

  20. A pilot study on diagnostic sensor networks for structure health monitoring.

    Science.gov (United States)

    2013-08-01

    The proposal was submitted in an effort to obtain some preliminary results on using sensor networks for real-time structure health : monitoring. The proposed work has twofold: to develop and validate an elective algorithm for the diagnosis of coupled...

  1. The Nordic Health Promotion Research Network (NHPRN).

    Science.gov (United States)

    Ringsberg, Karin C

    2015-08-01

    The Nordic Health Promotion Research Network (NHPRN) was established in 2007 at the Nordic School of Public Health (NHV). This article aims to describe the foundation of the NHPRN, the development and the present status of the work of NHPRN. The NHPRN consists of about 50 senior and junior researchers from all Nordic countries. It is a working network that aims to develop the theoretical understanding of health promotion, to create research cooperation in health promotion from a Nordic perspective and to extend the scope of health promotion through education. Network members meet biannually to discuss and further develop research within the field and are also responsible for the Nordic conference on Health Promotion, organized every 3 years. The NHV hosted the network between 2007 and 2014; and the World Health Organisation (WHO) will assume this role in 2015. © 2015 the Nordic Societies of Public Health.

  2. Diagnosis of Constant Faults in Read-Once Contact Networks over Finite Bases using Decision Trees

    KAUST Repository

    Busbait, Monther I.

    2014-05-01

    We study the depth of decision trees for diagnosis of constant faults in read-once contact networks over finite bases. This includes diagnosis of 0-1 faults, 0 faults and 1 faults. For any finite basis, we prove a linear upper bound on the minimum depth of decision tree for diagnosis of constant faults depending on the number of edges in a contact network over that basis. Also, we obtain asymptotic bounds on the depth of decision trees for diagnosis of each type of constant faults depending on the number of edges in contact networks in the worst case per basis. We study the set of indecomposable contact networks with up to 10 edges and obtain sharp coefficients for the linear upper bound for diagnosis of constant faults in contact networks over bases of these indecomposable contact networks. We use a set of algorithms, including one that we create, to obtain the sharp coefficients.

  3. An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis.

    Science.gov (United States)

    Li, Shaobo; Liu, Guokai; Tang, Xianghong; Lu, Jianguang; Hu, Jianjun

    2017-07-28

    Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster-Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions.

  4. Critical Review of Dual Diagnosis Training for Mental Health Professionals

    DEFF Research Database (Denmark)

    Pinderup, Pernille; Thylstrup, Birgitte; Hesse, Morten

    2016-01-01

    To review evidence on the effects of training programs in dual diagnosis treatment for mental health professionals. Three databases were searched. Included studies were evaluated by an adapted version of Kirkpatrick’s Training Evaluation Model, which evaluates participant perception of training, ...... of dual diagnosis training programs for mental health professionals should involve control groups, validated measures, follow-ups, and patient outcomes.......To review evidence on the effects of training programs in dual diagnosis treatment for mental health professionals. Three databases were searched. Included studies were evaluated by an adapted version of Kirkpatrick’s Training Evaluation Model, which evaluates participant perception of training...... level showed mixed results. Training mental health professionals in dual diagnosis treatment may have a positive effect on professional competencies and clinical practice. Any conclusion regarding the overall training effect is premature due to limitations in study designs. Future studies on the effects...

  5. Multiple simultaneous fault diagnosis via hierarchical and single artificial neural networks

    International Nuclear Information System (INIS)

    Eslamloueyan, R.; Shahrokhi, M.; Bozorgmehri, R.

    2003-01-01

    Process fault diagnosis involves interpreting the current status of the plant given sensor reading and process knowledge. There has been considerable work done in this area with a variety of approaches being proposed for process fault diagnosis. Neural networks have been used to solve process fault diagnosis problems in chemical process, as they are well suited for recognizing multi-dimensional nonlinear patterns. In this work, the use of Hierarchical Artificial Neural Networks in diagnosing the multi-faults of a chemical process are discussed and compared with that of Single Artificial Neural Networks. The lower efficiency of Hierarchical Artificial Neural Networks , in comparison to Single Artificial Neural Networks, in process fault diagnosis is elaborated and analyzed. Also, the concept of a multi-level selection switch is presented and developed to improve the performance of hierarchical artificial neural networks. Simulation results indicate that application of multi-level selection switch increase the performance of the hierarchical artificial neural networks considerably

  6. Social Networks and Health Knowledge in India

    DEFF Research Database (Denmark)

    Blunch, Niels-Hugo; Datta Gupta, Nabanita

    such as education and access to social networks explain part of the gap, a substantial part of the health knowledge gap is left unexplained. All groups have greater health knowledge in urban than in rural areas, but the gap is even wider in urban than in rural areas. Additionally, high caste women benefit more...... in terms of health knowledge from having health networks than women from other groups; except if the health person is of the same caste/religion, in which case low caste and Muslim women sometimes benefit by as much as double that of high caste women, or even more. It may therefore not be enough to give...... individuals access to high quality networks if caste and religion-related gaps in health knowledge are to be reduced; such networks also have to be homophilous, to have the maximum effect. Improved treatment from and confidence in the medical profession is found to be part of the mechanism linking health...

  7. Malaria diagnosis and mapping with m-Health and geographic information systems (GIS): evidence from Uganda.

    Science.gov (United States)

    Larocca, Alberto; Moro Visconti, Roberto; Marconi, Michele

    2016-10-24

    Rural populations experience several barriers to accessing clinical facilities for malaria diagnosis. Increasing penetration of ICT and mobile-phones and subsequent m-Health applications can contribute overcoming such obstacles. GIS is used to evaluate the feasibility of m-Health technologies as part of anti-malaria strategies. This study investigates where in Uganda: (1) malaria affects the largest number of people; (2) the application of m-Health protocol based on the mobile network has the highest potential impact. About 75% of the population affected by Plasmodium falciparum malaria have scarce access to healthcare facilities. The introduction of m-Health technologies should be based on the 2G protocol, as 3G mobile network coverage is still limited. The western border and the central-Southeast are the regions where m-Health could reach the largest percentage of the remote population. Six districts (Arua, Apac, Lira, Kamuli, Iganga, and Mubende) could have the largest benefit because they account for about 28% of the remote population affected by falciparum malaria with access to the 2G mobile network. The application of m-Health technologies could improve access to medical services for distant populations. Affordable remote malaria diagnosis could help to decongest health facilities, reducing costs and contagion. The combination of m-Health and GIS could provide real-time and geo-localized data transmission, improving anti-malarial strategies in Uganda. Scalability to other countries and diseases looks promising.

  8. Malaria diagnosis and mapping with m-Health and geographic information systems (GIS: evidence from Uganda

    Directory of Open Access Journals (Sweden)

    Alberto Larocca

    2016-10-01

    Full Text Available Abstract Background Rural populations experience several barriers to accessing clinical facilities for malaria diagnosis. Increasing penetration of ICT and mobile-phones and subsequent m-Health applications can contribute overcoming such obstacles. Methods GIS is used to evaluate the feasibility of m-Health technologies as part of anti-malaria strategies. This study investigates where in Uganda: (1 malaria affects the largest number of people; (2 the application of m-Health protocol based on the mobile network has the highest potential impact. Results About 75% of the population affected by Plasmodium falciparum malaria have scarce access to healthcare facilities. The introduction of m-Health technologies should be based on the 2G protocol, as 3G mobile network coverage is still limited. The western border and the central-Southeast are the regions where m-Health could reach the largest percentage of the remote population. Six districts (Arua, Apac, Lira, Kamuli, Iganga, and Mubende could have the largest benefit because they account for about 28% of the remote population affected by falciparum malaria with access to the 2G mobile network. Conclusions The application of m-Health technologies could improve access to medical services for distant populations. Affordable remote malaria diagnosis could help to decongest health facilities, reducing costs and contagion. The combination of m-Health and GIS could provide real-time and geo-localized data transmission, improving anti-malarial strategies in Uganda. Scalability to other countries and diseases looks promising.

  9. Automated diagnosis of rolling bearings using MRA and neural networks

    Science.gov (United States)

    Castejón, C.; Lara, O.; García-Prada, J. C.

    2010-01-01

    Any industry needs an efficient predictive plan in order to optimize the management of resources and improve the economy of the plant by reducing unnecessary costs and increasing the level of safety. A great percentage of breakdowns in productive processes are caused by bearings. They begin to deteriorate from early stages of their functional life, also called the incipient level. This manuscript develops an automated diagnosis of rolling bearings based on the analysis and classification of signature vibrations. The novelty of this work is the application of the methodology proposed for data collected from a quasi-real industrial machine, where rolling bearings support the radial and axial loads the bearings are designed for. Multiresolution analysis (MRA) is used in a first stage in order to extract the most interesting features from signals. Features will be used in a second stage as inputs of a supervised neural network (NN) for classification purposes. Experimental results carried out in a real system show the soundness of the method which detects four bearing conditions (normal, inner race fault, outer race fault and ball fault) in a very incipient stage.

  10. Motor Fault Diagnosis Based on Short-time Fourier Transform and Convolutional Neural Network

    Science.gov (United States)

    Wang, Li-Hua; Zhao, Xiao-Ping; Wu, Jia-Xin; Xie, Yang-Yang; Zhang, Yong-Hong

    2017-11-01

    With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and poor accuracy, when handling big data. In this study, the research object was the asynchronous motor in the drivetrain diagnostics simulator system. The vibration signals of different fault motors were collected. The raw signal was pretreated using short time Fourier transform (STFT) to obtain the corresponding time-frequency map. Then, the feature of the time-frequency map was adaptively extracted by using a convolutional neural network (CNN). The effects of the pretreatment method, and the hyper parameters of network diagnostic accuracy, were investigated experimentally. The experimental results showed that the influence of the preprocessing method is small, and that the batch-size is the main factor affecting accuracy and training efficiency. By investigating feature visualization, it was shown that, in the case of big data, the extracted CNN features can represent complex mapping relationships between signal and health status, and can also overcome the prior knowledge and engineering experience requirement for feature extraction, which is used by traditional diagnosis methods. This paper proposes a new method, based on STFT and CNN, which can complete motor fault diagnosis tasks more intelligently and accurately.

  11. A tripartite regulation of health networks.

    Science.gov (United States)

    Weil, T P; Jorgensen, N E

    1996-01-01

    With the Republicans in power, market-driven forces of managed care plans, capitated payment, and the regional networks (alliances) are likely to serve as centerpieces for improving the organization, financing, and delivery of our nation's health services. These "voluntary" alliances of health providers and health insurance underwriters foreshadow the powerful, geographically linked regional health networks that are now becoming oligopolies. As a result of these providers developing monopolistic practices, state health services commissions will be formed to regulate market share, the scope of health services, reimbursement rates, and profits. State departments of public health will continue to focus on broader community health initiatives such as access and quality. Complexities of relationships among those regulated by these responsible agencies, and the interfacing of these state health services commissions and state departments of public health and insurance, with their potentially conflicting goals and political forces, are expected.

  12. 78 FR 17418 - Rural Health Information Technology Network Development Grant

    Science.gov (United States)

    2013-03-21

    ... Information Technology Network Development Grant AGENCY: Health Resources and Services Administration (HRSA...-competitive replacement award under the Rural Health Information Technology Network Development Grant (RHITND... relinquishing its fiduciary responsibilities for the Rural Health Information Technology Network Development...

  13. ACO-Initialized Wavelet Neural Network for Vibration Fault Diagnosis of Hydroturbine Generating Unit

    Directory of Open Access Journals (Sweden)

    Zhihuai Xiao

    2015-01-01

    Full Text Available Considering the drawbacks of traditional wavelet neural network, such as low convergence speed and high sensitivity to initial parameters, an ant colony optimization- (ACO- initialized wavelet neural network is proposed in this paper for vibration fault diagnosis of a hydroturbine generating unit. In this method, parameters of the wavelet neural network are initialized by the ACO algorithm, and then the wavelet neural network is trained by the gradient descent algorithm. Amplitudes of the frequency components of the hydroturbine generating unit vibration signals are used as feature vectors for wavelet neural network training to realize mapping relationship from vibration features to fault types. A real vibration fault diagnosis case result of a hydroturbine generating unit shows that the proposed method has faster convergence speed and stronger generalization ability than the traditional wavelet neural network and ACO wavelet neural network. Thus it can provide an effective solution for online vibration fault diagnosis of a hydroturbine generating unit.

  14. Fault detection and diagnosis for complex multivariable processes using neural networks

    International Nuclear Information System (INIS)

    Weerasinghe, M.

    1998-06-01

    Development of a reliable fault diagnosis method for large-scale industrial plants is laborious and often difficult to achieve due to the complexity of the targeted systems. The main objective of this thesis is to investigate the application of neural networks to the diagnosis of non-catastrophic faults in an industrial nuclear fuel processing plant. The proposed methods were initially developed by application to a simulated chemical process prior to further validation on real industrial data. The diagnosis of faults at a single operating point is first investigated. Statistical data conditioning methods of data scaling and principal component analysis are investigated to facilitate fault classification and reduce the complexity of neural networks. Successful fault diagnosis was achieved with significantly smaller networks than using all process variables as network inputs. Industrial processes often manufacture at various operating points, but demonstrated applications of neural networks for fault diagnosis usually only consider a single (primary) operating point. Developing a standard neural network scheme for fault diagnosis at all operating points would be usually impractical due to the unavailability of suitable training data for less frequently used (secondary) operating points. To overcome this problem, the application of a single neural network for the diagnosis of faults operating at different points is investigated. The data conditioning followed the same techniques as used for the fault diagnosis of a single operating point. The results showed that a single neural network could be successfully used to diagnose faults at operating points other than that it is trained for, and the data conditioning significantly improved the classification. Artificial neural networks have been shown to be an effective tool for process fault diagnosis. However, a main criticism is that details of the procedures taken to reach the fault diagnosis decisions are embedded in

  15. Advanced neural network-based computational schemes for robust fault diagnosis

    CERN Document Server

    Mrugalski, Marcin

    2014-01-01

    The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practica...

  16. Service network analysis for agricultural mental health

    Directory of Open Access Journals (Sweden)

    Fuller Jeffrey D

    2009-05-01

    Full Text Available Abstract Background Farmers represent a subgroup of rural and remote communities at higher risk of suicide attributed to insecure economic futures, self-reliant cultures and poor access to health services. Early intervention models are required that tap into existing farming networks. This study describes service networks in rural shires that relate to the mental health needs of farming families. This serves as a baseline to inform service network improvements. Methods A network survey of mental health related links between agricultural support, health and other human services in four drought declared shires in comparable districts in rural New South Wales, Australia. Mental health links covered information exchange, referral recommendations and program development. Results 87 agencies from 111 (78% completed a survey. 79% indicated that two thirds of their clients needed assistance for mental health related problems. The highest mean number of interagency links concerned information exchange and the frequency of these links between sectors was monthly to three monthly. The effectiveness of agricultural support and health sector links were rated as less effective by the agricultural support sector than by the health sector (p Conclusion Aligning with agricultural agencies is important to build effective mental health service pathways to address the needs of farming populations. Work is required to ensure that these agricultural support agencies have operational and effective links to primary mental health care services. Network analysis provides a baseline to inform this work. With interventions such as local mental health training and joint service planning to promote network development we would expect to see over time an increase in the mean number of links, the frequency in which these links are used and the rated effectiveness of these links.

  17. Diagnosis of constant faults in read-once contact networks over finite bases

    KAUST Repository

    Busbait, Monther I.; Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail

    2015-01-01

    We study the depth of decision trees for diagnosis of constant 0 and 1 faults in read-once contact networks over finite bases containing only indecomposable networks. For each basis, we obtain a linear upper bound on the minimum depth of decision trees depending on the number of edges in the networks. For bases containing networks with at most 10 edges we find coefficients for linear bounds which are close to sharp. © 2014 Elsevier B.V. All rights reserved.

  18. ACO-Initialized Wavelet Neural Network for Vibration Fault Diagnosis of Hydroturbine Generating Unit

    OpenAIRE

    Xiao, Zhihuai; He, Xinying; Fu, Xiangqian; Malik, O. P.

    2015-01-01

    Considering the drawbacks of traditional wavelet neural network, such as low convergence speed and high sensitivity to initial parameters, an ant colony optimization- (ACO-) initialized wavelet neural network is proposed in this paper for vibration fault diagnosis of a hydroturbine generating unit. In this method, parameters of the wavelet neural network are initialized by the ACO algorithm, and then the wavelet neural network is trained by the gradient descent algorithm. Amplitudes of the fr...

  19. Diagnosis of constant faults in read-once contact networks over finite bases

    KAUST Repository

    Busbait, Monther I.

    2015-03-01

    We study the depth of decision trees for diagnosis of constant 0 and 1 faults in read-once contact networks over finite bases containing only indecomposable networks. For each basis, we obtain a linear upper bound on the minimum depth of decision trees depending on the number of edges in the networks. For bases containing networks with at most 10 edges we find coefficients for linear bounds which are close to sharp. © 2014 Elsevier B.V. All rights reserved.

  20. Diagnosis of three types of constant faults in read-once contact networks over finite bases

    KAUST Repository

    Busbait, Monther I.

    2016-03-24

    We study the depth of decision trees for diagnosis of three types of constant faults in read-once contact networks over finite bases containing only indecomposable networks. For each basis and each type of faults, we obtain a linear upper bound on the minimum depth of decision trees depending on the number of edges in networks. For bases containing networks with at most 10 edges, we find sharp coefficients for linear bounds.

  1. Health care's ills: A Catholic diagnosis

    Science.gov (United States)

    Sibley, Angus

    2016-01-01

    Catholic teaching is emphatic on the need to “guarantee adequate [health] care to all,” as Pope Benedict XVI has stated. America has been slower than other advanced countries in progressing towards this goal. Reasons for this delay can be found in certain attitudes that have long been present in American culture, and have been reinforced by the wave of libertarianism (free-market ideology) that swept the world in the late twentieth century. Catholic theology and social/economic teaching can help us understand the flaws in these attitudes, which involve fundamental philosophical and theological principles, but which are far from academic, since they have serious and very practical consequences. In the light of Catholic teaching, we can look towards a sounder understanding of healthcare needs and effective ways of meeting them. Lay Summary: This article argues that access to healthcare, at least up to the level of basic necessity, falls under the heading of distributive justice. It is a human right owed by the community to each of its citizens. And since rights entail obligations, this right entails an obligation upon each citizen to contribute, as circumstances permit, to the costs, which need to be shared equitably; they cannot be met simply by each individual providing solely for oneself. Also discussed are the problems of excessive costs in healthcare administration and in pharmacological research, as well as harmful tendencies in private-sector firms to over-reward top management and to target maximum (rather than adequate) profits. PMID:28392590

  2. Data for decision making in networked health

    Directory of Open Access Journals (Sweden)

    Christian Bourret

    2006-06-01

    Full Text Available In developed countries, nowadays we live in a networked society: a society of information, knowledge and services (Castells, 1996, with strong specificities in the Health field (Bourret, 2003, Silber, 2003. The World Health Organization (WHO has outlined the importance of information for improving health for all. However, financial resources remain limited. Health costs represent 11% of GNP in France, Germany, Switzerland and Canada, 14% in the USA, and 7.5% in Spain and the United Kingdom. Governments, local powers, health or insurance organizations therefore face difficult choices in terms of opportunities and priorities, and for that they need specific and valuable data. Firstly, this paper provide a comprehensive overview of our networked society and the appointment of ICT (Information and Communication Technologies and Health (in other words e-Health in a perspective of needs and uses at the micro, meso, and macro levels. We point out the main challenges of development of Nationwide Health Information Network both in the US, UK and France. Then we analyze the main issues about data for Decision Making in Networked Health: coordination and evaluation. In the last sections, we use an Information System perspective to investigate the three interoperability layers (micro, meso and macro. We analyze the requirements and challenges to design an interoperability global architecture which supports different kinds of interactions; then we focus on the harmonization efforts provided at several levels. Finally, we identify common methodological and engineering issues.

  3. Research on Fault Diagnosis Method Based on Rule Base Neural Network

    Directory of Open Access Journals (Sweden)

    Zheng Ni

    2017-01-01

    Full Text Available The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method.

  4. Application of Integrated Neural Network Method to Fault Diagnosis of Nuclear Steam Generator

    International Nuclear Information System (INIS)

    Zhou Gang; Yang Li

    2009-01-01

    A new fault diagnosis method based on integrated neural networks for nuclear steam generator (SG) was proposed in view of the shortcoming of the conventional fault monitoring and diagnosis method. In the method, two neural networks (ANNs) were employed for the fault diagnosis of steam generator. A neural network, which was used for predicting the values of steam generator operation parameters, was taken as the dynamics model of steam generator. The principle of fault monitoring method using the neural network model is to detect the deviations between process signals measured from an operating steam generator and corresponding output signals from the neural network model of steam generator. When the deviation exceeds the limit set in advance, the abnormal event is thought to occur. The other neural network as a fault classifier conducts the fault classification of steam generator. So, the fault types of steam generator are given by the fault classifier. The clear information on steam generator faults was obtained by fusing the monitoring and diagnosis results of two neural networks. The simulation results indicate that employing integrated neural networks can improve the capacity of fault monitoring and diagnosis for the steam generator. (authors)

  5. Future Earth Health Knowledge-Action Network.

    Science.gov (United States)

    Shrivastava, Paul; Raivio, Kari; Kasuga, Fumiko; Tewksbury, Joshua; Haines, Andy; Daszak, Peter

    Future Earth is an international research platform providing the knowledge and support to accelerate our transformations to a sustainable world. Future Earth 2025 Vision identified eight key focal challenges, and challenge #6 is to "Improve human health by elucidating, and finding responses to, the complex interactions amongst environmental change, pollution, pathogens, disease vectors, ecosystem services, and people's livelihoods, nutrition and well-being." Several studies, including the Rockefeller Foundation/Lancet Planetary Health Commission Report of 2015, the World Health Organization/Convention on Biological Diversity report and those by oneHEALTH (former ecoHEALTH), have been conducted over the last 30 years. Knowledge-Action Networks (KANs) are the frameworks to apply Future Earth principles of research to related activities that respond to societal challenges. Future Earth Health Knowledge-Action Network will connect health researchers with other natural and social scientists, health and environmental policy professionals and leaders in government, the private sector and civil society to provide research-based solutions based on better, integrated understanding of the complex interactions between a changing global environment and human health. It will build regional capacity to enhance resilience, protect the environment and avert serious threats to health and will also contribute to achieving Sustainable Development Goals. In addition to the initial partners, Future Earth Health Knowledge-Action Network will further nourish collaboration with other on-going, leading research programmes outside Future Earth, by encouraging them in active participation.

  6. A Survey on Proactive, Active and Passive Fault Diagnosis Protocols for WSNs: Network Operation Perspective

    Directory of Open Access Journals (Sweden)

    Amjad Mehmood

    2018-06-01

    Full Text Available Although wireless sensor networks (WSNs have been the object of research focus for the past two decades, fault diagnosis in these networks has received little attention. This is an essential requirement for wireless networks, especially in WSNs, because of their ad-hoc nature, deployment requirements and resource limitations. Therefore, in this paper we survey fault diagnosis from the perspective of network operations. To the best of our knowledge, this is the first survey from such a perspective. We survey the proactive, active and passive fault diagnosis schemes that have appeared in the literature to date, accenting their advantages and limitations of each scheme. In addition to illuminating the details of past efforts, this survey also reveals new research challenges and strengthens our understanding of the field of fault diagnosis.

  7. Model-Based Fault Diagnosis in Electric Drive Inverters Using Artificial Neural Network

    National Research Council Canada - National Science Library

    Masrur, Abul; Chen, ZhiHang; Zhang, Baifang; Jia, Hongbin; Murphey, Yi-Lu

    2006-01-01

    .... A normal model and various faulted models of the inverter-motor combination were developed, and voltages and current signals were generated from those models to train an artificial neural network for fault diagnosis...

  8. Fault Diagnosis of Hydraulic Servo Valve Based on Genetic Optimization RBF-BP Neural Network

    Directory of Open Access Journals (Sweden)

    Li-Ping FAN

    2014-04-01

    Full Text Available Electro-hydraulic servo valves are core components of the hydraulic servo system of rolling mills. It is necessary to adopt an effective fault diagnosis method to keep the hydraulic servo valve in a good work state. In this paper, RBF and BP neural network are integrated effectively to build a double hidden layers RBF-BP neural network for fault diagnosis. In the process of training the neural network, genetic algorithm (GA is used to initialize and optimize the connection weights and thresholds of the network. Several typical fault states are detected by the constructed GA-optimized fault diagnosis scheme. Simulation results shown that the proposed fault diagnosis scheme can give satisfactory effect.

  9. Research on Big Data Attribute Selection Method in Submarine Optical Fiber Network Fault Diagnosis Database

    Directory of Open Access Journals (Sweden)

    Chen Ganlang

    2017-11-01

    Full Text Available At present, in the fault diagnosis database of submarine optical fiber network, the attribute selection of large data is completed by detecting the attributes of the data, the accuracy of large data attribute selection cannot be guaranteed. In this paper, a large data attribute selection method based on support vector machines (SVM for fault diagnosis database of submarine optical fiber network is proposed. Mining large data in the database of optical fiber network fault diagnosis, and calculate its attribute weight, attribute classification is completed according to attribute weight, so as to complete attribute selection of large data. Experimental results prove that ,the proposed method can improve the accuracy of large data attribute selection in fault diagnosis database of submarine optical fiber network, and has high use value.

  10. Social network fragmentation and community health.

    Science.gov (United States)

    Chami, Goylette F; Ahnert, Sebastian E; Kabatereine, Narcis B; Tukahebwa, Edridah M

    2017-09-05

    Community health interventions often seek to intentionally destroy paths between individuals to prevent the spread of infectious diseases. Immunizing individuals through direct vaccination or the provision of health education prevents pathogen transmission and the propagation of misinformation concerning medical treatments. However, it remains an open question whether network-based strategies should be used in place of conventional field approaches to target individuals for medical treatment in low-income countries. We collected complete friendship and health advice networks in 17 rural villages of Mayuge District, Uganda. Here we show that acquaintance algorithms, i.e., selecting neighbors of randomly selected nodes, were systematically more efficient in fragmenting all networks than targeting well-established community roles, i.e., health workers, village government members, and schoolteachers. Additionally, community roles were not good proxy indicators of physical proximity to other households or connections to many sick people. We also show that acquaintance algorithms were effective in offsetting potential noncompliance with deworming treatments for 16,357 individuals during mass drug administration (MDA). Health advice networks were destroyed more easily than friendship networks. Only an average of 32% of nodes were removed from health advice networks to reduce the percentage of nodes at risk for refusing treatment in MDA to below 25%. Treatment compliance of at least 75% is needed in MDA to control human morbidity attributable to parasitic worms and progress toward elimination. Our findings point toward the potential use of network-based approaches as an alternative to role-based strategies for targeting individuals in rural health interventions.

  11. Do governance choices matter in health care networks?: an exploratory configuration study of health care networks

    Science.gov (United States)

    2013-01-01

    Background Health care networks are widely used and accepted as an organizational form that enables integrated care as well as dealing with complex matters in health care. However, research on the governance of health care networks lags behind. The research aim of our study is to explore the type and importance of governance structure and governance mechanisms for network effectiveness. Methods The study has a multiple case study design and covers 22 health care networks. Using a configuration view, combinations of network governance and other network characteristics were studied on the level of the network. Based on interview and questionnaire data, network characteristics were identified and patterns in the data looked for. Results Neither a dominant (or optimal) governance structure or mechanism nor a perfect fit among governance and other characteristics were revealed, but a number of characteristics that need further study might be related to effective networks such as the role of governmental agencies, legitimacy, and relational, hierarchical, and contractual governance mechanisms as complementary factors. Conclusions Although the results emphasize the situational character of network governance and effectiveness, they give practitioners in the health care sector indications of which factors might be more or less crucial for network effectiveness. PMID:23800334

  12. Breast Cancer Diagnosis using Artificial Neural Networks with Extreme Learning Techniques

    OpenAIRE

    Chandra Prasetyo Utomo; Aan Kardiana; Rika Yuliwulandari

    2014-01-01

    Breast cancer is the second cause of dead among women. Early detection followed by appropriate cancer treatment can reduce the deadly risk. Medical professionals can make mistakes while identifying a disease. The help of technology such as data mining and machine learning can substantially improve the diagnosis accuracy. Artificial Neural Networks (ANN) has been widely used in intelligent breast cancer diagnosis. However, the standard Gradient-Based Back Propagation Artificial Neural Networks...

  13. A Fault Diagnosis Approach for the Hydraulic System by Artificial Neural Networks

    OpenAIRE

    Xiangyu He; Shanghong He

    2014-01-01

    Based on artificial neural networks, a fault diagnosis approach for the hydraulic system was proposed in this paper. Normal state samples were used as the training data to develop a dynamic general regression neural network (DGRNN) model. The trained DGRNN model then served as the fault determinant to diagnose test faults and the work condition of the hydraulic system was identified. Several typical faults of the hydraulic system were used to verify the fault diagnosis approach. Experiment re...

  14. 42 CFR 485.603 - Rural health network.

    Science.gov (United States)

    2010-10-01

    ... 42 Public Health 5 2010-10-01 2010-10-01 false Rural health network. 485.603 Section 485.603... Participation: Critical Access Hospitals (CAHs) § 485.603 Rural health network. A rural health network is an... quality assurance with at least— (1) One hospital that is a member of the network when applicable; (2) One...

  15. Diagnosis of Constant Faults in Read-Once Contact Networks over Finite Bases using Decision Trees

    KAUST Repository

    Busbait, Monther I.

    2014-01-01

    We study the depth of decision trees for diagnosis of constant faults in read-once contact networks over finite bases. This includes diagnosis of 0-1 faults, 0 faults and 1 faults. For any finite basis, we prove a linear upper bound on the minimum

  16. Distributed Diagnosis in Uncertain Environments Using Dynamic Bayesian Networks

    Data.gov (United States)

    National Aeronautics and Space Administration — This paper presents a distributed Bayesian fault diagnosis scheme for physical systems. Our diagnoser design is based on a procedure for factoring the global system...

  17. Establishing a laboratory network of influenza diagnosis in Indonesia: an experience from the avian flu (H5N1 outbreak

    Directory of Open Access Journals (Sweden)

    Setiawaty V

    2012-08-01

    Full Text Available Vivi Setiawaty, Krisna NA Pangesti, Ondri D SampurnoNational Institute of Health Research and Development, Ministry of Health, the Republic of Indonesia, Jakarta, IndonesiaAbstract: Indonesia has been part of the global influenza surveillance since the establishment of a National Influenza Center (NIC at the National Institute of Health Research and Development (NIHRD by the Indonesian Ministry of Health in 1975. When the outbreak of avian influenza A (H5N1 occurred, the NIC and US Naval Medical Research Unit 2 were the only diagnostic laboratories equipped for etiology confirmation. The large geographical area of the Republic of Indonesia poses a real challenge to provide prompt and accurate diagnosis nationally. This was the main reason to establish a laboratory network for H5N1 diagnosis in Indonesia. Currently, 44 laboratories have been included in the network capable of performing polymerase chain reaction testing for influenza A. Diagnostic equipment and standard procedures of biosafety and biosecurity of handling specimens have been adopted largely from World Health Organization recommendations.Keywords: influenza, laboratory, networking

  18. Railway track circuit fault diagnosis using recurrent neural networks

    NARCIS (Netherlands)

    de Bruin, T.D.; Verbert, K.A.J.; Babuska, R.

    2017-01-01

    Timely detection and identification of faults in railway track circuits are crucial for the safety and availability of railway networks. In this paper, the use of the long-short-term memory (LSTM) recurrent neural network is proposed to accomplish these tasks based on the commonly available

  19. Diagnosis of three types of constant faults in read-once contact networks over finite bases

    KAUST Repository

    Busbait, Monther I.; Moshkov, Mikhail

    2016-01-01

    We study the depth of decision trees for diagnosis of three types of constant faults in read-once contact networks over finite bases containing only indecomposable networks. For each basis and each type of faults, we obtain a linear upper bound

  20. Privacy policies for health social networking sites

    Science.gov (United States)

    Li, Jingquan

    2013-01-01

    Health social networking sites (HSNS), virtual communities where users connect with each other around common problems and share relevant health data, have been increasingly adopted by medical professionals and patients. The growing use of HSNS like Sermo and PatientsLikeMe has prompted public concerns about the risks that such online data-sharing platforms pose to the privacy and security of personal health data. This paper articulates a set of privacy risks introduced by social networking in health care and presents a practical example that demonstrates how the risks might be intrinsic to some HSNS. The aim of this study is to identify and sketch the policy implications of using HSNS and how policy makers and stakeholders should elaborate upon them to protect the privacy of online health data. PMID:23599228

  1. Privacy policies for health social networking sites.

    Science.gov (United States)

    Li, Jingquan

    2013-01-01

    Health social networking sites (HSNS), virtual communities where users connect with each other around common problems and share relevant health data, have been increasingly adopted by medical professionals and patients. The growing use of HSNS like Sermo and PatientsLikeMe has prompted public concerns about the risks that such online data-sharing platforms pose to the privacy and security of personal health data. This paper articulates a set of privacy risks introduced by social networking in health care and presents a practical example that demonstrates how the risks might be intrinsic to some HSNS. The aim of this study is to identify and sketch the policy implications of using HSNS and how policy makers and stakeholders should elaborate upon them to protect the privacy of online health data.

  2. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

    Science.gov (United States)

    Jia, Feng; Lei, Yaguo; Lin, Jing; Zhou, Xin; Lu, Na

    2016-05-01

    Aiming to promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rotating machinery. Among these studies, the methods based on artificial neural networks (ANNs) are commonly used, which employ signal processing techniques for extracting features and further input the features to ANNs for classifying faults. Though these methods did work in intelligent fault diagnosis of rotating machinery, they still have two deficiencies. (1) The features are manually extracted depending on much prior knowledge about signal processing techniques and diagnostic expertise. In addition, these manual features are extracted according to a specific diagnosis issue and probably unsuitable for other issues. (2) The ANNs adopted in these methods have shallow architectures, which limits the capacity of ANNs to learn the complex non-linear relationships in fault diagnosis issues. As a breakthrough in artificial intelligence, deep learning holds the potential to overcome the aforementioned deficiencies. Through deep learning, deep neural networks (DNNs) with deep architectures, instead of shallow ones, could be established to mine the useful information from raw data and approximate complex non-linear functions. Based on DNNs, a novel intelligent method is proposed in this paper to overcome the deficiencies of the aforementioned intelligent diagnosis methods. The effectiveness of the proposed method is validated using datasets from rolling element bearings and planetary gearboxes. These datasets contain massive measured signals involving different health conditions under various operating conditions. The diagnosis results show that the proposed method is able to not only adaptively mine available fault characteristics from the measured signals, but also obtain superior diagnosis accuracy compared with the existing methods.

  3. A belief network approach for development of a nuclear power plant diagnosis system

    Energy Technology Data Exchange (ETDEWEB)

    Hwang, I K; Kim, J T; Lee, D Y; Jung, C H; Kim, J Y; Lee, J S; Ham, C S [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    1999-12-31

    Belief network (or Bayesian network) based on Bayes` rule in probabilistic theory can be applied to the reasoning of diagnostic system. This paper describes the basic theory of concept and feasibility of using the network for diagnosis of nuclear power plants. An example shows that the probabilities of root causes of a failure are calculated from the measured or believed evidences. 6 refs., 3 figs. (Author)

  4. A belief network approach for development of a nuclear power plant diagnosis system

    Energy Technology Data Exchange (ETDEWEB)

    Hwang, I. K.; Kim, J. T.; Lee, D. Y.; Jung, C. H.; Kim, J. Y.; Lee, J. S.; Ham, C. S. [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    1998-12-31

    Belief network (or Bayesian network) based on Bayes` rule in probabilistic theory can be applied to the reasoning of diagnostic system. This paper describes the basic theory of concept and feasibility of using the network for diagnosis of nuclear power plants. An example shows that the probabilities of root causes of a failure are calculated from the measured or believed evidences. 6 refs., 3 figs. (Author)

  5. Four Challenges That Global Health Networks Face

    Directory of Open Access Journals (Sweden)

    Jeremy Shiffman

    2017-04-01

    Full Text Available Global health networks, webs of individuals and organizations with a shared concern for a particular condition, have proliferated over the past quarter century. They differ in their effectiveness, a factor that may help explain why resource allocations vary across health conditions and do not correspond closely with disease burden. Drawing on findings from recently concluded studies of eight global health networks—addressing alcohol harm, early childhood development (ECD, maternal mortality, neonatal mortality, pneumonia, surgically-treatable conditions, tobacco use, and tuberculosis—I identify four challenges that networks face in generating attention and resources for the conditions that concern them. The first is problem definition: generating consensus on what the problem is and how it should be addressed. The second is positioning: portraying the issue in ways that inspire external audiences to act. The third is coalition-building: forging alliances with these external actors, particularly ones outside the health sector. The fourth is governance: establishing institutions to facilitate collective action. Research indicates that global health networks that effectively tackle these challenges are more likely to garner support to address the conditions that concern them. In addition to the effectiveness of networks, I also consider their legitimacy, identifying reasons both to affirm and to question their right to exert power.

  6. A Learning Health Care System Using Computer-Aided Diagnosis.

    Science.gov (United States)

    Cahan, Amos; Cimino, James J

    2017-03-08

    Physicians intuitively apply pattern recognition when evaluating a patient. Rational diagnosis making requires that clinical patterns be put in the context of disease prior probability, yet physicians often exhibit flawed probabilistic reasoning. Difficulties in making a diagnosis are reflected in the high rates of deadly and costly diagnostic errors. Introduced 6 decades ago, computerized diagnosis support systems are still not widely used by internists. These systems cannot efficiently recognize patterns and are unable to consider the base rate of potential diagnoses. We review the limitations of current computer-aided diagnosis support systems. We then portray future diagnosis support systems and provide a conceptual framework for their development. We argue for capturing physician knowledge using a novel knowledge representation model of the clinical picture. This model (based on structured patient presentation patterns) holds not only symptoms and signs but also their temporal and semantic interrelations. We call for the collection of crowdsourced, automatically deidentified, structured patient patterns as means to support distributed knowledge accumulation and maintenance. In this approach, each structured patient pattern adds to a self-growing and -maintaining knowledge base, sharing the experience of physicians worldwide. Besides supporting diagnosis by relating the symptoms and signs with the final diagnosis recorded, the collective pattern map can also provide disease base-rate estimates and real-time surveillance for early detection of outbreaks. We explain how health care in resource-limited settings can benefit from using this approach and how it can be applied to provide feedback-rich medical education for both students and practitioners. ©Amos Cahan, James J Cimino. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 08.03.2017.

  7. Health promotion strategies: situational diagnosis in elementary schools

    Directory of Open Access Journals (Sweden)

    Cristina Berger Fadel

    2014-06-01

    Full Text Available Objective: To identify the existence of health-promoting actions in public and private schools. Methods: Exploratory and descriptive study with qualitative approach, conducted from June 2012 to June 2013, comprising 10 institutional managers of elementary schools of the public and private networks in the city of Ponta Grossa, PR. Data was collected through semistructured interviews, and examined with use of content analysis thus emerging thematic categories. Results: Regarding nutrition, monitoring is carried out by nutritionists in both types of school. Private schools provide theoretical guidance, while the public ones practice the orientations about personal care. With respect to the access to health services, public schools provide assistance to their students through the city’s Health Secretariat, whereas private schools are direct providers. The private network was also found to satisfy fully the human and social development. Concerning the structure, accessibility has been prioritized, both schools having implemented the necessary adaptations. As for security, although schools are equipped with monitoring cameras, violence and vandalism are more frequent in public schools. Conclusion: The institutions practice health-promoting actions, with significant differences between public and private schools, especially in the field of personal care, and social and human development. Approaching public and private networks is suggested, in order to perform an inter-institutional work, aiming to improve health promotion for the students. doi:10.5020/18061230.2014.p169

  8. Orthopedic Health: Joint Health and Care: Prevention, Symptoms, Diagnosis & Treatment

    Science.gov (United States)

    ... version of this page please turn Javascript on. Prevention Regular exercise, a balanced diet, and a healthful weight can help you reduce your risk of developing osteoarthritis, especially in the hips and knees, or suffering sports injuries. Exercise helps bone density, improves muscle strength and ...

  9. Improved algorithms for circuit fault diagnosis based on wavelet packet and neural network

    International Nuclear Information System (INIS)

    Zhang, W-Q; Xu, C

    2008-01-01

    In this paper, two improved BP neural network algorithms of fault diagnosis for analog circuit are presented through using optimal wavelet packet transform(OWPT) or incomplete wavelet packet transform(IWPT) as preprocessor. The purpose of preprocessing is to reduce the nodes in input layer and hidden layer of BP neural network, so that the neural network gains faster training and convergence speed. At first, we apply OWPT or IWPT to the response signal of circuit under test(CUT), and then calculate the normalization energy of each frequency band. The normalization energy is used to train the BP neural network to diagnose faulty components in the analog circuit. These two algorithms need small network size, while have faster learning and convergence speed. Finally, simulation results illustrate the two algorithms are effective for fault diagnosis

  10. Inference method using bayesian network for diagnosis of pulmonary nodules

    International Nuclear Information System (INIS)

    Kawagishi, Masami; Iizuka, Yoshio; Yamamoto, Hiroyuki; Yakami, Masahiro; Kubo, Takeshi; Fujimoto, Koji; Togashi, Kaori

    2010-01-01

    This report describes the improvements of a naive Bayes model that infers the diagnosis of pulmonary nodules in chest CT images based on the findings obtained when a radiologist interprets the CT images. We have previously introduced an inference model using a naive Bayes classifier and have reported its clinical value based on evaluation using clinical data. In the present report, we introduce the following improvements to the original inference model: the selection of findings based on correlations and the generation of a model using only these findings, and the introduction of classifiers that integrate several simple classifiers each of which is specialized for specific diagnosis. These improvements were found to increase the inference accuracy by 10.4% (p<.01) as compared to the original model in 100 cases (222 nodules) based on leave-one-out evaluation. (author)

  11. Fault diagnosis system of electromagnetic valve using neural network filter

    International Nuclear Information System (INIS)

    Hayashi, Shoji; Odaka, Tomohiro; Kuroiwa, Jousuke; Ogura, Hisakazu

    2008-01-01

    This paper is concerned with the gas leakage fault detection of electromagnetic valve using a neural network filter. In modern plants, the ability to detect and identify gas leakage faults is becoming increasingly important. The main difficulty in detecting gas leakage faults by sound signals lies in the fact that the practical plants are usually very noisy. To solve this difficulty, a neural network filter is used to eliminate background noise and raise the signal noise ratio of the sound signal. The background noise is assumed as a dynamic system, and an accurate mathematical model of the dynamic system can be established using a neural network filter. The predicted error between predicted values and practical ones constitutes the output of the filter. If the predicted error is zero, then there is no leakage. If the predicted error is greater than a certain value, then there is a leakage fault. Through application to practical pneumatic systems, it is verified that the neural network filter was effective in gas leakage detection. (author)

  12. A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network.

    Science.gov (United States)

    Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing

    2015-01-01

    This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information.

  13. Management continuity in local health networks

    Directory of Open Access Journals (Sweden)

    Mylaine Breton

    2012-04-01

    Full Text Available Introduction: Patients increasingly receive care from multiple providers in a variety of settings. They expect management continuity that crosses boundaries and bridges gaps in the healthcare system. To our knowledge, little research has been done to assess coordination across organizational and professional boundaries from the patients' perspective. Our objective was to assess whether greater local health network integration is associated with management continuity as perceived by patients. Method: We used the data from a research project on the development and validation of a generic and comprehensive continuity measurement instrument that can be applied to a variety of patient conditions and settings. We used the results of a cross-sectional survey conducted in 2009 with 256 patients in two local health networks in Quebec, Canada. We compared four aspects of management continuity between two contrasting network types (highly integrated vs. poorly integrated. Results: The scores obtained in the highly integrated network are better than those of the poorly integrated network on all dimensions of management continuity (coordinator role, role clarity and coordination between clinics, and information gaps between providers except for experience of care plan. Conclusion: Some aspects of care coordination among professionals and organizations are noticed by patients and may be valid indicators to assess care coordination.

  14. Communication security in open health care networks.

    Science.gov (United States)

    Blobel, B; Pharow, P; Engel, K; Spiegel, V; Krohn, R

    1999-01-01

    Fulfilling the shared care paradigm, health care networks providing open systems' interoperability in health care are needed. Such communicating and co-operating health information systems, dealing with sensitive personal medical information across organisational, regional, national or even international boundaries, require appropriate security solutions. Based on the generic security model, within the European MEDSEC project an open approach for secure EDI like HL7, EDIFACT, XDT or XML has been developed. The consideration includes both securing the message in an unsecure network and the transport of the unprotected information via secure channels (SSL, TLS etc.). Regarding EDI, an open and widely usable security solution has been specified and practically implemented for the examples of secure mailing and secure file transfer (FTP) via wrapping the sensitive information expressed by the corresponding protocols. The results are currently prepared for standardisation.

  15. A telecommunications journey rural health network.

    Science.gov (United States)

    Moore, Joe

    2012-01-01

    Utilizing a multi-gigabit statewide fiber healthcare network, Radiology Consultants of Iowa (RCI) set out to provide instantaneous service to their rural, critical access, hospital partners. RCIs idea was to assemble a collection of technologies and services that would even out workflow, reduce time on the road, and provide superior service. These technologies included PACS, voice recognition enabled dictation, HL7 interface technology, an imaging system for digitizing paper and prior films, and modern communication networks. The Iowa Rural Health Telecommunication Project was undertaken to form a system that all critical access hospitals would participate in, allowing RCI radiologists the efficiency of "any image, anywhere, anytime".

  16. Investigations on Incipient Fault Diagnosis of Power Transformer Using Neural Networks and Adaptive Neurofuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Nandkumar Wagh

    2014-01-01

    Full Text Available Continuity of power supply is of utmost importance to the consumers and is only possible by coordination and reliable operation of power system components. Power transformer is such a prime equipment of the transmission and distribution system and needs to be continuously monitored for its well-being. Since ratio methods cannot provide correct diagnosis due to the borderline problems and the probability of existence of multiple faults, artificial intelligence could be the best approach. Dissolved gas analysis (DGA interpretation may provide an insight into the developing incipient faults and is adopted as the preliminary diagnosis tool. In the proposed work, a comparison of the diagnosis ability of backpropagation (BP, radial basis function (RBF neural network, and adaptive neurofuzzy inference system (ANFIS has been investigated and the diagnosis results in terms of error measure, accuracy, network training time, and number of iterations are presented.

  17. The effect of narrow provider networks on health care use.

    Science.gov (United States)

    Atwood, Alicia; Lo Sasso, Anthony T

    2016-12-01

    Network design is an often overlooked aspect of health insurance contracts. Recent policy factors have resulted in narrower provider networks. We provide plausibly causal evidence on the effect of narrow network plans offered by a large national health insurance carrier in a major metropolitan market. Our econometric design exploits the fact that some firms offer a narrow network plan to their employees and some do not. Our results show that narrow network health plans lead to reductions in health care utilization and spending. We find evidence that narrow networks save money by selecting lower cost providers into the network. Copyright © 2016 Elsevier B.V. All rights reserved.

  18. A network-based biomarker approach for molecular investigation and diagnosis of lung cancer

    Directory of Open Access Journals (Sweden)

    Chen Bor-Sen

    2011-01-01

    Full Text Available Abstract Background Lung cancer is the leading cause of cancer deaths worldwide. Many studies have investigated the carcinogenic process and identified the biomarkers for signature classification. However, based on the research dedicated to this field, there is no highly sensitive network-based method for carcinogenesis characterization and diagnosis from the systems perspective. Methods In this study, a systems biology approach integrating microarray gene expression profiles and protein-protein interaction information was proposed to develop a network-based biomarker for molecular investigation into the network mechanism of lung carcinogenesis and diagnosis of lung cancer. The network-based biomarker consists of two protein association networks constructed for cancer samples and non-cancer samples. Results Based on the network-based biomarker, a total of 40 significant proteins in lung carcinogenesis were identified with carcinogenesis relevance values (CRVs. In addition, the network-based biomarker, acting as the screening test, proved to be effective in diagnosing smokers with signs of lung cancer. Conclusions A network-based biomarker using constructed protein association networks is a useful tool to highlight the pathways and mechanisms of the lung carcinogenic process and, more importantly, provides potential therapeutic targets to combat cancer.

  19. Online Social Networking and Mental Health

    Science.gov (United States)

    2014-01-01

    Abstract During the past decade, online social networking has caused profound changes in the way people communicate and interact. It is unclear, however, whether some of these changes may affect certain normal aspects of human behavior and cause psychiatric disorders. Several studies have indicated that the prolonged use of social networking sites (SNS), such as Facebook, may be related to signs and symptoms of depression. In addition, some authors have indicated that certain SNS activities might be associated with low self-esteem, especially in children and adolescents. Other studies have presented opposite results in terms of positive impact of social networking on self-esteem. The relationship between SNS use and mental problems to this day remains controversial, and research on this issue is faced with numerous challenges. This concise review focuses on the recent findings regarding the suggested connection between SNS and mental health issues such as depressive symptoms, changes in self-esteem, and Internet addiction. PMID:25192305

  20. Online social networking and mental health.

    Science.gov (United States)

    Pantic, Igor

    2014-10-01

    During the past decade, online social networking has caused profound changes in the way people communicate and interact. It is unclear, however, whether some of these changes may affect certain normal aspects of human behavior and cause psychiatric disorders. Several studies have indicated that the prolonged use of social networking sites (SNS), such as Facebook, may be related to signs and symptoms of depression. In addition, some authors have indicated that certain SNS activities might be associated with low self-esteem, especially in children and adolescents. Other studies have presented opposite results in terms of positive impact of social networking on self-esteem. The relationship between SNS use and mental problems to this day remains controversial, and research on this issue is faced with numerous challenges. This concise review focuses on the recent findings regarding the suggested connection between SNS and mental health issues such as depressive symptoms, changes in self-esteem, and Internet addiction.

  1. Application of CMAC Neural Network to Solar Energy Heliostat Field Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Neng-Sheng Pai

    2013-01-01

    Full Text Available Solar energy heliostat fields comprise numerous sun tracking platforms. As a result, fault detection is a highly challenging problem. Accordingly, the present study proposes a cerebellar model arithmetic computer (CMAC neutral network for automatically diagnosing faults within the heliostat field in accordance with the rotational speed, vibration, and temperature characteristics of the individual heliostat transmission systems. As compared with radial basis function (RBF neural network and back propagation (BP neural network in the heliostat field fault diagnosis, the experimental results show that the proposed neural network has a low training time, good robustness, and a reliable diagnostic performance. As a result, it provides an ideal solution for fault diagnosis in modern, large-scale heliostat fields.

  2. Program Spotlight: National Outreach Network's Community Health Educators

    Science.gov (United States)

    National Outreach Network of Community Health Educators located at Community Network Program Centers, Partnerships to Advance Cancer Health Equity, and NCI-designated cancer centers help patients and their families receive survivorship support.

  3. Neural Networks and Fault Probability Evaluation for Diagnosis Issues

    Science.gov (United States)

    Lefebvre, Dimitri; Guersi, Noureddine

    2014-01-01

    This paper presents a new FDI technique for fault detection and isolation in unknown nonlinear systems. The objective of the research is to construct and analyze residuals by means of artificial intelligence and probabilistic methods. Artificial neural networks are first used for modeling issues. Neural networks models are designed for learning the fault-free and the faulty behaviors of the considered systems. Once the residuals generated, an evaluation using probabilistic criteria is applied to them to determine what is the most likely fault among a set of candidate faults. The study also includes a comparison between the contributions of these tools and their limitations, particularly through the establishment of quantitative indicators to assess their performance. According to the computation of a confidence factor, the proposed method is suitable to evaluate the reliability of the FDI decision. The approach is applied to detect and isolate 19 fault candidates in the DAMADICS benchmark. The results obtained with the proposed scheme are compared with the results obtained according to a usual thresholding method. PMID:25132845

  4. Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network

    Energy Technology Data Exchange (ETDEWEB)

    Du, Zhimin; Jin, Xinqiao; Yang, Yunyu [School of Mechanical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai (China)

    2009-09-15

    Wavelet neural network, the integration of wavelet analysis and neural network, is presented to diagnose the faults of sensors including temperature, flow rate and pressure in variable air volume (VAV) systems to ensure well capacity of energy conservation. Wavelet analysis is used to process the original data collected from the building automation first. With three-level wavelet decomposition, the series of characteristic information representing various operation conditions of the system are obtained. In addition, neural network is developed to diagnose the source of the fault. To improve the diagnosis efficiency, three data groups based on several physical models or balances are classified and constructed. Using the data decomposed by three-level wavelet, the neural network can be well trained and series of convergent networks are obtained. Finally, the new measurements to diagnose are similarly processed by wavelet. And the well-trained convergent neural networks are used to identify the operation condition and isolate the source of the fault. (author)

  5. Lack of knowledge regarding the microscopic diagnosis of malaria by technicians of the laboratory network in Luanda, Angola.

    Science.gov (United States)

    Nazaré-Pembele, García; Rojas, Lázara; Núñez, Fidel Ángel

    2016-03-03

    Malaria is still one of the most important public health problems worldwide. The diagnosis of this disease is still mainly based on thick blood films.  To evaluate the knowledge about malaria diagnosis of the technicians of the public health network in Luanda, Angola, by means of a survey.  This survey was carried out in three phases. In the first one, open interviews were done to technicians related with the different procedures for malaria diagnosis. In the second one, a preliminary questionnaire was prepared and evaluated. In the third phase, a definitive questionnaire was applied to 120 technicians from Luanda between April and July, 2013. The proportions of correct and incorrect answers were compared for every question of the survey.  Significantly higher proportions of incorrect answers (p<0.05) were found in the questions related to clinical manifestations, 68/52 (p<0.05), species of Plasmodium according to geographical areas, 76/44 (p<0.05), the type of granulations according to species, 96/24 (p<0.01), the class of microscope magnifying glasses used to observe the thick smear, 105/15 (p<0.01), the thick smear report, 76/44 (p<0.01), the time and preparation of different stain solutions, 81/39 (p<0.01), and the number of parasites counted per 200 leukocytes, 96/24 (p<0.01).  Various failures for the microscopic diagnosis of malaria were observed amongst the evaluated technicians. These results will be useful as a baseline study before applying an educational intervention aimed to improve the quality of malaria diagnosis in Luanda's laboratory network.

  6. Structural health monitoring using wireless sensor networks

    Science.gov (United States)

    Sreevallabhan, K.; Nikhil Chand, B.; Ramasamy, Sudha

    2017-11-01

    Monitoring and analysing health of large structures like bridges, dams, buildings and heavy machinery is important for safety, economical, operational, making prior protective measures, and repair and maintenance point of view. In recent years there is growing demand for such larger structures which in turn make people focus more on safety. By using Microelectromechanical Systems (MEMS) Accelerometer we can perform Structural Health Monitoring by studying the dynamic response through measure of ambient vibrations and strong motion of such structures. By using Wireless Sensor Networks (WSN) we can embed these sensors in wireless networks which helps us to transmit data wirelessly thus we can measure the data wirelessly at any remote location. This in turn reduces heavy wiring which is a cost effective as well as time consuming process to lay those wires. In this paper we developed WSN based MEMS-accelerometer for Structural to test the results in the railway bridge near VIT University, Vellore campus.

  7. Application of artificial neural networks in fault diagnosis for 10MW high-temperature gas-cooled reactor

    International Nuclear Information System (INIS)

    Li Hui; Wang Ruipian; Hu Shouyin

    2003-01-01

    This paper makes researches on 10 MW High-Temperature Gas-Cooled Reactor fault diagnosis system using Artificial Neural Network, and uses the tendency value and real value of the data under the accidents to train and test two BP networks respectively. The final diagnostic result is the combination of the results of the two networks. The compound system can enhance the accuracy and adaptability of the diagnosis compared to the single network system

  8. Diagnosis of mechanical pumping system using neural networks and system parameters analysis

    International Nuclear Information System (INIS)

    Tsai, Tai Ming; Wang, Wei Hui

    2009-01-01

    Normally, a mechanical pumping system is equipped to monitor some of the important input and output signals which are set to the prescribed values. This paper addressed dealing with these signals to establish the database of input- output relation by using a number of neural network models through learning algorithms. These signals encompass normal and abnormal running conditions. The abnormal running conditions were artificially generated. Meanwhile, for the purpose of setting up an on-line diagnosis network, the learning speed and accuracy of three kinds of networks, viz., the backpropagation (BPN), radial basis function (RBF) and adaptive linear (ADALINE) neural networks have been compared and assessed. The assessment criteria of the networks are compared with the correlation result matrix in terms of the neuron vectors. Both BPN and RBF are judged by the maximum vector based on the post-regression analysis, and the ADALINE is judged by the minimum vector based on the least mean square error analysis. By ignoring the neural network training time, it has been shown that if the mechanical diagnosis system is tackled off-line, the RBF method is suggested. However, for on-line diagnosis, the BPN method is recommended

  9. Diagnosis of mechanical pumping system using neural networks and system parameters analysis

    Energy Technology Data Exchange (ETDEWEB)

    Tsai, Tai Ming; Wang, Wei Hui [National Taiwan Ocean University, Keelung (China)

    2009-01-15

    Normally, a mechanical pumping system is equipped to monitor some of the important input and output signals which are set to the prescribed values. This paper addressed dealing with these signals to establish the database of input- output relation by using a number of neural network models through learning algorithms. These signals encompass normal and abnormal running conditions. The abnormal running conditions were artificially generated. Meanwhile, for the purpose of setting up an on-line diagnosis network, the learning speed and accuracy of three kinds of networks, viz., the backpropagation (BPN), radial basis function (RBF) and adaptive linear (ADALINE) neural networks have been compared and assessed. The assessment criteria of the networks are compared with the correlation result matrix in terms of the neuron vectors. Both BPN and RBF are judged by the maximum vector based on the post-regression analysis, and the ADALINE is judged by the minimum vector based on the least mean square error analysis. By ignoring the neural network training time, it has been shown that if the mechanical diagnosis system is tackled off-line, the RBF method is suggested. However, for on-line diagnosis, the BPN method is recommended

  10. Personality Diagnosis for Personalized eHealth Services

    Science.gov (United States)

    Cortellese, Fabio; Nalin, Marco; Morandi, Angelica; Sanna, Alberto; Grasso, Floriana

    In this paper we present two different approaches to personality diagnosis, for the provision of innovative personalized services, as used in a case study where diabetic patients were supported in the improvement of physical activity in their daily life. The first approach presented relies on a static clustering of the population, with a specific motivation strategy designed for each cluster. The second approach relies on a dynamic population clustering, making use of recommendation systems and algorithms, like Collaborative Filtering. We discuss pro and cons of each approach and a possible combination of the two, as the most promising solution for this and other personalization services in eHealth.

  11. Mental Health and Social Networks After Disaster.

    Science.gov (United States)

    Bryant, Richard A; Gallagher, H Colin; Gibbs, Lisa; Pattison, Philippa; MacDougall, Colin; Harms, Louise; Block, Karen; Baker, Elyse; Sinnott, Vikki; Ireton, Greg; Richardson, John; Forbes, David; Lusher, Dean

    2017-03-01

    Although disasters are a major cause of mental health problems and typically affect large numbers of people and communities, little is known about how social structures affect mental health after a disaster. The authors assessed the extent to which mental health outcomes after disaster are associated with social network structures. In a community-based cohort study of survivors of a major bushfire disaster, participants (N=558) were assessed for probable posttraumatic stress disorder (PTSD) and probable depression. Social networks were assessed by asking participants to nominate people with whom they felt personally close. These nominations were used to construct a social network map that showed each participant's ties to other participants they nominated and also to other participants who nominated them. This map was then analyzed for prevailing patterns of mental health outcomes. Depression risk was higher for participants who reported fewer social connections, were connected to other depressed people, or were connected to people who had left their community. PTSD risk was higher if fewer people reported being connected with the participant, if those who felt close to the participant had higher levels of property loss, or if the participant was linked to others who were themselves not interconnected. Interestingly, being connected to other people who in turn were reciprocally close to each other was associated with a lower risk of PTSD. These findings provide the first evidence of disorder-specific patterns in relation to one's social connections after disaster. Depression appears to co-occur in linked individuals, whereas PTSD risk is increased with social fragmentation. These patterns underscore the need to adopt a sociocentric perspective of postdisaster mental health in order to better understand the potential for societal interventions in the wake of disaster.

  12. The application of neural networks for fault diagnosis in nuclear reactors

    International Nuclear Information System (INIS)

    Jalel, N.A.; Nicholson, H.

    1990-11-01

    In recent years considerable work have been done in the field of neural networks due to the recent development of effective learning algorithms, and the results of their applications have suggested that they can provide useful tools for solving practical problems. Artificial neural networks are mathematical models of theorized mind and brain activity. They are aimed to explore and reproduce human information processing tasks such as speech, vision, knowledge processing and control. The possibility of using artificial neural networks for fault and accident diagnosis in the Loss Of Fluid Test (LOFT) reactor, a small scale pressurised water reactor, is examined and explained in the paper. (author)

  13. [Local public health networks. Apropos of an experience].

    Science.gov (United States)

    Guix, Joan; Bocio, Ana; Ferràs, Joaquim; Margalef, Jordi; Osanz, Anna C; Serrano, Mónica; Sentenà, Anna

    2013-01-01

    Public health action on a territory is complex and requires the involvement of multiple actors, who do not always act coordinately. Networks of organizations structures including the whole of the local actors facilitate the generation of synergies and enable greater effectiveness and efficiency of the joint action from the different actors on a same landscape. We present 3 years experience of four Public Health Committees in a region of Catalonia (Spain), composed by the main actors in public health planning. Each of the committees is organized on a plenary and working groups on issues arising from the regional health diagnosis, and coincident with the Health Plan of the Region. Coordination in no case implies the loss or dilution of the firm of the actor generator of intervention initiative in public health, but their empowerment and collaboration by the other actors. In conclusion welcomes the creation of a culture of collaboration and synergies between the different organizations concerned. Lack of specificity is observed in establishing operational objectives, and the need for greater coordination and involvement of the components of the various working groups. Copyright © 2012 SESPAS. Published by Elsevier Espana. All rights reserved.

  14. Distributed Data Networks That Support Public Health Information Needs.

    Science.gov (United States)

    Tabano, David C; Cole, Elizabeth; Holve, Erin; Davidson, Arthur J

    Data networks, consisting of pooled electronic health data assets from health care providers serving different patient populations, promote data sharing, population and disease monitoring, and methods to assess interventions. Better understanding of data networks, and their capacity to support public health objectives, will help foster partnerships, expand resources, and grow learning health systems. We conducted semistructured interviews with 16 key informants across the United States, identified as network stakeholders based on their respective experience in advancing health information technology and network functionality. Key informants were asked about their experience with and infrastructure used to develop data networks, including each network's utility to identify and characterize populations, usage, and sustainability. Among 11 identified data networks representing hundreds of thousands of patients, key informants described aggregated health care clinical data contributing to population health measures. Key informant interview responses were thematically grouped to illustrate how networks support public health, including (1) infrastructure and information sharing; (2) population health measures; and (3) network sustainability. Collaboration between clinical data networks and public health entities presents an opportunity to leverage infrastructure investments to support public health. Data networks can provide resources to enhance population health information and infrastructure.

  15. Fault diagnosis and performance evaluation for high current LIA based on radial basis function neural network

    International Nuclear Information System (INIS)

    Yang Xinglin; Wang Huacen; Chen Nan; Dai Wenhua; Li Jin

    2006-01-01

    High current linear induction accelerator (LIA) is a complicated experimental physics device. It is difficult to evaluate and predict its performance. this paper presents a method which combines wavelet packet transform and radial basis function (RBF) neural network to build fault diagnosis and performance evaluation in order to improve reliability of high current LIA. The signal characteristics vectors which are extracted based on energy parameters of wavelet packet transform can well present the temporal and steady features of pulsed power signal, and reduce data dimensions effectively. The fault diagnosis system for accelerating cell and the trend classification system for the beam current based on RBF networks can perform fault diagnosis and evaluation, and provide predictive information for precise maintenance of high current LIA. (authors)

  16. Application of neural networks to multiple alarm processing and diagnosis in nuclear power plants

    International Nuclear Information System (INIS)

    Cheon, Se Woo; Chang Soon Heung; Chung, Hak Yeong

    1992-01-01

    This paper presents feasibility studies of multiple alarm processing and diagnosis using neural networks. The back-propagation neural network model is applied to the training of multiple alarm patterns for the identification of failure in a reactor coolant pump (RCP) system. The general mapping capability of the neural network enables to identify a fault easily. The case studies are performed with emphasis on the applicability of the neural network to pattern recognition problems. It is revealed that the neural network model can identify the cause of multiple alarms properly, even when untrained or sensor-failed alarm symptoms are given. It is also shown that multiple failures are easily identified using the symptoms of multiple alarms

  17. Diagnosis and Reconfiguration using Bayesian Networks: An Electrical Power System Case Study

    Science.gov (United States)

    Knox, W. Bradley; Mengshoel, Ole

    2009-01-01

    Automated diagnosis and reconfiguration are important computational techniques that aim to minimize human intervention in autonomous systems. In this paper, we develop novel techniques and models in the context of diagnosis and reconfiguration reasoning using causal Bayesian networks (BNs). We take as starting point a successful diagnostic approach, using a static BN developed for a real-world electrical power system. We discuss in this paper the extension of this diagnostic approach along two dimensions, namely: (i) from a static BN to a dynamic BN; and (ii) from a diagnostic task to a reconfiguration task. More specifically, we discuss the auto-generation of a dynamic Bayesian network from a static Bayesian network. In addition, we discuss subtle, but important, differences between Bayesian networks when used for diagnosis versus reconfiguration. We discuss a novel reconfiguration agent, which models a system causally, including effects of actions through time, using a dynamic Bayesian network. Though the techniques we discuss are general, we demonstrate them in the context of electrical power systems (EPSs) for aircraft and spacecraft. EPSs are vital subsystems on-board aircraft and spacecraft, and many incidents and accidents of these vehicles have been attributed to EPS failures. We discuss a case study that provides initial but promising results for our approach in the setting of electrical power systems.

  18. Impact of small-world network topology on the conventional artificial neural network for the diagnosis of diabetes

    International Nuclear Information System (INIS)

    Erkaymaz, Okan; Ozer, Mahmut

    2016-01-01

    Artificial intelligent systems have been widely used for diagnosis of diseases. Due to their importance, new approaches are attempted consistently to increase the performance of these systems. In this study, we introduce a new approach for diagnosis of diabetes based on the Small-World Feed Forward Artificial Neural Network (SW- FFANN). We construct the small-world network by following the Watts–Strogatz approach, and use this architecture for classifying the diabetes, and compare its performance with that of the regular or the conventional FFANN. We show that the classification performance of the SW-FFANN is better than that of the conventional FFANN. The SW-FFANN approach also results in both the highest output correlation and the best output error parameters. We also perform the accuracy analysis and show that SW-FFANN approach exhibits the highest classifier performance.

  19. Computer-aided diagnosis workstation and telemedicine network system for chest diagnosis based on multislice CT images

    Science.gov (United States)

    Satoh, Hitoshi; Niki, Noboru; Eguchi, Kenji; Ohmatsu, Hironobu; Kakinuma, Ryutaru; Moriyama, Noriyuki

    2009-02-01

    Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. Moreover, the doctor who diagnoses a medical image is insufficient in Japan. To overcome these problems, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images, a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification and a vertebra body analysis algorithm for quantitative evaluation of osteoporosis likelihood by using helical CT scanner for the lung cancer mass screening. The functions to observe suspicious shadow in detail are provided in computer-aided diagnosis workstation with these screening algorithms. We also have developed the telemedicine network by using Web medical image conference system with the security improvement of images transmission, Biometric fingerprint authentication system and Biometric face authentication system. Biometric face authentication used on site of telemedicine makes "Encryption of file" and "Success in login" effective. As a result, patients' private information is protected. We can share the screen of Web medical image conference system from two or more web conference terminals at the same time. An opinion can be exchanged mutually by using a camera and a microphone that are connected with workstation. Based on these diagnostic assistance methods, we have developed a new computer-aided workstation and a new telemedicine network that can display suspected lesions three-dimensionally in a short time. The results of this study indicate that our radiological information system without film by using computer-aided diagnosis workstation and our telemedicine network system can increase diagnostic speed, diagnostic accuracy and

  20. Fault diagnosis in nuclear power plants using an artificial neural network technique

    International Nuclear Information System (INIS)

    Chou, H.P.; Prock, J.; Bonfert, J.P.

    1993-01-01

    Application of artificial intelligence (AI) computational techniques, such as expert systems, fuzzy logic, and neural networks in diverse areas has taken place extensively. In the nuclear industry, the intended goal for these AI techniques is to improve power plant operational safety and reliability. As a computerized operator support tool, the artificial neural network (ANN) approach is an emerging technology that currently attracts a large amount of interest. The ability of ANNs to extract the input/output relation of a complicated process and the superior execution speed of a trained ANN motivated this study. The goal was to develop neural networks for sensor and process faults diagnosis with the potential of implementing as a component of a real-time operator support system LYDIA, early sensor and process fault detection and diagnosis

  1. Improved Diagnosis and Care for Rare Diseases through Implementation of Precision Public Health Framework.

    Science.gov (United States)

    Baynam, Gareth; Bowman, Faye; Lister, Karla; Walker, Caroline E; Pachter, Nicholas; Goldblatt, Jack; Boycott, Kym M; Gahl, William A; Kosaki, Kenjiro; Adachi, Takeya; Ishii, Ken; Mahede, Trinity; McKenzie, Fiona; Townshend, Sharron; Slee, Jennie; Kiraly-Borri, Cathy; Vasudevan, Anand; Hawkins, Anne; Broley, Stephanie; Schofield, Lyn; Verhoef, Hedwig; Groza, Tudor; Zankl, Andreas; Robinson, Peter N; Haendel, Melissa; Brudno, Michael; Mattick, John S; Dinger, Marcel E; Roscioli, Tony; Cowley, Mark J; Olry, Annie; Hanauer, Marc; Alkuraya, Fowzan S; Taruscio, Domenica; Posada de la Paz, Manuel; Lochmüller, Hanns; Bushby, Kate; Thompson, Rachel; Hedley, Victoria; Lasko, Paul; Mina, Kym; Beilby, John; Tifft, Cynthia; Davis, Mark; Laing, Nigel G; Julkowska, Daria; Le Cam, Yann; Terry, Sharon F; Kaufmann, Petra; Eerola, Iiro; Norstedt, Irene; Rath, Ana; Suematsu, Makoto; Groft, Stephen C; Austin, Christopher P; Draghia-Akli, Ruxandra; Weeramanthri, Tarun S; Molster, Caron; Dawkins, Hugh J S

    2017-01-01

    Public health relies on technologies to produce and analyse data, as well as effectively develop and implement policies and practices. An example is the public health practice of epidemiology, which relies on computational technology to monitor the health status of populations, identify disadvantaged or at risk population groups and thereby inform health policy and priority setting. Critical to achieving health improvements for the underserved population of people living with rare diseases is early diagnosis and best care. In the rare diseases field, the vast majority of diseases are caused by destructive but previously difficult to identify protein-coding gene mutations. The reduction in cost of genetic testing and advances in the clinical use of genome sequencing, data science and imaging are converging to provide more precise understandings of the 'person-time-place' triad. That is: who is affected (people); when the disease is occurring (time); and where the disease is occurring (place). Consequently we are witnessing a paradigm shift in public health policy and practice towards 'precision public health'.Patient and stakeholder engagement has informed the need for a national public health policy framework for rare diseases. The engagement approach in different countries has produced highly comparable outcomes and objectives. Knowledge and experience sharing across the international rare diseases networks and partnerships has informed the development of the Western Australian Rare Diseases Strategic Framework 2015-2018 (RD Framework) and Australian government health briefings on the need for a National plan.The RD Framework is guiding the translation of genomic and other technologies into the Western Australian health system, leading to greater precision in diagnostic pathways and care, and is an example of how a precision public health framework can improve health outcomes for the rare diseases population.Five vignettes are used to illustrate how policy

  2. Applying a Cerebellar Model Articulation Controller Neural Network to a Photovoltaic Power Generation System Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Kuei-Hsiang Chao

    2013-01-01

    Full Text Available This study employed a cerebellar model articulation controller (CMAC neural network to conduct fault diagnoses on photovoltaic power generation systems. We composed a module array using 9 series and 2 parallel connections of SHARP NT-R5E3E 175 W photovoltaic modules. In addition, we used data that were outputted under various fault conditions as the training samples for the CMAC and used this model to conduct the module array fault diagnosis after completing the training. The results of the training process and simulations indicate that the method proposed in this study requires fewer number of training times compared to other methods. In addition to significantly increasing the accuracy rate of the fault diagnosis, this model features a short training duration because the training process only tunes the weights of the exited memory addresses. Therefore, the fault diagnosis is rapid, and the detection tolerance of the diagnosis system is enhanced.

  3. Social networks of professionals in health care organizations: a review.

    Science.gov (United States)

    Tasselli, Stefano

    2014-12-01

    In this article, we provide an overview of social network research in health care, with a focus on social interactions between professionals in organizations. We begin by introducing key concepts defining the social network approach, including network density, centrality, and brokerage. We then review past and current research on the antecedents of health care professionals' social networks-including demographic attributes, professional groups, and organizational arrangements-and their consequences-including satisfaction at work, leadership, behaviors, knowledge transfer, diffusion of innovation, and performance. Finally, we examine future directions for social network research in health care, focusing on micro-macro linkages and network dynamics. © The Author(s) 2014.

  4. Multi-threshold white matter structural networks fusion for accurate diagnosis of Tourette syndrome children

    Science.gov (United States)

    Wen, Hongwei; Liu, Yue; Wang, Shengpei; Li, Zuoyong; Zhang, Jishui; Peng, Yun; He, Huiguang

    2017-03-01

    Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder. To date, TS is still misdiagnosed due to its varied presentation and lacking of obvious clinical symptoms. Therefore, studies of objective imaging biomarkers are of great importance for early TS diagnosis. As tic generation has been linked to disturbed structural networks, and many efforts have been made recently to investigate brain functional or structural networks using machine learning methods, for the purpose of disease diagnosis. However, few studies were related to TS and some drawbacks still existed in them. Therefore, we propose a novel classification framework integrating a multi-threshold strategy and a network fusion scheme to address the preexisting drawbacks. Here we used diffusion MRI probabilistic tractography to construct the structural networks of 44 TS children and 48 healthy children. We ameliorated the similarity network fusion algorithm specially to fuse the multi-threshold structural networks. Graph theoretical analysis was then implemented, and nodal degree, nodal efficiency and nodal betweenness centrality were selected as features. Finally, support vector machine recursive feature extraction (SVM-RFE) algorithm was used for feature selection, and then optimal features are fed into SVM to automatically discriminate TS children from controls. We achieved a high accuracy of 89.13% evaluated by a nested cross validation, demonstrated the superior performance of our framework over other comparison methods. The involved discriminative regions for classification primarily located in the basal ganglia and frontal cortico-cortical networks, all highly related to the pathology of TS. Together, our study may provide potential neuroimaging biomarkers for early-stage TS diagnosis.

  5. Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence.

    Science.gov (United States)

    Zhang, Ran; Peng, Zhen; Wu, Lifeng; Yao, Beibei; Guan, Yong

    2017-03-09

    Intelligent condition monitoring and fault diagnosis by analyzing the sensor data can assure the safety of machinery. Conventional fault diagnosis and classification methods usually implement pretreatments to decrease noise and extract some time domain or frequency domain features from raw time series sensor data. Then, some classifiers are utilized to make diagnosis. However, these conventional fault diagnosis approaches suffer from the expertise of feature selection and they do not consider the temporal coherence of time series data. This paper proposes a fault diagnosis model based on Deep Neural Networks (DNN). The model can directly recognize raw time series sensor data without feature selection and signal processing. It also takes advantage of the temporal coherence of the data. Firstly, raw time series training data collected by sensors are used to train the DNN until the cost function of DNN gets the minimal value; Secondly, test data are used to test the classification accuracy of the DNN on local time series data. Finally, fault diagnosis considering temporal coherence with former time series data is implemented. Experimental results show that the classification accuracy of bearing faults can get 100%. The proposed fault diagnosis approach is effective in recognizing the type of bearing faults.

  6. Fault Diagnosis Method of Polymerization Kettle Equipment Based on Rough Sets and BP Neural Network

    Directory of Open Access Journals (Sweden)

    Shu-zhi Gao

    2013-01-01

    Full Text Available Polyvinyl chloride (PVC polymerizing production process is a typical complex controlled object, with complexity features, such as nonlinear, multivariable, strong coupling, and large time-delay. Aiming at the real-time fault diagnosis and optimized monitoring requirements of the large-scale key polymerization equipment of PVC production process, a real-time fault diagnosis strategy is proposed based on rough sets theory with the improved discernibility matrix and BP neural networks. The improved discernibility matrix is adopted to reduct the attributes of rough sets in order to decrease the input dimensionality of fault characteristics effectively. Levenberg-Marquardt BP neural network is trained to diagnose the polymerize faults according to the reducted decision table, which realizes the nonlinear mapping from fault symptom set to polymerize fault set. Simulation experiments are carried out combining with the industry history datum to show the effectiveness of the proposed rough set neural networks fault diagnosis method. The proposed strategy greatly increased the accuracy rate and efficiency of the polymerization fault diagnosis system.

  7. Data Quality in Online Health Social Networks for Chronic Diseases

    Science.gov (United States)

    Venkatesan, Srikanth

    2017-01-01

    Can medical advice from other participants in online health social networks impact patient safety? What can we do alleviate this problem? How does the accuracy of information on such networks affect the patients?. There has been a significant increase , in recent years, in the use of online health social network sites as more patients seek to…

  8. Challenges for Game Addiction as a Mental Health Diagnosis

    DEFF Research Database (Denmark)

    Nielsen, Rune K.L.; Aarseth, Espen; Poulsen, Arne

    2014-01-01

    In this paper, we outline the proposed PhD project: "Challenges for Game Addiction as a Mental Health Diagnosis". The project aims to bridge gaps between the perspectives, theories and data of current research trajectories that engage with the concept of game addiction; from psychology, psychiatry......, cognitive neuroscience to media and game studies. The project has several proposed outcomes. Based on a review of the literature, the adequacy of 'game addiction' as a concept is questioned. The concept is further discussed in a historical perspective of game related pathologies and media/moral panics....... The validity of the prevalent instruments used to assess the prevalence of computer game addiction is examined in a cross-disciplinary context. The argument of the project is that research on computer game addiction is limited by mono-disciplinary approaches that fail to capture significant nuances at the cost...

  9. Remote consultation and diagnosis in medical imaging using a global PACS backbone network

    Science.gov (United States)

    Martinez, Ralph; Sutaria, Bijal N.; Kim, Jinman; Nam, Jiseung

    1993-10-01

    A Global PACS is a national network which interconnects several PACS networks at medical and hospital complexes using a national backbone network. A Global PACS environment enables new and beneficial operations between radiologists and physicians, when they are located in different geographical locations. One operation allows the radiologist to view the same image folder at both Local and Remote sites so that a diagnosis can be performed. The paper describes the user interface, database management, and network communication software which has been developed in the Computer Engineering Research Laboratory and Radiology Research Laboratory. Specifically, a design for a file management system in a distributed environment is presented. In the remote consultation and diagnosis operation, a set of images is requested from the database archive system and sent to the Local and Remote workstation sites on the Global PACS network. Viewing the same images, the radiologists use pointing overlay commands, or frames to point out features on the images. Each workstation transfers these frames, to the other workstation, so that an interactive session for diagnosis takes place. In this phase, we use fixed frames and variable size frames, used to outline an object. The data pockets for these frames traverses the national backbone in real-time. We accomplish this feature by using TCP/IP protocol sockets for communications. The remote consultation and diagnosis operation has been tested in real-time between the University Medical Center and the Bowman Gray School of Medicine at Wake Forest University, over the Internet. In this paper, we show the feasibility of the operation in a Global PACS environment. Future improvements to the system will include real-time voice and interactive compressed video scenarios.

  10. Application of artificial neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer.

    Science.gov (United States)

    Duan, Xiaoran; Yang, Yongli; Tan, Shanjuan; Wang, Sihua; Feng, Xiaolei; Cui, Liuxin; Feng, Feifei; Yu, Songcheng; Wang, Wei; Wu, Yongjun

    2017-08-01

    The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length. Real-time quantitative methylation-specific PCR was used to detect the levels of three-gene promoter methylation, and real-time PCR method was applied to determine the relative telomere length. BP neural network and Fisher discrimination analysis were used to establish the discrimination diagnosis model. The levels of three-gene promoter methylation in patients with lung cancer were significantly higher than those of the normal controls. The values of Z(P) in two groups were 2.641 (0.008), 2.075 (0.038) and 3.044 (0.002), respectively. The relative telomere lengths of patients with lung cancer (0.93 ± 0.32) were significantly lower than those of the normal controls (1.16 ± 0.57), t = 4.072, P < 0.001. The areas under the ROC curve (AUC) and 95 % CI of prediction set from Fisher discrimination analysis and BP neural network were 0.670 (0.569-0.761) and 0.760 (0.664-0.840). The AUC of BP neural network was higher than that of Fisher discrimination analysis, and Z(P) was 0.76. Four biomarkers are associated with lung cancer. BP neural network model for the prediction of lung cancer is better than Fisher discrimination analysis, and it can provide an excellent and intelligent diagnosis tool for lung cancer.

  11. Social networks, social support mechanisms, and quality of life after breast cancer diagnosis.

    Science.gov (United States)

    Kroenke, Candyce H; Kwan, Marilyn L; Neugut, Alfred I; Ergas, Isaac J; Wright, Jaime D; Caan, Bette J; Hershman, Dawn; Kushi, Lawrence H

    2013-06-01

    We examined mechanisms through which social relationships influence quality of life (QOL) in breast cancer survivors. This study included 3,139 women from the Pathways Study who were diagnosed with breast cancer from 2006 to 2011 and provided data on social networks (the presence of a spouse or intimate partner, religious/social ties, volunteering, and numbers of close friends and relatives), social support (tangible support, emotional/informational support, affection, positive social interaction), and QOL, measured by the FACT-B, approximately 2 months post diagnosis. We used logistic models to evaluate associations between social network size, social support, and lower versus higher than median QOL scores. We further stratified by stage at diagnosis and treatment. In multivariate-adjusted analyses, women who were characterized as socially isolated had significantly lower FACT-B (OR = 2.18, 95 % CI: 1.72-2.77), physical well-being (WB) (OR = 1.61, 95 % CI: 1.27-2.03), functional WB (OR = 2.08, 95 % CI: 1.65-2.63), social WB (OR = 3.46, 95 % CI: 2.73-4.39), and emotional WB (OR = 1.67, 95 % CI: 1.33-2.11) scores and higher breast cancer symptoms (OR = 1.48, 95 % CI: 1.18-1.87) compared with socially integrated women. Each social network member independently predicted higher QOL. Simultaneous adjustment for social networks and social support partially attenuated associations between social networks and QOL. The strongest mediator and type of social support that was most predictive of QOL outcomes was "positive social interaction." However, each type of support was important depending on outcome, stage, and treatment status. Larger social networks and greater social support were related to higher QOL after a diagnosis of breast cancer. Effective social support interventions need to evolve beyond social-emotional interventions and need to account for disease severity and treatment status.

  12. Social networks, social support mechanisms, and quality of life after breast cancer diagnosis

    Science.gov (United States)

    Kroenke, Candyce H; Kwan, Marilyn L.; Neugut, Alfred I.; Ergas, Isaac J.; Wright, Jaime D.; Caan, Bette J.; Hershman, Dawn; Kushi, Lawrence H.

    2013-01-01

    Purpose We examined mechanisms through which social relationships influence quality of life (QOL) in breast cancer survivors. Methods This study included 3,139 women from the Pathways Study who were diagnosed with breast cancer from 2006-2011 and provided data on social networks (presence of spouse or intimate partner, religious/social ties, volunteering, and numbers of close friends and relatives), social support (tangible, emotional/informational, affection, positive social interaction), and quality of life (QOL), measured by the FACT-B, approximately two months post-diagnosis. We used logistic models to evaluate associations between social network size, social support, and lower vs. higher than median QOL scores. We further stratified by stage at diagnosis and treatment. Results In multivariate-adjusted analyses, women who were characterized as socially isolated had significantly lower FACT-B (OR=2.18, 95%CI:1.72-2.77), physical well-being (WB) (OR=1.61, 95%CI:1.27-2.03), functional WB (OR=2.08, 95%CI:1.65-2.63), social WB (OR=3.46, 95%CI:2.73-4.39), and emotional WB (OR=1.67, 95%CI:1.33-2.11) scores and higher breast cancer symptoms (OR=1.48, 95%CI:1.18-1.87), compared with socially integrated women. Each social network member independently predicted higher QOL. Simultaneous adjustment for social networks and social support partially attenuated associations between social networks and QOL. The strongest mediator and type of social support that was most predictive of QOL outcomes was “positive social interaction”. However, each type of support was important depending on outcome, stage, and treatment status. Conclusions Larger social networks and greater social support were related to higher QOL after a diagnosis of breast cancer. Effective social support interventions need to evolve beyond social-emotional interventions and need to account for disease severity and treatment status. PMID:23657404

  13. Adaptive neural network/expert system that learns fault diagnosis for different structures

    Science.gov (United States)

    Simon, Solomon H.

    1992-08-01

    Corporations need better real-time monitoring and control systems to improve productivity by watching quality and increasing production flexibility. The innovative technology to achieve this goal is evolving in the form artificial intelligence and neural networks applied to sensor processing, fusion, and interpretation. By using these advanced Al techniques, we can leverage existing systems and add value to conventional techniques. Neural networks and knowledge-based expert systems can be combined into intelligent sensor systems which provide real-time monitoring, control, evaluation, and fault diagnosis for production systems. Neural network-based intelligent sensor systems are more reliable because they can provide continuous, non-destructive monitoring and inspection. Use of neural networks can result in sensor fusion and the ability to model highly, non-linear systems. Improved models can provide a foundation for more accurate performance parameters and predictions. We discuss a research software/hardware prototype which integrates neural networks, expert systems, and sensor technologies and which can adapt across a variety of structures to perform fault diagnosis. The flexibility and adaptability of the prototype in learning two structures is presented. Potential applications are discussed.

  14. Community Health Global Network and Sustainable Development

    Directory of Open Access Journals (Sweden)

    Rebekah Young

    2016-01-01

    Full Text Available With the achievements, failures and passing of the Millennium Development Goals (MDG, the world has turned its eyes to the Sustainable Development Goals (SDG, designed to foster sustainable social, economic and environmental development over the next 15 years.(1 Community-led initiatives are increasingly being recognised as playing a key role in realising sustainable community development and in the aspirations of universal healthcare.(2 In many parts of the world, faith-based organisations are some of the main players in community-led development and health care.(3 Community Health Global Network (CHGN creates links between organisations, with the purpose being to encourage communities to recognise their assets and abilities, identify shared concerns and discover solutions together, in order to define and lead their futures in sustainable ways.(4 CHGN has facilitated the development of collaborative groups of health and development initiatives called ‘Clusters’ in several countries including India, Bangladesh, Kenya, Tanzania, Zambia and Myanmar. In March 2016 these Clusters met together in an International Forum, to share learnings, experiences, challenges, achievements and to encourage one another. Discussions held throughout the forum suggest that the CHGN model is helping to promote effective, sustainable development and health care provision on both a local and a global scale.

  15. [Analysis of qualifications of medical and health institutions and certified doctors for providing occupational disease diagnosis in China].

    Science.gov (United States)

    Wang, Huan-qiang; Li, Tao; Qi, Fang; Wu, Rui; Nie, Wu; Yu, Chen

    2013-10-01

    To investigate the qualifications and current situations of the medical and health institutions and certified doctors for providing occupational disease diagnosis in China and to provide a reference for developing relevant policies. Work reports and questionnaires survey were used to investigate the qualifications of all medical and health institutions and certified doctors for providing occupational disease diagnosis in China and their acceptance and diagnosis of occupational disease cases from 2006 to 2010. The rate for the work reports was 100%, and the response rate for the questionnaires was 71.0%. By the end of 2010, in the 31 provincial-level regions (excluding Hong Kong, Macao, and Taiwan) in China, there had been 503 medical and health institutions which were qualified for providing occupational disease diagnosis, including 207 centers for disease control and prevention, accounting for 41.2%, 145 general hospitals, accounting for 28.8%, 69 enterprise-owned hospitals, accounting for 13.7%, and 64 institutes or centers for occupational disease prevention and control, accounting for 12.7%; 4986 certified doctors got the qualification for providing occupational disease diagnosis, with 9.4 certified doctors on average in each institution, and there was 0.65 certified doctor per 100 000 employees. In addition, 16.5% of the institutions got all the qualifications for diagnosing 9 occupational diseases, and 17.1% of the institutions got the qualification for diagnosing one occupational disease. Each certified doctor accepted diagnosis of 16.8 cases of occupational diseases on average every year. A national occupational disease diagnosis network has been established in China, but the imbalance in regional distribution and specialty programs still exists among the qualified medical and health institutions and certified doctors. It is essential to further strengthen the development of regional qualified medical and health institutions and training of qualified

  16. Fault detection Based Bayesian network and MOEA/D applied to Sensorless Drive Diagnosis

    Directory of Open Access Journals (Sweden)

    Zhou Qing

    2017-01-01

    Full Text Available Sensorless Drive Diagnosis can be used to assess the process data without the need for additional cost-intensive sensor technology, and you can understand the synchronous motor and connecting parts of the damaged state. Considering the number of features involved in the process data, it is necessary to perform feature selection and reduce the data dimension in the process of fault detection. In this paper, the MOEA / D algorithm based on multi-objective optimization is used to obtain the weight vector of all the features in the original data set. It is more suitable to classify or make decisions based on these features. In order to ensure the fastness and convenience sensorless drive diagnosis, in this paper, the classic Bayesian network learning algorithm-K2 algorithm is used to study the network structure of each feature in sensorless drive, which makes the fault detection and elimination process more targeted.

  17. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.

    Science.gov (United States)

    Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adeli, Hojjat

    2017-09-27

    An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provides variable results secondary to reader expertise level, and is limited in identifying abnormalities. Therefore, it is essential to develop a computer-aided diagnosis (CAD) system to automatically distinguish the class of these EEG signals using machine learning techniques. This is the first study to employ the convolutional neural network (CNN) for analysis of EEG signals. In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes. The proposed technique achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Signs for early diagnosis of heart failure in primary health care

    Directory of Open Access Journals (Sweden)

    Devroey D

    2011-09-01

    Full Text Available Dirk Devroey1,2, Viviane Van Casteren11Scientific Institute of Public Health, Unit of Epidemiology, Brussels, Belgium; 2Vrije Universiteit Brussel (VUB, Department of Family Medicine, Brussels, BelgiumObjective: The current guidelines for the diagnosis of heart failure (HF are based on studies of hospital-based patients. The aim of this study is to describe the symptoms, clinical signs, and diagnostic procedures confirming the diagnosis of HF in primary health care.Materials/subjects and methods: Data were prospectively collected during a 2-year period by a nationwide network of sentinel practices. All adult patients without known HF, for which the diagnosis of HF was clinically suspected for the first time, were registered. When diagnosed, HF was confirmed after 1 month.Results: 754 patients with a suspicion of HF were recorded. The diagnosis of HF was confirmed for 74% of the patients. The average age of the patients with confirmed HF was 77.7 years, and for those without HF 75.6 years (P = 0.018. From a logistic regression, breathlessness on exercise (P < 0.001, limitations of physical activity (P = 0.003, and orthopnea (P = 0.040 were the symptoms most associated with HF. The clinical signs most associated with HF, were pulmonary rales (P < 0.001, peripheral edema (P < 0.001, and raised jugular venous pressure (P = 0.039. An electrocardiogram was performed in 75% of the cases, blood analyses in 68%, echocardiogram in 63%, chest X-ray in 61%, and determination of natriuretic peptides in 11% of the cases.Conclusion: Many clinical signs may occur in patients with HF. However, the occurrence of peripheral edema, breathlessness on exercise, or pulmonary rales, are highly suggestive for HF when diagnosed in primary health care, as is the case in hospital-admitted patients. The diagnosis of HF was often left unconfirmed by an echocardiogram and/or an electrocardiogram.Keywords: heart failure, primary health care, diagnostic clinical signs

  19. Artificial intelligence in diagnosis and supply restoration for a distribution network

    Energy Technology Data Exchange (ETDEWEB)

    Teo, C.Y.; Gooi, H.B. [Nanyang Technological University (Singapore). School of Electrical and Electronic Engineering

    1998-07-01

    The development of a PC-based integrated system, to illustrate the application of artificial intelligence in the fault diagnosis and supply restoration for an interconnected distribution network is described. The intelligent process utilises the post-fault network status, a list of the tripped breakers, main protection alarm, and the conventional event log. The fault diagnostic system is implemented by three independent mechanisms, namely the generic core rule, specific post-fault network matching, and generic relay inference rules. The intelligent restoration process is implemented by the switching check, the dynamic restoration algorithm and the mechanism for restoration by record matching and learning. By linking to a PC-based distribution simulator it has been demonstrated that the developed mechanisms enable the correct deduction of fault under different network configurations. The appropriate restoration plan can also be generated to restore supply to the entire restorable load for various post-fault networks. This system is currently used for undergraduate teaching and will be ideal for the training of network operation engineers. As the system developed is generic and can be used for a general network, it can be further developed for practical operation in a subtransmission system or an urban distribution system operated in any configuration. (author)

  20. Tracks: A National Environmental Public Health Tracking Network Overview

    Centers for Disease Control (CDC) Podcasts

    In this podcast, Dr. Mike McGeehin, Director of CDC's Division of Environmental Hazards and Health Effects, provides an overview of the National Environmental Public Health Tracking Network. It highlights the Tracking Network's goal, how it will improve public health, its audience, and much more.

  1. Bayesian network modelling on data from fine needle aspiration cytology examination for breast cancer diagnosis

    OpenAIRE

    Ding, Xuemei; Cao, Yi; Zhai, Jia; Maguire, Liam; Li, Yuhua; Yang, Hongqin; Wang, Yuhua; Zeng, Jinshu; Liu, Shuo

    2017-01-01

    The paper employed Bayesian network (BN) modelling approach to discover causal dependencies among different data features of Breast Cancer Wisconsin Dataset (BCWD) derived from openly sourced UCI repository. K2 learning algorithm and k-fold cross validation were used to construct and optimize BN structure. Compared to Na‹ve Bayes (NB), the obtained BN presented better performance for breast cancer diagnosis based on fine needle aspiration cytology (FNAC) examination. It also showed that, amon...

  2. Developing Large-Scale Bayesian Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft

    Science.gov (United States)

    Mengshoel, Ole Jakob; Poll, Scott; Kurtoglu, Tolga

    2009-01-01

    In this paper, we investigate the use of Bayesian networks to construct large-scale diagnostic systems. In particular, we consider the development of large-scale Bayesian networks by composition. This compositional approach reflects how (often redundant) subsystems are architected to form systems such as electrical power systems. We develop high-level specifications, Bayesian networks, clique trees, and arithmetic circuits representing 24 different electrical power systems. The largest among these 24 Bayesian networks contains over 1,000 random variables. Another BN represents the real-world electrical power system ADAPT, which is representative of electrical power systems deployed in aerospace vehicles. In addition to demonstrating the scalability of the compositional approach, we briefly report on experimental results from the diagnostic competition DXC, where the ProADAPT team, using techniques discussed here, obtained the highest scores in both Tier 1 (among 9 international competitors) and Tier 2 (among 6 international competitors) of the industrial track. While we consider diagnosis of power systems specifically, we believe this work is relevant to other system health management problems, in particular in dependable systems such as aircraft and spacecraft. (See CASI ID 20100021910 for supplemental data disk.)

  3. Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness.

    Science.gov (United States)

    Chennu, Srivas; Annen, Jitka; Wannez, Sarah; Thibaut, Aurore; Chatelle, Camille; Cassol, Helena; Martens, Géraldine; Schnakers, Caroline; Gosseries, Olivia; Menon, David; Laureys, Steven

    2017-08-01

    Recent advances in functional neuroimaging have demonstrated novel potential for informing diagnosis and prognosis in the unresponsive wakeful syndrome and minimally conscious states. However, these technologies come with considerable expense and difficulty, limiting the possibility of wider clinical application in patients. Here, we show that high density electroencephalography, collected from 104 patients measured at rest, can provide valuable information about brain connectivity that correlates with behaviour and functional neuroimaging. Using graph theory, we visualize and quantify spectral connectivity estimated from electroencephalography as a dense brain network. Our findings demonstrate that key quantitative metrics of these networks correlate with the continuum of behavioural recovery in patients, ranging from those diagnosed as unresponsive, through those who have emerged from minimally conscious, to the fully conscious locked-in syndrome. In particular, a network metric indexing the presence of densely interconnected central hubs of connectivity discriminated behavioural consciousness with accuracy comparable to that achieved by expert assessment with positron emission tomography. We also show that this metric correlates strongly with brain metabolism. Further, with classification analysis, we predict the behavioural diagnosis, brain metabolism and 1-year clinical outcome of individual patients. Finally, we demonstrate that assessments of brain networks show robust connectivity in patients diagnosed as unresponsive by clinical consensus, but later rediagnosed as minimally conscious with the Coma Recovery Scale-Revised. Classification analysis of their brain network identified each of these misdiagnosed patients as minimally conscious, corroborating their behavioural diagnoses. If deployed at the bedside in the clinical context, such network measurements could complement systematic behavioural assessment and help reduce the high misdiagnosis rate reported

  4. Multisector Health Policy Networks in 15 Large US Cities

    Science.gov (United States)

    Leider, J. P.; Carothers, Bobbi J.; Castrucci, Brian C.; Hearne, Shelley

    2016-01-01

    Context: Local health departments (LHDs) have historically not prioritized policy development, although it is one of the 3 core areas they address. One strategy that may influence policy in LHD jurisdictions is the formation of partnerships across sectors to work together on local public health policy. Design: We used a network approach to examine LHD local health policy partnerships across 15 large cities from the Big Cities Health Coalition. Setting/Participants: We surveyed the health departments and their partners about their working relationships in 5 policy areas: core local funding, tobacco control, obesity and chronic disease, violence and injury prevention, and infant mortality. Outcome Measures: Drawing on prior literature linking network structures with performance, we examined network density, transitivity, centralization and centrality, member diversity, and assortativity of ties. Results: Networks included an average of 21.8 organizations. Nonprofits and government agencies made up the largest proportions of the networks, with 28.8% and 21.7% of network members, whereas for-profits and foundations made up the smallest proportions in all of the networks, with just 1.2% and 2.4% on average. Mean values of density, transitivity, diversity, assortativity, centralization, and centrality showed similarity across policy areas and most LHDs. The tobacco control and obesity/chronic disease networks were densest and most diverse, whereas the infant mortality policy networks were the most centralized and had the highest assortativity. Core local funding policy networks had lower scores than other policy area networks by most network measures. Conclusion: Urban LHDs partner with organizations from diverse sectors to conduct local public health policy work. Network structures are similar across policy areas jurisdictions. Obesity and chronic disease, tobacco control, and infant mortality networks had structures consistent with higher performing networks, whereas

  5. Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network

    Directory of Open Access Journals (Sweden)

    Jun He

    2017-07-01

    Full Text Available Artificial intelligence (AI techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN and support vector machine (SVM. The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods.

  6. Fault detection and diagnosis using statistical control charts and artificial neural networks

    International Nuclear Information System (INIS)

    Leger, R.P.; Garland, W.J.; Poehlman, W.F.S.

    1995-01-01

    In order to operate a successful plant or process, continuous improvement must be made in the areas of safety, quality and reliability. Central to this continuous improvement is the early or proactive detection and correct diagnosis of process faults. This research examines the feasibility of using Cumulative Summation (CUSUM) Control Charts and artificial neural networks together for fault detection and diagnosis (FDD). The proposed FDD strategy was tested on a model of the heat transport system of a CANDU nuclear reactor. The results of the investigation indicate that a FDD system using CUSUM Control Charts and a Radial Basis Function (RBF) neural network is not only feasible but also of promising potential. The control charts and neural network are linked together by using a characteristic fault signature pattern for each fault which is to be detected and diagnosed. When tested, the system was able to eliminate all false alarms at steady state, promptly detect 6 fault conditions and correctly diagnose 5 out of the 6 faults. The diagnosis for the sixth fault was inconclusive. (author). 9 refs., 6 tabs., 7 figs

  7. Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network.

    Science.gov (United States)

    He, Jun; Yang, Shixi; Gan, Chunbiao

    2017-07-04

    Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN) is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN) and support vector machine (SVM). The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods.

  8. Neural network based expert system for fault diagnosis of particle accelerators

    International Nuclear Information System (INIS)

    Dewidar, M.M.

    1997-01-01

    Particle accelerators are generators that produce beams of charged particles, acquiring different energies, depending on the accelerator type. The MGC-20 cyclotron is a cyclic particle accelerator used for accelerating protons, deuterons, alpha particles, and helium-3 to different energies. Its applications include isotope production, nuclear reaction, and mass spectroscopy studies. It is a complicated machine, it consists of five main parts, the ion source, the deflector, the beam transport system, the concentric and harmonic coils, and the radio frequency system. The diagnosis of this device is a very complex task. it depends on the conditions of 27 indicators of the control panel of the device. The accurate diagnosis can lead to a high system reliability and save maintenance costs. so an expert system for the cyclotron fault diagnosis is necessary to be built. In this thesis , a hybrid expert system was developed for the fault diagnosis of the MGC-20 cyclotron. Two intelligent techniques, multilayer feed forward back propagation neural network and the rule based expert system, are integrated as a pre-processor loosely coupled model to build the proposed hybrid expert system. The architecture of the developed hybrid expert system consists of two levels. The first level is two feed forward back propagation neural networks, used for isolating the faulty part of the cyclotron. The second level is the rule based expert system, used for troubleshooting the faults inside the isolated faulty part. 4-6 tabs., 4-5 figs., 36 refs

  9. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network.

    Science.gov (United States)

    Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

    2016-01-08

    A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.

  10. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

    Directory of Open Access Journals (Sweden)

    Ke Li

    2016-01-01

    Full Text Available A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF and Diagnostic Bayesian Network (DBN is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO. To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA is proposed to evaluate the sensitiveness of symptom parameters (SPs for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.

  11. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

    Science.gov (United States)

    Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

    2016-01-01

    A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006

  12. Health and Maintenance Status Determination and Predictive Fault Diagnosis System, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — The objective of this project is to demonstrate intelligent health and maintenance status determination and predictive fault diagnosis techniques for NASA rocket...

  13. A neural network approach to breast cancer diagnosis as a constraint satisfaction problem

    International Nuclear Information System (INIS)

    Tourassi, Georgia D.; Markey, Mia K.; Lo, Joseph Y.; Floyd, Carey E. Jr.

    2001-01-01

    A constraint satisfaction neural network (CSNN) approach is proposed for breast cancer diagnosis using mammographic and patient history findings. Initially, the diagnostic decision to biopsy was formulated as a constraint satisfaction problem. Then, an associative memory type neural network was applied to solve the problem. The proposed network has a flexible, nonhierarchical architecture that allows it to operate not only as a predictive tool but also as an analysis tool for knowledge discovery of association rules. The CSNN was developed and evaluated using a database of 500 nonpalpable breast lesions with definitive histopathological diagnosis. The CSNN diagnostic performance was evaluated using receiver operating characteristic analysis (ROC). The results of the study showed that the CSNN ROC area index was 0.84±0.02. The CSNN predictive performance is competitive with that achieved by experienced radiologists and backpropagation artificial neural networks (BP-ANNs) presented before. Furthermore, the study illustrates how CSNN can be used as a knowledge discovery tool overcoming some of the well-known limitations of BP-ANNs

  14. An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network.

    Science.gov (United States)

    Shen, Xiaolei; Zhang, Jiachi; Yan, Chenjun; Zhou, Hong

    2018-04-11

    In this paper, we present a new automatic diagnosis method for facial acne vulgaris which is based on convolutional neural networks (CNNs). To overcome the shortcomings of previous methods which were the inability to classify enough types of acne vulgaris. The core of our method is to extract features of images based on CNNs and achieve classification by classifier. A binary-classifier of skin-and-non-skin is used to detect skin area and a seven-classifier is used to achieve the classification task of facial acne vulgaris and healthy skin. In the experiments, we compare the effectiveness of our CNN and the VGG16 neural network which is pre-trained on the ImageNet data set. We use a ROC curve to evaluate the performance of binary-classifier and use a normalized confusion matrix to evaluate the performance of seven-classifier. The results of our experiments show that the pre-trained VGG16 neural network is effective in extracting features from facial acne vulgaris images. And the features are very useful for the follow-up classifiers. Finally, we try applying the classifiers both based on the pre-trained VGG16 neural network to assist doctors in facial acne vulgaris diagnosis.

  15. Wire Finishing Mill Rolling Bearing Fault Diagnosis Based on Feature Extraction and BP Neural Network

    Directory of Open Access Journals (Sweden)

    Hong-Yu LIU

    2014-10-01

    Full Text Available Rolling bearing is main part of rotary machine. It is frail section of rotary machine. Its running status affects entire mechanical equipment system performance directly. Vibration acceleration signals of the third finishing mill of Anshan Steel and Iron Group wire plant were collected in this paper. Fourier analysis, power spectrum analysis and wavelet transform were made on collected signals. Frequency domain feature extraction and wavelet transform feature extraction were made on collected signals. BP neural network fault diagnosis model was adopted. Frequency domain feature values and wavelet transform feature values were treated as neural network input values. Various typical fault models were treated as neural network output values. Corresponding relations between feature vector and fault omen were utilized. BP neural network model of typical wire plant finishing mill rolling bearing fault was constructed by training many groups sample data. After inputting sample needed to be diagnosed, wire plant finishing mill rolling bearing fault can be diagnosed. This research has important practical significance on enhancing rolling bearing fault diagnosis precision, repairing rolling bearing duly, decreasing stop time, enhancing equipment running efficiency and enhancing economic benefits.

  16. SHINE: Strategic Health Informatics Networks for Europe.

    Science.gov (United States)

    Kruit, D; Cooper, P A

    1994-10-01

    The mission of SHINE is to construct an open systems framework for the development of regional community healthcare telematic services that support and add to the strategic business objectives of European healthcare providers and purchasers. This framework will contain a Methodology, that identifies healthcare business processes and develops a supporting IT strategy, and the Open Health Environment. This consists of an architecture and information standards that are 'open' and will be available to any organisation wishing to construct SHINE conform regional healthcare telematic services. Results are: generic models, e.g., regional healthcare business networks, IT strategies; demonstrable, e.g., pilot demonstrators, application and service prototypes; reports, e.g., SHINE Methodology, pilot specifications & evaluations; proposals, e.g., service/interface specifications, standards conformance.

  17. Role of ventilation scintigraphy in diagnosis of acute pulmonary embolism: an evaluation using artificial neural networks

    International Nuclear Information System (INIS)

    Evander, Eva; Holst, Holger; Jaerund, Andreas; Wollmer, Per; Edenbrandt, Lars; Ohlsson, Mattias; Aastroem, Karl

    2003-01-01

    The purpose of this study was to assess the value of the ventilation study in the diagnosis of acute pulmonary embolism using a new automated method. Either perfusion scintigrams alone or two different combinations of ventilation/perfusion scintigrams were used as the only source of information regarding pulmonary embolism. A completely automated method based on computerised image processing and artificial neural networks was used for the interpretation. Three artificial neural networks were trained for the diagnosis of pulmonary embolism. Each network was trained with 18 automatically obtained features. Three different sets of features originating from three sets of scintigrams were used. One network was trained using features obtained from each set of perfusion scintigrams, including six projections. The second network was trained using features from each set of (joint) ventilation and perfusion studies in six projections. A third network was trained using features from the perfusion study in six projections combined with a single ventilation image from the posterior view. A total of 1,087 scintigrams from patients with suspected pulmonary embolism were used for network training. The test group consisted of 102 patients who had undergone both scintigraphy and pulmonary angiography. Performances in the test group were measured as area under the receiver operation characteristic curve. The performance of the neural network in interpreting perfusion scintigrams alone was 0.79 (95% confidence limits 0.71-0.86). When one ventilation image (posterior view) was added to the perfusion study, the performance was 0.84 (0.77-0.90). This increase was statistically significant (P=0.022). The performance increased to 0.87 (0.81-0.93) when all perfusion and ventilation images were used, and the increase in performance from 0.79 to 0.87 was also statistically significant (P=0.016). The automated method presented here for the interpretation of lung scintigrams shows a significant

  18. Genetic algorithm-based neural network for accidents diagnosis of research reactors on FPGA

    International Nuclear Information System (INIS)

    Ghuname, A.A.A.

    2012-01-01

    The Nuclear Research Reactors plants are expected to be operated with high levels of reliability, availability and safety. In order to achieve and maintain system stability and assure satisfactory and safe operation, there is increasing demand for automated systems to detect and diagnose such failures. Artificial Neural Networks (ANNs) are one of the most popular solutions because of their parallel structure, high speed, and their ability to give easy solution to complicated problems. The genetic algorithms (GAs) which are search algorithms (optimization techniques), in recent years, have been used to find the optimum construction of a neural network for definite application, as one of the advantages of its usage. Nowadays, Field Programmable Gate Arrays (FPGAs) are being an important implementation method of neural networks due to their high performance and they can easily be made parallel. The VHDL, which stands for VHSIC (Very High Speed Integrated Circuits) Hardware Description Language, have been used to describe the design behaviorally in addition to schematic and other description languages. The description of designs in synthesizable language such as VHDL make them reusable and be implemented in upgradeable systems like the Nuclear Research Reactors plants. In this thesis, the work was carried out through three main parts.In the first part, the Nuclear Research Reactors accident's pattern recognition is tackled within the artificial neural network approach. Such patterns are introduced initially without noise. And, to increase the reliability of such neural network, the noise ratio up to 50% was added for training in order to ensure the recognition of these patterns if it introduced with noise.The second part is concerned with the construction of Artificial Neural Networks (ANNs) using Genetic algorithms (GAs) for the nuclear accidents diagnosis. MATLAB ANNs toolbox and GAs toolbox are employed to optimize an ANN for this purpose. The results obtained show

  19. Blind Source Separation and Dynamic Fuzzy Neural Network for Fault Diagnosis in Machines

    International Nuclear Information System (INIS)

    Huang, Haifeng; Ouyang, Huajiang; Gao, Hongli

    2015-01-01

    Many assessment and detection methods are used to diagnose faults in machines. High accuracy in fault detection and diagnosis can be achieved by using numerical methods with noise-resistant properties. However, to some extent, noise always exists in measured data on real machines, which affects the identification results, especially in the diagnosis of early- stage faults. In view of this situation, a damage assessment method based on blind source separation and dynamic fuzzy neural network (DFNN) is presented to diagnose the early-stage machinery faults in this paper. In the processing of measurement signals, blind source separation is adopted to reduce noise. Then sensitive features of these faults are obtained by extracting low dimensional manifold characteristics from the signals. The model for fault diagnosis is established based on DFNN. Furthermore, on-line computation is accelerated by means of compressed sensing. Numerical vibration signals of ball screw fault modes are processed on the model for mechanical fault diagnosis and the results are in good agreement with the actual condition even at the early stage of fault development. This detection method is very useful in practice and feasible for early-stage fault diagnosis. (paper)

  20. A Novel Fault Diagnosis Method for Rotating Machinery Based on a Convolutional Neural Network.

    Science.gov (United States)

    Guo, Sheng; Yang, Tao; Gao, Wei; Zhang, Chen

    2018-05-04

    Fault diagnosis is critical to ensure the safety and reliable operation of rotating machinery. Most methods used in fault diagnosis of rotating machinery extract a few feature values from vibration signals for fault diagnosis, which is a dimensionality reduction from the original signal and may omit some important fault messages in the original signal. Thus, a novel diagnosis method is proposed involving the use of a convolutional neural network (CNN) to directly classify the continuous wavelet transform scalogram (CWTS), which is a time-frequency domain transform of the original signal and can contain most of the information of the vibration signals. In this method, CWTS is formed by discomposing vibration signals of rotating machinery in different scales using wavelet transform. Then the CNN is trained to diagnose faults, with CWTS as the input. A series of experiments is conducted on the rotor experiment platform using this method. The results indicate that the proposed method can diagnose the faults accurately. To verify the universality of this method, the trained CNN was also used to perform fault diagnosis for another piece of rotor equipment, and a good result was achieved.

  1. Tufts academic health information network: concept and scenario.

    Science.gov (United States)

    Stearns, N S

    1986-04-01

    Tufts University School of Medicine's new health sciences education building, the Arthur M. Sackler Center for Health Communications, will house a modern medical library and computer center, classrooms, auditoria, and media facilities. The building will also serve as the center for an information and communication network linking the medical school and adjacent New England Medical Center, Tufts' primary teaching hospital, with Tufts Associated Teaching Hospitals throughout New England. Ultimately, the Tufts network will join other gateway networks, information resource facilities, health care institutions, and medical schools throughout the world. The center and the network are intended to facilitate and improve the education of health professionals, the delivery of health care to patients, the conduct of research, and the implementation of administrative management approaches that should provide more efficient utilization of resources and save dollars. A model and scenario show how health care delivery and health care education are integrated through better use of information transfer technologies by health information specialists, practitioners, and educators.

  2. Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network.

    Science.gov (United States)

    Yang, Zhongliang; Huang, Yongfeng; Jiang, Yiran; Sun, Yuxi; Zhang, Yu-Jin; Luo, Pengcheng

    2018-04-20

    Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67% accuracy and 96.02% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.

  3. Social Network Types and Mental Health Among LGBT Older Adults.

    Science.gov (United States)

    Kim, Hyun-Jun; Fredriksen-Goldsen, Karen I; Bryan, Amanda E B; Muraco, Anna

    2017-02-01

    This study was designed to identify social network types among lesbian, gay, bisexual, and transgender (LGBT) older adults and examine the relationship between social network type and mental health. We analyzed the 2014 survey data of LGBT adults aged 50 and older (N = 2,450) from Aging with Pride: National Health, Aging, and Sexuality/Gender Study. Latent profile analyses were conducted to identify clusters of social network ties based on 11 indicators. Multiple regression analysis was performed to examine the association between social network types and mental health. We found five social network types. Ordered from greatest to least access to family, friend, and other non-family network ties, they were diverse, diverse/no children, immediate family-focused, friend-centered/restricted, and fully restricted. The friend-centered/restricted (33%) and diverse/no children network types (31%) were the most prevalent. Among individuals with the friend-centered/restricted type, access to social networks was limited to friends, and across both types children were not present. The least prevalent type was the fully restricted network type (6%). Social network type was significantly associated with mental health, after controlling for background characteristics and total social network size; those with the fully restricted type showed the poorest mental health. Unique social network types (diverse/no children and friend-centered/restricted) emerge among LGBT older adults. Moreover, individuals with fully restricted social networks are at particular risk due to heightened health needs and limited social resources. This study highlights the importance of understanding heterogeneous social relations and developing tailored interventions to promote social connectedness and mental health in LGBT older adults. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  4. Social Network Types and Mental Health Among LGBT Older Adults

    Science.gov (United States)

    Kim, Hyun-Jun; Fredriksen-Goldsen, Karen I.; Bryan, Amanda E. B.; Muraco, Anna

    2017-01-01

    Purpose of the Study: This study was designed to identify social network types among lesbian, gay, bisexual, and transgender (LGBT) older adults and examine the relationship between social network type and mental health. Design and Methods: We analyzed the 2014 survey data of LGBT adults aged 50 and older (N = 2,450) from Aging with Pride: National Health, Aging, and Sexuality/Gender Study. Latent profile analyses were conducted to identify clusters of social network ties based on 11 indicators. Multiple regression analysis was performed to examine the association between social network types and mental health. Results: We found five social network types. Ordered from greatest to least access to family, friend, and other non-family network ties, they were diverse, diverse/no children, immediate family-focused, friend-centered/restricted, and fully restricted. The friend-centered/restricted (33%) and diverse/no children network types (31%) were the most prevalent. Among individuals with the friend-centered/restricted type, access to social networks was limited to friends, and across both types children were not present. The least prevalent type was the fully restricted network type (6%). Social network type was significantly associated with mental health, after controlling for background characteristics and total social network size; those with the fully restricted type showed the poorest mental health. Implications: Unique social network types (diverse/no children and friend-centered/restricted) emerge among LGBT older adults. Moreover, individuals with fully restricted social networks are at particular risk due to heightened health needs and limited social resources. This study highlights the importance of understanding heterogeneous social relations and developing tailored interventions to promote social connectedness and mental health in LGBT older adults. PMID:28087798

  5. Diagnosis of cranial hemangioma: Comparison between logistic regression analysis and neuronal network

    International Nuclear Information System (INIS)

    Arana, E.; Marti-Bonmati, L.; Bautista, D.; Paredes, R.

    1998-01-01

    To study the utility of logistic regression and the neuronal network in the diagnosis of cranial hemangiomas. Fifteen patients presenting hemangiomas were selected form a total of 167 patients with cranial lesions. All were evaluated by plain radiography and computed tomography (CT). Nineteen variables in their medical records were reviewed. Logistic regression and neuronal network models were constructed and validated by the jackknife (leave-one-out) approach. The yields of the two models were compared by means of ROC curves, using the area under the curve as parameter. Seven men and 8 women presented hemangiomas. The mean age of these patients was 38.4 (15.4 years (mea ± standard deviation). Logistic regression identified as significant variables the shape, soft tissue mass and periosteal reaction. The neuronal network lent more importance to the existence of ossified matrix, ruptured cortical vein and the mixed calcified-blastic (trabeculated) pattern. The neuronal network showed a greater yield than logistic regression (Az, 0.9409) (0.004 versus 0.7211± 0.075; p<0.001). The neuronal network discloses hidden interactions among the variables, providing a higher yield in the characterization of cranial hemangiomas and constituting a medical diagnostic acid. (Author)29 refs

  6. A Case for Open Network Health Systems: Systems as Networks in Public Mental Health.

    Science.gov (United States)

    Rhodes, Michael Grant; de Vries, Marten W

    2017-01-08

    Increases in incidents involving so-called confused persons have brought attention to the potential costs of recent changes to public mental health (PMH) services in the Netherlands. Decentralized under the (Community) Participation Act (2014), local governments must find resources to compensate for reduced central funding to such services or "innovate." But innovation, even when pressure for change is intense, is difficult. This perspective paper describes experience during and after an investigation into a particularly violent incident and murder. The aim was to provide recommendations to improve the functioning of local PMH services. The investigation concluded that no specific failure by an individual professional or service provider facility led to the murder. Instead, also as a result of the Participation Act that severed communication lines between individuals and organizations, information sharing failures were likely to have reduced system level capacity to identify risks. The methods and analytical frameworks employed to reach this conclusion, also lead to discussion as to the plausibility of an unconventional solution. If improving communication is the primary problem, non-hierarchical information, and organizational networks arise as possible and innovative system solutions. The proposal for debate is that traditional "health system" definitions, literature and narratives, and operating assumptions in public (mental) health are 'locked in' constraining technical and organization innovations. If we view a "health system" as an adaptive system of economic and social "networks," it becomes clear that the current orthodox solution, the so-called integrated health system, typically results in a "centralized hierarchical" or "tree" network. An overlooked alternative that breaks out of the established policy narratives is the view of a 'health systems' as a non-hierarchical organizational structure or 'Open Network.' In turn, this opens new technological and

  7. A Case for Open Network Health Systems: Systems as Networks in Public Mental Health

    Directory of Open Access Journals (Sweden)

    Michael Grant Rhodes

    2017-03-01

    Full Text Available Increases in incidents involving so-called confused persons have brought attention to the potential costs of recent changes to public mental health (PMH services in the Netherlands. Decentralized under the (Community Participation Act (2014, local governments must find resources to compensate for reduced central funding to such services or “innovate.” But innovation, even when pressure for change is intense, is difficult. This perspective paper describes experience during and after an investigation into a particularly violent incident and murder. The aim was to provide recommendations to improve the functioning of local PMH services. The investigation concluded that no specific failure by an individual professional or service provider facility led to the murder. Instead, also as a result of the Participation Act that severed communication lines between individuals and organizations, information sharing failures were likely to have reduced system level capacity to identify risks. The methods and analytical frameworks employed to reach this conclusion, also lead to discussion as to the plausibility of an unconventional solution. If improving communication is the primary problem, non-hierarchical information, and organizational networks arise as possible and innovative system solutions. The proposal for debate is that traditional “health system” definitions, literature and narratives, and operating assumptions in public (mental health are ‘locked in’ constraining technical and organization innovations. If we view a “health system” as an adaptive system of economic and social “networks,” it becomes clear that the current orthodox solution, the so-called integrated health system, typically results in a “centralized hierarchical” or “tree” network. An overlooked alternative that breaks out of the established policy narratives is the view of a ‘health systems’ as a non-hierarchical organizational structure or

  8. A modular neural network scheme applied to fault diagnosis in electric power systems.

    Science.gov (United States)

    Flores, Agustín; Quiles, Eduardo; García, Emilio; Morant, Francisco; Correcher, Antonio

    2014-01-01

    This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

  9. A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

    Directory of Open Access Journals (Sweden)

    Agustín Flores

    2014-01-01

    Full Text Available This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

  10. Social network characteristics associated with health promoting behaviors among Latinos.

    Science.gov (United States)

    Marquez, Becky; Elder, John P; Arredondo, Elva M; Madanat, Hala; Ji, Ming; Ayala, Guadalupe X

    2014-06-01

    This study examined the relationship between social network characteristics and health promoting behaviors (having a routine medical check-up, consuming no alcohol, consuming no fast food, and meeting recommendations for leisure-time physical activity and sleep duration) among Latinos to identify potential targets for behavioral interventions. Personal network characteristics and health behavior data were collected from a community sample of 393 adult Latinos (73% women) in San Diego County, California. Network characteristics consisted of size and composition. Network size was calculated by the number of alters listed on a name generator questionnaire eliciting people with whom respondents discussed personal issues. Network composition variables were the proportion of Latinos, Spanish-speakers, females, family, and friends listed in the name generator. Additional network composition variables included marital status and the number of adults or children in the household. Network members were predominately Latinos (95%), Spanish-speakers (80%), females (64%), and family (55%). In multivariate logistic regression analyses, gender moderated the relationship between network composition, but not size, and a health behavior. Married women were more likely to have had a routine medical check-up than married men. For both men and women, having a larger network was associated with meeting the recommendation for leisure-time physical activity. Few social network characteristics were significantly associated with health promoting behaviors, suggesting a need to examine other aspects of social relationships that may influence health behaviors. PsycINFO Database Record (c) 2014 APA, all rights reserved.

  11. Fault Diagnosis Based on Chemical Sensor Data with an Active Deep Neural Network.

    Science.gov (United States)

    Jiang, Peng; Hu, Zhixin; Liu, Jun; Yu, Shanen; Wu, Feng

    2016-10-13

    Big sensor data provide significant potential for chemical fault diagnosis, which involves the baseline values of security, stability and reliability in chemical processes. A deep neural network (DNN) with novel active learning for inducing chemical fault diagnosis is presented in this study. It is a method using large amount of chemical sensor data, which is a combination of deep learning and active learning criterion to target the difficulty of consecutive fault diagnosis. DNN with deep architectures, instead of shallow ones, could be developed through deep learning to learn a suitable feature representation from raw sensor data in an unsupervised manner using stacked denoising auto-encoder (SDAE) and work through a layer-by-layer successive learning process. The features are added to the top Softmax regression layer to construct the discriminative fault characteristics for diagnosis in a supervised manner. Considering the expensive and time consuming labeling of sensor data in chemical applications, in contrast to the available methods, we employ a novel active learning criterion for the particularity of chemical processes, which is a combination of Best vs. Second Best criterion (BvSB) and a Lowest False Positive criterion (LFP), for further fine-tuning of diagnosis model in an active manner rather than passive manner. That is, we allow models to rank the most informative sensor data to be labeled for updating the DNN parameters during the interaction phase. The effectiveness of the proposed method is validated in two well-known industrial datasets. Results indicate that the proposed method can obtain superior diagnosis accuracy and provide significant performance improvement in accuracy and false positive rate with less labeled chemical sensor data by further active learning compared with existing methods.

  12. Fault Diagnosis Based on Chemical Sensor Data with an Active Deep Neural Network

    Science.gov (United States)

    Jiang, Peng; Hu, Zhixin; Liu, Jun; Yu, Shanen; Wu, Feng

    2016-01-01

    Big sensor data provide significant potential for chemical fault diagnosis, which involves the baseline values of security, stability and reliability in chemical processes. A deep neural network (DNN) with novel active learning for inducing chemical fault diagnosis is presented in this study. It is a method using large amount of chemical sensor data, which is a combination of deep learning and active learning criterion to target the difficulty of consecutive fault diagnosis. DNN with deep architectures, instead of shallow ones, could be developed through deep learning to learn a suitable feature representation from raw sensor data in an unsupervised manner using stacked denoising auto-encoder (SDAE) and work through a layer-by-layer successive learning process. The features are added to the top Softmax regression layer to construct the discriminative fault characteristics for diagnosis in a supervised manner. Considering the expensive and time consuming labeling of sensor data in chemical applications, in contrast to the available methods, we employ a novel active learning criterion for the particularity of chemical processes, which is a combination of Best vs. Second Best criterion (BvSB) and a Lowest False Positive criterion (LFP), for further fine-tuning of diagnosis model in an active manner rather than passive manner. That is, we allow models to rank the most informative sensor data to be labeled for updating the DNN parameters during the interaction phase. The effectiveness of the proposed method is validated in two well-known industrial datasets. Results indicate that the proposed method can obtain superior diagnosis accuracy and provide significant performance improvement in accuracy and false positive rate with less labeled chemical sensor data by further active learning compared with existing methods. PMID:27754386

  13. Fault Diagnosis Based on Chemical Sensor Data with an Active Deep Neural Network

    Directory of Open Access Journals (Sweden)

    Peng Jiang

    2016-10-01

    Full Text Available Big sensor data provide significant potential for chemical fault diagnosis, which involves the baseline values of security, stability and reliability in chemical processes. A deep neural network (DNN with novel active learning for inducing chemical fault diagnosis is presented in this study. It is a method using large amount of chemical sensor data, which is a combination of deep learning and active learning criterion to target the difficulty of consecutive fault diagnosis. DNN with deep architectures, instead of shallow ones, could be developed through deep learning to learn a suitable feature representation from raw sensor data in an unsupervised manner using stacked denoising auto-encoder (SDAE and work through a layer-by-layer successive learning process. The features are added to the top Softmax regression layer to construct the discriminative fault characteristics for diagnosis in a supervised manner. Considering the expensive and time consuming labeling of sensor data in chemical applications, in contrast to the available methods, we employ a novel active learning criterion for the particularity of chemical processes, which is a combination of Best vs. Second Best criterion (BvSB and a Lowest False Positive criterion (LFP, for further fine-tuning of diagnosis model in an active manner rather than passive manner. That is, we allow models to rank the most informative sensor data to be labeled for updating the DNN parameters during the interaction phase. The effectiveness of the proposed method is validated in two well-known industrial datasets. Results indicate that the proposed method can obtain superior diagnosis accuracy and provide significant performance improvement in accuracy and false positive rate with less labeled chemical sensor data by further active learning compared with existing methods.

  14. Simulation studies of a wide area health care network.

    Science.gov (United States)

    McDaniel, J. G.

    1994-01-01

    There is an increasing number of efforts to install wide area health care networks. Some of these networks are being built to support several applications over a wide user base consisting primarily of medical practices, hospitals, pharmacies, medical laboratories, payors, and suppliers. Although on-line, multi-media telecommunication is desirable for some purposes such as cardiac monitoring, store-and-forward messaging is adequate for many common, high-volume applications. Laboratory test results and payment claims, for example, can be distributed using electronic messaging networks. Several network prototypes have been constructed to determine the technical problems and to assess the effectiveness of electronic messaging in wide area health care networks. Our project, Health Link, developed prototype software that was able to use the public switched telephone network to exchange messages automatically, reliably and securely. The network could be configured to accommodate the many different traffic patterns and cost constraints of its users. Discrete event simulations were performed on several network models. Canonical star and mesh networks, that were composed of nodes operating at steady state under equal loads, were modeled. Both topologies were found to support the throughput of a generic wide area health care network. The mean message delivery time of the mesh network was found to be less than that of the star network. Further simulations were conducted for a realistic large-scale health care network consisting of 1,553 doctors, 26 hospitals, four medical labs, one provincial lab and one insurer. Two network topologies were investigated: one using predominantly peer-to-peer communication, the other using client-server communication.(ABSTRACT TRUNCATED AT 250 WORDS) PMID:7949966

  15. Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data.

    Science.gov (United States)

    Sun, Wenqing; Tseng, Tzu-Liang Bill; Zhang, Jianying; Qian, Wei

    2017-04-01

    In this study we developed a graph based semi-supervised learning (SSL) scheme using deep convolutional neural network (CNN) for breast cancer diagnosis. CNN usually needs a large amount of labeled data for training and fine tuning the parameters, and our proposed scheme only requires a small portion of labeled data in training set. Four modules were included in the diagnosis system: data weighing, feature selection, dividing co-training data labeling, and CNN. 3158 region of interests (ROIs) with each containing a mass extracted from 1874 pairs of mammogram images were used for this study. Among them 100 ROIs were treated as labeled data while the rest were treated as unlabeled. The area under the curve (AUC) observed in our study was 0.8818, and the accuracy of CNN is 0.8243 using the mixed labeled and unlabeled data. Copyright © 2016. Published by Elsevier Ltd.

  16. Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet.

    Science.gov (United States)

    Shao, Haidong; Jiang, Hongkai; Wang, Fuan; Wang, Yanan

    2017-07-01

    Automatic and accurate identification of rolling bearing fault categories, especially for the fault severities and compound faults, is a challenge in rotating machinery fault diagnosis. For this purpose, a novel method called adaptive deep belief network (DBN) with dual-tree complex wavelet packet (DTCWPT) is developed in this paper. DTCWPT is used to preprocess the vibration signals to refine the fault characteristics information, and an original feature set is designed from each frequency-band signal of DTCWPT. An adaptive DBN is constructed to improve the convergence rate and identification accuracy with multiple stacked adaptive restricted Boltzmann machines (RBMs). The proposed method is applied to the fault diagnosis of rolling bearings. The results confirm that the proposed method is more effective than the existing methods. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  17. Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network

    Science.gov (United States)

    Jiang, Hongkai; Li, Xingqiu; Shao, Haidong; Zhao, Ke

    2018-06-01

    Traditional intelligent fault diagnosis methods for rolling bearings heavily depend on manual feature extraction and feature selection. For this purpose, an intelligent deep learning method, named the improved deep recurrent neural network (DRNN), is proposed in this paper. Firstly, frequency spectrum sequences are used as inputs to reduce the input size and ensure good robustness. Secondly, DRNN is constructed by the stacks of the recurrent hidden layer to automatically extract the features from the input spectrum sequences. Thirdly, an adaptive learning rate is adopted to improve the training performance of the constructed DRNN. The proposed method is verified with experimental rolling bearing data, and the results confirm that the proposed method is more effective than traditional intelligent fault diagnosis methods.

  18. Diagnosis of compliance of health care product processing in Primary Health Care

    Directory of Open Access Journals (Sweden)

    Camila Eugenia Roseira

    Full Text Available ABSTRACT Objective: identify the compliance of health care product processing in Primary Health Care and assess possible differences in the compliance among the services characterized as Primary Health Care Service and Family Health Service. Method: quantitative, observational, descriptive and inferential study with the application of structure, process and outcome indicators of the health care product processing at ten services in an interior city of the State of São Paulo - Brazil. Results: for all indicators, the compliance indices were inferior to the ideal levels. No statistically significant difference was found in the indicators between the two types of services investigated. The health care product cleaning indicators obtained the lowest compliance index, while the indicator technical-operational resources for the preparation, conditioning, disinfection/sterilization, storage and distribution of health care products obtained the best index. Conclusion: the diagnosis of compliance of health care product processing at the services assessed indicates that the quality of the process is jeopardized, as no results close to ideal levels were obtained at any service. In addition, no statistically significant difference in these indicators was found between the two types of services studied.

  19. Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders.

    Science.gov (United States)

    Liu, Han; Zhou, Jianzhong; Zheng, Yang; Jiang, Wei; Zhang, Yuncheng

    2018-04-19

    As the rolling bearings being the key part of rotary machine, its healthy condition is quite important for safety production. Fault diagnosis of rolling bearing has been research focus for the sake of improving the economic efficiency and guaranteeing the operation security. However, the collected signals are mixed with ambient noise during the operation of rotary machine, which brings great challenge to the exact diagnosis results. Using signals collected from multiple sensors can avoid the loss of local information and extract more helpful characteristics. Recurrent Neural Networks (RNN) is a type of artificial neural network which can deal with multiple time sequence data. The capacity of RNN has been proved outstanding for catching time relevance about time sequence data. This paper proposed a novel method for bearing fault diagnosis with RNN in the form of an autoencoder. In this approach, multiple vibration value of the rolling bearings of the next period are predicted from the previous period by means of Gated Recurrent Unit (GRU)-based denoising autoencoder. These GRU-based non-linear predictive denoising autoencoders (GRU-NP-DAEs) are trained with strong generalization ability for each different fault pattern. Then for the given input data, the reconstruction errors between the next period data and the output data generated by different GRU-NP-DAEs are used to detect anomalous conditions and classify fault type. Classic rotating machinery datasets have been employed to testify the effectiveness of the proposed diagnosis method and its preponderance over some state-of-the-art methods. The experiment results indicate that the proposed method achieves satisfactory performance with strong robustness and high classification accuracy. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

  20. DIAGNOSIS AND PREDICTION OF CHOLECYSTITIS DEVELOPMENT ON THE BASIS OF NEURAL NETWORK ANALYSIS OF RISK FACTORS

    Directory of Open Access Journals (Sweden)

    V. A. Lazarenko

    2017-01-01

    Full Text Available Purpose. To develop an artificial neural network for diagnosing and predicting the development of cholecystitis based on an analysis of data on risk factors, and to explore the possibilities of its application in real clinical practice.Materials and methods. The collection of materials was held in at the hospitals of the city of Kursk and included a survey of 488 patients with hepatopancreatoduodenal diseases. 203 patients were suffering from cholecystitis, in 285 patients the diagnosis of cholecystitis was excluded. Analysis of risk factors’ data (such as sex, age, bad habits, profession, family relationships, etc. was carried out using an internally developed artificial neural network (multilayer perceptron with hyperbolic tangent as the activation function. The computer program “System of Intellectual Analysis and Diagnosis of Diseases” was registered in accordance with established procedure (Certificate No. 2017613090.Results. The use of neural network analysis of data on risk factors in comparison with the processing of information that forms a clinical picture allows the diagnosis of a potential disease with cholecystitis before the onset of symptoms. The training of the artificial neural network with a quantitative output coding the age of probable hospitalization made it possible to generate an array of values, signifficantly (α ≤ 0.001 not differing from the empirical data. The difference between the mean calculated and mean empirical values was 0.45 for the training set and 1.75 for the clinical approbation group. The mean absolute error was within the range of 1.87–2.07 years.Conclusion. 1. The proposed new approach to the diagnosis and prognosis of cholecystitis has demonstrated its effectiveness, which is confirmed in clinical approbation by the levels of sensitivity (94.44%, m = 2.26 and specificity (80.6%, m = 3.9.2. The error in predicting the age of probable hospitalization of patients with cholecystitis did not

  1. Wireless sensor networks for structural health monitoring

    CERN Document Server

    Cao, Jiannong

    2016-01-01

    This brief covers the emerging area of wireless sensor network (WSN)-based structural health monitoring (SHM) systems, and introduces the authors’ WSN-based platform called SenetSHM. It helps the reader differentiate specific requirements of SHM applications from other traditional WSN applications, and demonstrates how these requirements are addressed by using a series of systematic approaches. The brief serves as a practical guide, explaining both the state-of-the-art technologies in domain-specific applications of WSNs, as well as the methodologies used to address the specific requirements for a WSN application. In particular, the brief offers instruction for problem formulation and problem solving based on the authors’ own experiences implementing SenetSHM. Seven concise chapters cover the development of hardware and software design of SenetSHM, as well as in-field experiments conducted while testing the platform. The brief’s exploration of the SenetSHM platform is a valuable feature for civil engine...

  2. Tracks: A National Environmental Public Health Tracking Network Overview

    Centers for Disease Control (CDC) Podcasts

    2009-08-04

    In this podcast, Dr. Mike McGeehin, Director of CDC's Division of Environmental Hazards and Health Effects, provides an overview of the National Environmental Public Health Tracking Network. It highlights the Tracking Network's goal, how it will improve public health, its audience, and much more.  Created: 8/4/2009 by Centers for Disease Control and Prevention (CDC).   Date Released: 8/4/2009.

  3. EXPERIMENT BASED FAULT DIAGNOSIS ON BOTTLE FILLING PLANT WITH LVQ ARTIFICIAL NEURAL NETWORK ALGORITHM

    Directory of Open Access Journals (Sweden)

    Mustafa DEMETGÜL

    2008-01-01

    Full Text Available In this study, an artificial neural network is developed to find an error rapidly on pneumatic system. Also the ANN prevents the system versus the failure. The error on the experimental bottle filling plant can be defined without any interference using analog values taken from pressure sensors and linear potentiometers. The sensors and potentiometers are placed on different places of the plant. Neural network diagnosis faults on plant, where no bottle, cap closing cylinder B is not working, bottle cap closing cylinder C is not working, air pressure is not sufficient, water is not filling and low air pressure faults. The fault is diagnosed by artificial neural network with LVQ. It is possible to find an failure by using normal programming or PLC. The reason offing Artificial Neural Network is to give a information where the fault is. However, ANN can be used for different systems. The aim is to find the fault by using ANN simultaneously. In this situation, the error taken place on the pneumatic system is collected by a data acquisition card. It is observed that the algorithm is very capable program for many industrial plants which have mechatronic systems.

  4. Empowering health personnel for decentralized health planning in India: The Public Health Resource Network

    Directory of Open Access Journals (Sweden)

    Prasad Vandana

    2009-07-01

    Full Text Available Abstract The Public Health Resource Network is an innovative distance-learning course in training, motivating, empowering and building a network of health personnel from government and civil society groups. Its aim is to build human resource capacity for strengthening decentralized health planning, especially at the district level, to improve accountability of health systems, elicit community participation for health, ensure equitable and accessible health facilities and to bring about convergence in programmes and services. The question confronting health systems in India is how best to reform, revitalize and resource primary health systems to deliver different levels of service aligned to local realities, ensuring universal coverage, equitable access, efficiency and effectiveness, through an empowered cadre of health personnel. To achieve these outcomes it is essential that health planning be decentralized. Districts vary widely according to the specific needs of their population, and even more so in terms of existing interventions and available resources. Strategies, therefore, have to be district-specific, not only because health needs vary, but also because people's perceptions and capacities to intervene and implement programmes vary. In centrally designed plans there is little scope for such adaptation and contextualization, and hence decentralized planning becomes crucial. To undertake these initiatives, there is a strong need for trained, motivated, empowered and networked health personnel. It is precisely at this level that a lack of technical knowledge and skills and the absence of a supportive network or adequate educational opportunities impede personnel from making improvements. The absence of in-service training and of training curricula that reflect field realities also adds to this, discouraging health workers from pursuing effective strategies. The Public Health Resource Network is thus an attempt to reach out to motivated

  5. Empowering health personnel for decentralized health planning in India: The Public Health Resource Network.

    Science.gov (United States)

    Kalita, Anuska; Zaidi, Sarover; Prasad, Vandana; Raman, V R

    2009-07-20

    The Public Health Resource Network is an innovative distance-learning course in training, motivating, empowering and building a network of health personnel from government and civil society groups. Its aim is to build human resource capacity for strengthening decentralized health planning, especially at the district level, to improve accountability of health systems, elicit community participation for health, ensure equitable and accessible health facilities and to bring about convergence in programmes and services. The question confronting health systems in India is how best to reform, revitalize and resource primary health systems to deliver different levels of service aligned to local realities, ensuring universal coverage, equitable access, efficiency and effectiveness, through an empowered cadre of health personnel. To achieve these outcomes it is essential that health planning be decentralized. Districts vary widely according to the specific needs of their population, and even more so in terms of existing interventions and available resources. Strategies, therefore, have to be district-specific, not only because health needs vary, but also because people's perceptions and capacities to intervene and implement programmes vary. In centrally designed plans there is little scope for such adaptation and contextualization, and hence decentralized planning becomes crucial. To undertake these initiatives, there is a strong need for trained, motivated, empowered and networked health personnel. It is precisely at this level that a lack of technical knowledge and skills and the absence of a supportive network or adequate educational opportunities impede personnel from making improvements. The absence of in-service training and of training curricula that reflect field realities also adds to this, discouraging health workers from pursuing effective strategies. The Public Health Resource Network is thus an attempt to reach out to motivated though often isolated health

  6. Proposal for Alzheimer’s diagnosis using molecular buffer and bus network

    Directory of Open Access Journals (Sweden)

    Mitatha S

    2011-06-01

    Full Text Available S Mitatha1, N Moongfangklang1, MA Jalil2, N Suwanpayak3, T Saktioto4, J Ali4, PP Yupapin31Hybrid Computing Research Laboratory, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand; 2Ibnu Sina Institute of Fundamental Science Studies, Nanotechnology Research Alliance, Universiti Teknologi Malaysia, Johor Bahru, Malaysia; 3Nanoscale Science and Engineering Research Alliance (N'SERA, Advanced Research Center for Photonics, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand; 4Institute of Advanced Photonics Science, Nanotechnology Research Alliance, Universiti Teknologi Malaysia, Johor Bahru, MalaysiaAbstract: A novel design of an optical trapping tool for tangle protein (tau tangles, ß-amyloid plaques and molecular motor storage and delivery using a PANDA ring resonator is proposed. The optical vortices can be generated and controlled to form the trapping tools in the same way as the optical tweezers. In theory, the trapping force is formed by the combination between the gradient field and scattering photons, and is reviewed. By using the intense optical vortices generated within the PANDA ring resonator, the required molecular volumes can be trapped and moved dynamically within the molecular buffer and bus network. The tangle protein and molecular motor can transport and connect to the required destinations, enabling availability for Alzheimer’s diagnosis.Keywords: Alzheimer’s disease, molecular diagnosis, optical trapping tool, molecular networks

  7. Social network analysis of public health programs to measure partnership.

    Science.gov (United States)

    Schoen, Martin W; Moreland-Russell, Sarah; Prewitt, Kim; Carothers, Bobbi J

    2014-12-01

    In order to prevent chronic diseases, community-based programs are encouraged to take an ecological approach to public health promotion and involve many diverse partners. Little is known about measuring partnership in implementing public health strategies. We collected data from 23 Missouri communities in early 2012 that received funding from three separate programs to prevent obesity and/or reduce tobacco use. While all of these funding programs encourage partnership, only the Social Innovation for Missouri (SIM) program included a focus on building community capacity and enhancing collaboration. Social network analysis techniques were used to understand contact and collaboration networks in community organizations. Measurements of average degree, density, degree centralization, and betweenness centralization were calculated for each network. Because of the various sizes of the networks, we conducted comparative analyses with and without adjustment for network size. SIM programs had increased measurements of average degree for partner collaboration and larger networks. When controlling for network size, SIM groups had higher measures of network density and lower measures of degree centralization and betweenness centralization. SIM collaboration networks were more dense and less centralized, indicating increased partnership. The methods described in this paper can be used to compare partnership in community networks of various sizes. Further research is necessary to define causal mechanisms of partnership development and their relationship to public health outcomes. Copyright © 2014 Elsevier Ltd. All rights reserved.

  8. Synergistic combination of systems for structural health monitoring and earthquake early warning for structural health prognosis and diagnosis

    Science.gov (United States)

    Wu, Stephen; Beck, James L.

    2012-04-01

    Earthquake early warning (EEW) systems are currently operating nationwide in Japan and are in beta-testing in California. Such a system detects an earthquake initiation using online signals from a seismic sensor network and broadcasts a warning of the predicted location and magnitude a few seconds to a minute or so before an earthquake hits a site. Such a system can be used synergistically with installed structural health monitoring (SHM) systems to enhance pre-event prognosis and post-event diagnosis of structural health. For pre-event prognosis, the EEW system information can be used to make probabilistic predictions of the anticipated damage to a structure using seismic loss estimation methodologies from performance-based earthquake engineering. These predictions can support decision-making regarding the activation of appropriate mitigation systems, such as stopping traffic from entering a bridge that has a predicted high probability of damage. Since the time between warning and arrival of the strong shaking is very short, probabilistic predictions must be rapidly calculated and the decision making automated for the mitigation actions. For post-event diagnosis, the SHM sensor data can be used in Bayesian updating of the probabilistic damage predictions with the EEW predictions as a prior. Appropriate Bayesian methods for SHM have been published. In this paper, we use pre-trained surrogate models (or emulators) based on machine learning methods to make fast damage and loss predictions that are then used in a cost-benefit decision framework for activation of a mitigation measure. A simple illustrative example of an infrastructure application is presented.

  9. Rural Health Networks: How Network Analysis Can Inform Patient Care and Organizational Collaboration in a Rural Breast Cancer Screening Network.

    Science.gov (United States)

    Prusaczyk, Beth; Maki, Julia; Luke, Douglas A; Lobb, Rebecca

    2018-04-15

    Rural health networks have the potential to improve health care quality and access. Despite this, the use of network analysis to study rural health networks is limited. The purpose of this study was to use network analysis to understand how a network of rural breast cancer care providers deliver services and to demonstrate the value of this methodology in this research area. Leaders at 47 Federally Qualified Health Centers and Rural Health Clinics across 10 adjacent rural counties were asked where they refer patients for mammograms or breast biopsies. These clinics and the 22 referral providers that respondents named comprised the network. The network was analyzed graphically and statistically with exponential random graph modeling. Most (96%, n = 45) of the clinics and referral sites (95%, n = 21) are connected to each other. Two clinics of the same type were 62% less likely to refer patients to the same providers as 2 clinics of different types (OR = 0.38, 95% CI = 0.29-0.50). Clinics in the same county have approximately 8 times higher odds of referring patients to the same providers compared to clinics in different counties (OR = 7.80, CI = 4.57-13.31). This study found that geographic location of resources is an important factor in rural health care providers' referral decisions and demonstrated the usefulness of network analysis for understanding rural health networks. These results can be used to guide delivery of patient care and strengthen the network by building resources that take location into account. © 2018 National Rural Health Association.

  10. Detection and diagnosis of colitis on computed tomography using deep convolutional neural networks.

    Science.gov (United States)

    Liu, Jiamin; Wang, David; Lu, Le; Wei, Zhuoshi; Kim, Lauren; Turkbey, Evrim B; Sahiner, Berkman; Petrick, Nicholas A; Summers, Ronald M

    2017-09-01

    Colitis refers to inflammation of the inner lining of the colon that is frequently associated with infection and allergic reactions. In this paper, we propose deep convolutional neural networks methods for lesion-level colitis detection and a support vector machine (SVM) classifier for patient-level colitis diagnosis on routine abdominal CT scans. The recently developed Faster Region-based Convolutional Neural Network (Faster RCNN) is utilized for lesion-level colitis detection. For each 2D slice, rectangular region proposals are generated by region proposal networks (RPN). Then, each region proposal is jointly classified and refined by a softmax classifier and bounding-box regressor. Two convolutional neural networks, eight layers of ZF net and 16 layers of VGG net are compared for colitis detection. Finally, for each patient, the detections on all 2D slices are collected and a SVM classifier is applied to develop a patient-level diagnosis. We trained and evaluated our method with 80 colitis patients and 80 normal cases using 4 × 4-fold cross validation. For lesion-level colitis detection, with ZF net, the mean of average precisions (mAP) were 48.7% and 50.9% for RCNN and Faster RCNN, respectively. The detection system achieved sensitivities of 51.4% and 54.0% at two false positives per patient for RCNN and Faster RCNN, respectively. With VGG net, Faster RCNN increased the mAP to 56.9% and increased the sensitivity to 58.4% at two false positive per patient. For patient-level colitis diagnosis, with ZF net, the average areas under the ROC curve (AUC) were 0.978 ± 0.009 and 0.984 ± 0.008 for RCNN and Faster RCNN method, respectively. The difference was not statistically significant with P = 0.18. At the optimal operating point, the RCNN method correctly identified 90.4% (72.3/80) of the colitis patients and 94.0% (75.2/80) of normal cases. The sensitivity improved to 91.6% (73.3/80) and the specificity improved to 95.0% (76.0/80) for the Faster RCNN

  11. Predictive models for pressure ulcers from intensive care unit electronic health records using Bayesian networks.

    Science.gov (United States)

    Kaewprag, Pacharmon; Newton, Cheryl; Vermillion, Brenda; Hyun, Sookyung; Huang, Kun; Machiraju, Raghu

    2017-07-05

    We develop predictive models enabling clinicians to better understand and explore patient clinical data along with risk factors for pressure ulcers in intensive care unit patients from electronic health record data. Identifying accurate risk factors of pressure ulcers is essential to determining appropriate prevention strategies; in this work we examine medication, diagnosis, and traditional Braden pressure ulcer assessment scale measurements as patient features. In order to predict pressure ulcer incidence and better understand the structure of related risk factors, we construct Bayesian networks from patient features. Bayesian network nodes (features) and edges (conditional dependencies) are simplified with statistical network techniques. Upon reviewing a network visualization of our model, our clinician collaborators were able to identify strong relationships between risk factors widely recognized as associated with pressure ulcers. We present a three-stage framework for predictive analysis of patient clinical data: 1) Developing electronic health record feature extraction functions with assistance of clinicians, 2) simplifying features, and 3) building Bayesian network predictive models. We evaluate all combinations of Bayesian network models from different search algorithms, scoring functions, prior structure initializations, and sets of features. From the EHRs of 7,717 ICU patients, we construct Bayesian network predictive models from 86 medication, diagnosis, and Braden scale features. Our model not only identifies known and suspected high PU risk factors, but also substantially increases sensitivity of the prediction - nearly three times higher comparing to logistical regression models - without sacrificing the overall accuracy. We visualize a representative model with which our clinician collaborators identify strong relationships between risk factors widely recognized as associated with pressure ulcers. Given the strong adverse effect of pressure ulcers

  12. Rwanda Health and Education Information Network (OASIS-RHEIN ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Rwanda Health and Education Information Network (OASIS-RHEIN). Partners in Health (PIH), an international nongovernmental organization, has demonstrated the effectiveness of its open source electronic medical record system (OpenMRS) in eight clinics in Rwanda. As a result, the Ministry of Health has decided to roll ...

  13. State Support: A Prerequisite for Global Health Network Effectiveness Comment on "Four Challenges that Global Health Networks Face".

    Science.gov (United States)

    Marten, Robert; Smith, Richard D

    2017-07-24

    Shiffman recently summarized lessons for network effectiveness from an impressive collection of case-studies. However, in common with most global health governance analysis in recent years, Shiffman underplays the important role of states in these global networks. As the body which decides and signs international agreements, often provides the resourcing, and is responsible for implementing initiatives all contributing to the prioritization of certain issues over others, state recognition and support is a prerequisite to enabling and determining global health networks' success. The role of states deserves greater attention, analysis and consideration. We reflect upon the underappreciated role of the state within the current discourse on global health. We present the tobacco case study to illustrate the decisive role of states in determining progress for global health networks, and highlight how states use a legitimacy loop to gain legitimacy from and provide legitimacy to global health networks. Moving forward in assessing global health networks' effectiveness, further investigating state support as a determinant of success will be critical. Understanding how global health networks and states interact and evolve to shape and support their respective interests should be a focus for future research. © 2018 The Author(s); Published by Kerman University of Medical Sciences. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

  14. EARLY DIAGNOSIS OF CRANIOSYNOSTOSIS IN INFANTS AT PRIMARY HEALTH CARE

    Directory of Open Access Journals (Sweden)

    Skoric Jasmina

    2014-12-01

    Full Text Available Craniosynostosis or premature fusion of one or more cranial sutures in infants disturbs normal brain growth. This condition causes abnormal skull configuration, increased intracranial pressure, headache, strabismus, blurred vision, blindness, psychomotor retardation. The diagnosis of craniosynostosis is very simple. Pediatricians should routinely assess neurological status and measure head circumference and anterior fontanelle. When necessary, ultrasound of CNS, X-ray and cranial CT scan can be done. When it comes to this condition, early diagnosis and surgical intervention are of utmost importance. In this paper, we have presented a case on craniosynostosis in a female infant, discovered in the third month of life during systematic review that included measurement of head circumference, palpation of anterior fontanelle and cranial sutures. The child was referred to a neurosurgeon who performed the CT scan of endocranium and confirmed the initial diagnosis of craniosynostosis. With head circumference of 40 cm and fused anterior fontanelle, the surgery was timely performed at the sixth month of life due to early diagnosis.

  15. Early diagnosis of craniosynostosis in infants at primary health care

    Directory of Open Access Journals (Sweden)

    Skoric Jasmina

    2014-12-01

    Full Text Available Craniosynostosis or premature fusion of one or more cranial sutures in infants disturbs normal brain growth. This condition causes abnormal skull configuration, increased intracranial pressure, headache, strabismus, blurred vision, blindness, psychomotor retardation. The diagnosis of craniosynostosis is very simple. Pediatricians should routinely assess neurological status and measure head circumference and anterior fontanelle. When necessary, ultrasound of CNS, X-ray and cranial CT scan can be done. When it comes to this condition, early diagnosis and surgical intervention are of utmost importance. In this paper, we have presented a case on craniosynostosis in a female infant, discovered in the third month of life during systematic review that included measurement of head circumference, palpation of anterior fontanelle and cranial sutures. The child was referred to a neurosurgeon who performed the CT scan of endocranium and confirmed the initial diagnosis of craniosynostosis. With head circumference of 40 cm and fused anterior fontanelle, the surgery was timely performed at the sixth month of life due to early diagnosis.

  16. Diagnosis of vaginal infection in pregnancy | Botha | Health SA ...

    African Journals Online (AJOL)

    In 51,4% of the cases the diagnosis differed. Candida albicans infection was diagnosed by 10 respondents, while 3 actually had Trichomonas vaginalis infection and seven had Gardnerella vaginalis infection. Trichomonas vaginalis infection was diagnosed in 26 cases, while 15 were actually due to Candida albicans and ...

  17. Implications of network structure on public health collaboratives.

    Science.gov (United States)

    Retrum, Jessica H; Chapman, Carrie L; Varda, Danielle M

    2013-10-01

    Interorganizational collaboration is an essential function of public health agencies. These partnerships form social networks that involve diverse types of partners and varying levels of interaction. Such collaborations are widely accepted and encouraged, yet very little comparative research exists on how public health partnerships develop and evolve, specifically in terms of how subsequent network structures are linked to outcomes. A systems science approach, that is, one that considers the interdependencies and nested features of networks, provides the appropriate methods to examine the complex nature of these networks. Applying Mays and Scutchfields's categorization of "structural signatures" (breadth, density, and centralization), this research examines how network structure influences the outcomes of public health collaboratives. Secondary data from the Program to Analyze, Record, and Track Networks to Enhance Relationships (www.partnertool.net) data set are analyzed. This data set consists of dyadic (N = 12,355), organizational (N = 2,486), and whole network (N = 99) data from public health collaborations around the United States. Network data are used to calculate structural signatures and weighted least squares regression is used to examine how network structures can predict selected intermediary outcomes (resource contributions, overall value and trust rankings, and outcomes) in public health collaboratives. Our findings suggest that network structure may have an influence on collaborative-related outcomes. The structural signature that had the most significant relationship to outcomes was density, with higher density indicating more positive outcomes. Also significant was the finding that more breadth creates new challenges such as difficulty in reaching consensus and creating ties with other members. However, assumptions that these structural components lead to improved outcomes for public health collaboratives may be slightly premature. Implications of

  18. Computer-Aided Diagnosis Based on Convolutional Neural Network System for Colorectal Polyp Classification: Preliminary Experience.

    Science.gov (United States)

    Komeda, Yoriaki; Handa, Hisashi; Watanabe, Tomohiro; Nomura, Takanobu; Kitahashi, Misaki; Sakurai, Toshiharu; Okamoto, Ayana; Minami, Tomohiro; Kono, Masashi; Arizumi, Tadaaki; Takenaka, Mamoru; Hagiwara, Satoru; Matsui, Shigenaga; Nishida, Naoshi; Kashida, Hiroshi; Kudo, Masatoshi

    2017-01-01

    Computer-aided diagnosis (CAD) is becoming a next-generation tool for the diagnosis of human disease. CAD for colon polyps has been suggested as a particularly useful tool for trainee colonoscopists, as the use of a CAD system avoids the complications associated with endoscopic resections. In addition to conventional CAD, a convolutional neural network (CNN) system utilizing artificial intelligence (AI) has been developing rapidly over the past 5 years. We attempted to generate a unique CNN-CAD system with an AI function that studied endoscopic images extracted from movies obtained with colonoscopes used in routine examinations. Here, we report our preliminary results of this novel CNN-CAD system for the diagnosis of colon polyps. A total of 1,200 images from cases of colonoscopy performed between January 2010 and December 2016 at Kindai University Hospital were used. These images were extracted from the video of actual endoscopic examinations. Additional video images from 10 cases of unlearned processes were retrospectively assessed in a pilot study. They were simply diagnosed as either an adenomatous or nonadenomatous polyp. The number of images used by AI to learn to distinguish adenomatous from nonadenomatous was 1,200:600. These images were extracted from the videos of actual endoscopic examinations. The size of each image was adjusted to 256 × 256 pixels. A 10-hold cross-validation was carried out. The accuracy of the 10-hold cross-validation is 0.751, where the accuracy is the ratio of the number of correct answers over the number of all the answers produced by the CNN. The decisions by the CNN were correct in 7 of 10 cases. A CNN-CAD system using routine colonoscopy might be useful for the rapid diagnosis of colorectal polyp classification. Further prospective studies in an in vivo setting are required to confirm the effectiveness of a CNN-CAD system in routine colonoscopy. © 2017 S. Karger AG, Basel.

  19. Artificial Neural Network Application in the Diagnosis of Disease Conditions with Liver Ultrasound Images

    Directory of Open Access Journals (Sweden)

    Karthik Kalyan

    2014-01-01

    Full Text Available The preliminary study presented within this paper shows a comparative study of various texture features extracted from liver ultrasonic images by employing Multilayer Perceptron (MLP, a type of artificial neural network, to study the presence of disease conditions. An ultrasound (US image shows echo-texture patterns, which defines the organ characteristics. Ultrasound images of liver disease conditions such as “fatty liver,” “cirrhosis,” and “hepatomegaly” produce distinctive echo patterns. However, various ultrasound imaging artifacts and speckle noise make these echo-texture patterns difficult to identify and often hard to distinguish visually. Here, based on the extracted features from the ultrasonic images, we employed an artificial neural network for the diagnosis of disease conditions in liver and finding of the best classifier that distinguishes between abnormal and normal conditions of the liver. Comparison of the overall performance of all the feature classifiers concluded that “mixed feature set” is the best feature set. It showed an excellent rate of accuracy for the training data set. The gray level run length matrix (GLRLM feature shows better results when the network was tested against unknown data.

  20. Artificial Neural Network Application in the Diagnosis of Disease Conditions with Liver Ultrasound Images

    Science.gov (United States)

    Lele, Ramachandra Dattatraya; Joshi, Mukund; Chowdhary, Abhay

    2014-01-01

    The preliminary study presented within this paper shows a comparative study of various texture features extracted from liver ultrasonic images by employing Multilayer Perceptron (MLP), a type of artificial neural network, to study the presence of disease conditions. An ultrasound (US) image shows echo-texture patterns, which defines the organ characteristics. Ultrasound images of liver disease conditions such as “fatty liver,” “cirrhosis,” and “hepatomegaly” produce distinctive echo patterns. However, various ultrasound imaging artifacts and speckle noise make these echo-texture patterns difficult to identify and often hard to distinguish visually. Here, based on the extracted features from the ultrasonic images, we employed an artificial neural network for the diagnosis of disease conditions in liver and finding of the best classifier that distinguishes between abnormal and normal conditions of the liver. Comparison of the overall performance of all the feature classifiers concluded that “mixed feature set” is the best feature set. It showed an excellent rate of accuracy for the training data set. The gray level run length matrix (GLRLM) feature shows better results when the network was tested against unknown data. PMID:25332717

  1. Societal costs and patients' experience of health inequities before and after diagnosis of psoriatic arthritis

    DEFF Research Database (Denmark)

    Kristensen, Lars Erik; Jørgensen, Tanja S.; Christensen, Robin

    2017-01-01

    . 49) compared with GPC subjects. The RR increased to 1. 60 (95% CI 1. 49 to 1. 72) at the time of diagnosis and was 2. 69 (95% CI 2. 40 to 3. 02) 10â years after diagnosis, where 21. 8% of the patients with PsA received disability pension. Conclusions Our findings are suggestive of health inequity...

  2. Digital Networked Information Society and Public Health: Problems and Promises of Networked Health Communication of Lay Publics.

    Science.gov (United States)

    Kim, Jeong-Nam

    2018-01-01

    This special issue of Health Communication compiles 10 articles to laud the promise and yet confront the problems in the digital networked information society related to public health. We present this anthology of symphony and cacophony of lay individuals' communicative actions in a digital networked information society. The collection of problems and promise of the new digital world may be a cornerstone joining two worlds-pre- and postdigital network society-and we hope this special issue will help better shape our future states of public health.

  3. NETWORKS OF HEALTH CARE: A CHALLENGE TO SUS MANAGEMENT

    Directory of Open Access Journals (Sweden)

    Camila Dubow

    2013-09-01

    Full Text Available The article proposes a critical reflection, based on national law, scholarly, scientific, on the current development of Networks of Health Care, as a strategy for strengthening the Single Health System (SUS. Are weighted inefficiency of traditional ways of organizing care and management, the challenge of Network Health Care for comprehensive care and management mechanisms used in this process. The work provides subsidies for the care practices and health management are reflected, pointing strategies that result in disruptions of paradigms through a refocusing of attention in existing models. For networks of health care can be consolidated, is fundamental to political sensitivity of health managers with a commitment to build a new model of care, through the struggle to consolidate the SUS and the realization of the principles of universality, comprehensiveness and equity.

  4. Uganda Health Information Network (UHIN) - Phase IV | IDRC ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    ... reports on drug supplies and use, and continuing education materials. This phase aims to fully integrate the Network into the Ministry of Health district and national ... IWRA/IDRC webinar on climate change and adaptive water management.

  5. Air Quality Measures on the National Environmental Health Tracking Network

    Data.gov (United States)

    U.S. Department of Health & Human Services — The Environmental Protection Agency (EPA) provides air pollution data about ozone and particulate matter (PM2.5) to CDC for the Tracking Network. The EPA maintains a...

  6. Mother and Child Health International Research Network | IDRC ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Building a virtual global research institute to support maternal and child health ... Learning Initiatives for Network Economies in Asia (LIRNEasia) : Building ... to information and communication technology (ICT) initiatives through its global ...

  7. From diagnosis to health: a cross-cultural interview study with immigrants from Somalia.

    Science.gov (United States)

    Wallin, Anne-Marie; Ahlström, Gerd

    2010-06-01

    Being diagnosed as having a chronic disease gives rise to emotions. Beliefs about health are culturally constructed and affect people's decisions regarding treatment. No studies have been reported that focus on the health beliefs of immigrants of Somalian origin with diabetes and how these people experiences the diagnosis. Therefore the aim of the present study was to investigate how immigrants from Somalia living in Sweden experienced receiving the diagnosis and describe their beliefs about health. The sample consisted of 19 adults with diabetes born in Somalia and now living in Sweden who were interviewed with the aid of an interpreter. The interviews were subjected to qualitative content analysis. From the analysis of what the participants said about their experiences of the diagnosis there emerged three themes: 'Existential brooding', 'Avoiding the diagnosis' and 'Accepting what is fated'. Three themes also emerged from the analysis of what they said about beliefs about health: 'Health as absence of disease', 'Health as general well-being' and 'Fated by a higher power'. A major finding was that women when they communicated their experiences regarding the diagnosis and health beliefs made more use of supernatural beliefs than men did. The participants, irrespective of gender, did not immediately respond with shock or other strong emotion when they received the diagnosis. The study provides health-care staff with knowledge concerning a minority group's experiences of being diagnosed as having diabetes and their beliefs about health. The findings indicate that men and women differ in how they experiences the diagnosis and how they described their health beliefs. The quality improvement of health education and nursing for patients with diabetes calls for consideration of the variation of beliefs related to cultural background and gender.

  8. Students with Dual Diagnosis: Can School-Based Mental Health Services Play a Role?

    Science.gov (United States)

    Lambros, Katina; Kraemer, Bonnie; Wager, James Derek; Culver, Shirley; Angulo, Aidee; Saragosa, Marie

    2016-01-01

    This article describes and investigates initial findings from the Esperanza Mental Health Services (EMHS) Program, which is an intensive outpatient program that provides individual and group mental health services for students with "dual diagnosis" or developmental disabilities and co-occurring mental health problems. Previous research…

  9. The discourse of health managers on aspects related to the delay in tuberculosis diagnosis

    Directory of Open Access Journals (Sweden)

    Lenilde Duarte de Sa

    2013-10-01

    Full Text Available The aim of this study was to analyze the discourse of health managers on aspects related to delay in tuberculosis diagnosis. This was a qualitative research study, conducted with 16 Family Health Unit managers. The empirical data were obtained through semi-structured interviews. The analysis was based on the theoretical framework of the French school of discourse analysis. According to the managers’ statements, the delay in tuberculosis diagnosis is related to patient and health service aspects. As for patient aspects, managers report fear, prejudice and lack of information as factors that may promote a delayed diagnosis. Regarding health service aspects, structural problems and lack of professional skills were reported. The discourse of managers should be considered to qualify tuberculosis control actions and to prevent delays in diagnosis.

  10. Dissemination of health information through social networks: twitter and antibiotics.

    Science.gov (United States)

    Scanfeld, Daniel; Scanfeld, Vanessa; Larson, Elaine L

    2010-04-01

    This study reviewed Twitter status updates mentioning "antibiotic(s)" to determine overarching categories and explore evidence of misunderstanding or misuse of antibiotics. One thousand Twitter status updates mentioning antibiotic(s) were randomly selected for content analysis and categorization. To explore cases of potential misunderstanding or misuse, these status updates were mined for co-occurrence of the following terms: "cold + antibiotic(s)," "extra + antibiotic(s)," "flu + antibiotic(s)," "leftover + antibiotic(s)," and "share + antibiotic(s)" and reviewed to confirm evidence of misuse or misunderstanding. Of the 1000 status updates, 971 were categorized into 11 groups: general use (n = 289), advice/information (n = 157), side effects/negative reactions (n = 113), diagnosis (n = 102), resistance (n = 92), misunderstanding and/or misuse (n = 55), positive reactions (n = 48), animals (n = 46), other (n = 42), wanting/needing (n = 19), and cost (n = 8). Cases of misunderstanding or abuse were identified for the following combinations: "flu + antibiotic(s)" (n = 345), "cold + antibiotic(s)" (n = 302), "leftover + antibiotic(s)" (n = 23), "share + antibiotic(s)" (n = 10), and "extra + antibiotic(s)" (n = 7). Social media sites offer means of health information sharing. Further study is warranted to explore how such networks may provide a venue to identify misuse or misunderstanding of antibiotics, promote positive behavior change, disseminate valid information, and explore how such tools can be used to gather real-time health data. 2010 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Mosby, Inc. All rights reserved.

  11. Integration of temporal and spatial properties of dynamic connectivity networks for automatic diagnosis of brain disease.

    Science.gov (United States)

    Jie, Biao; Liu, Mingxia; Shen, Dinggang

    2018-07-01

    Functional connectivity networks (FCNs) using resting-state functional magnetic resonance imaging (rs-fMRI) have been applied to the analysis and diagnosis of brain disease, such as Alzheimer's disease (AD) and its prodrome, i.e., mild cognitive impairment (MCI). Different from conventional studies focusing on static descriptions on functional connectivity (FC) between brain regions in rs-fMRI, recent studies have resorted to dynamic connectivity networks (DCNs) to characterize the dynamic changes of FC, since dynamic changes of FC may indicate changes in macroscopic neural activity patterns in cognitive and behavioral aspects. However, most of the existing studies only investigate the temporal properties of DCNs (e.g., temporal variability of FC between specific brain regions), ignoring the important spatial properties of the network (e.g., spatial variability of FC associated with a specific brain region). Also, emerging evidence on FCNs has suggested that, besides temporal variability, there is significant spatial variability of activity foci over time. Hence, integrating both temporal and spatial properties of DCNs can intuitively promote the performance of connectivity-network-based learning methods. In this paper, we first define a new measure to characterize the spatial variability of DCNs, and then propose a novel learning framework to integrate both temporal and spatial variabilities of DCNs for automatic brain disease diagnosis. Specifically, we first construct DCNs from the rs-fMRI time series at successive non-overlapping time windows. Then, we characterize the spatial variability of a specific brain region by computing the correlation of functional sequences (i.e., the changing profile of FC between a pair of brain regions within all time windows) associated with this region. Furthermore, we extract both temporal variabilities and spatial variabilities from DCNs as features, and integrate them for classification by using manifold regularized multi

  12. Narrow Networks On The Health Insurance Marketplaces: Prevalence, Pricing, And The Cost Of Network Breadth.

    Science.gov (United States)

    Dafny, Leemore S; Hendel, Igal; Marone, Victoria; Ody, Christopher

    2017-09-01

    Anecdotal reports and systematic research highlight the prevalence of narrow-network plans on the Affordable Care Act's health insurance Marketplaces. At the same time, Marketplace premiums in the period 2014-16 were much lower than projected by the Congressional Budget Office in 2009. Using detailed data on the breadth of both hospital and physician networks, we studied the prevalence of narrow networks and quantified the association between network breadth and premiums. Controlling for many potentially confounding factors, we found that a plan with narrow physician and hospital networks was 16 percent cheaper than a plan with broad networks for both, and that narrowing the breadth of just one type of network was associated with a 6-9 percent decrease in premiums. Narrow-network plans also have a sizable impact on federal outlays, as they depress the premium of the second-lowest-price silver plan, to which subsidy amounts are linked. Holding all else constant, we estimate that federal subsidies would have been 10.8 percent higher in 2014 had Marketplaces required all plans to offer broad provider networks. Narrow networks are a promising source of potential savings for other segments of the commercial insurance market. Project HOPE—The People-to-People Health Foundation, Inc.

  13. An adaptive deep convolutional neural network for rolling bearing fault diagnosis

    International Nuclear Information System (INIS)

    Fuan, Wang; Hongkai, Jiang; Haidong, Shao; Wenjing, Duan; Shuaipeng, Wu

    2017-01-01

    The working conditions of rolling bearings usually is very complex, which makes it difficult to diagnose rolling bearing faults. In this paper, a novel method called the adaptive deep convolutional neural network (CNN) is proposed for rolling bearing fault diagnosis. Firstly, to get rid of manual feature extraction, the deep CNN model is initialized for automatic feature learning. Secondly, to adapt to different signal characteristics, the main parameters of the deep CNN model are determined with a particle swarm optimization method. Thirdly, to evaluate the feature learning ability of the proposed method, t-distributed stochastic neighbor embedding (t-SNE) is further adopted to visualize the hierarchical feature learning process. The proposed method is applied to diagnose rolling bearing faults, and the results confirm that the proposed method is more effective and robust than other intelligent methods. (paper)

  14. An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network.

    Science.gov (United States)

    Sun, Weifang; Yao, Bin; Zeng, Nianyin; Chen, Binqiang; He, Yuchao; Cao, Xincheng; He, Wangpeng

    2017-07-12

    As a typical example of large and complex mechanical systems, rotating machinery is prone to diversified sorts of mechanical faults. Among these faults, one of the prominent causes of malfunction is generated in gear transmission chains. Although they can be collected via vibration signals, the fault signatures are always submerged in overwhelming interfering contents. Therefore, identifying the critical fault's characteristic signal is far from an easy task. In order to improve the recognition accuracy of a fault's characteristic signal, a novel intelligent fault diagnosis method is presented. In this method, a dual-tree complex wavelet transform (DTCWT) is employed to acquire the multiscale signal's features. In addition, a convolutional neural network (CNN) approach is utilized to automatically recognise a fault feature from the multiscale signal features. The experiment results of the recognition for gear faults show the feasibility and effectiveness of the proposed method, especially in the gear's weak fault features.

  15. An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network

    Science.gov (United States)

    Sun, Weifang; Yao, Bin; Zeng, Nianyin; He, Yuchao; Cao, Xincheng; He, Wangpeng

    2017-01-01

    As a typical example of large and complex mechanical systems, rotating machinery is prone to diversified sorts of mechanical faults. Among these faults, one of the prominent causes of malfunction is generated in gear transmission chains. Although they can be collected via vibration signals, the fault signatures are always submerged in overwhelming interfering contents. Therefore, identifying the critical fault’s characteristic signal is far from an easy task. In order to improve the recognition accuracy of a fault’s characteristic signal, a novel intelligent fault diagnosis method is presented. In this method, a dual-tree complex wavelet transform (DTCWT) is employed to acquire the multiscale signal’s features. In addition, a convolutional neural network (CNN) approach is utilized to automatically recognise a fault feature from the multiscale signal features. The experiment results of the recognition for gear faults show the feasibility and effectiveness of the proposed method, especially in the gear’s weak fault features. PMID:28773148

  16. Quebec mental health services networks: models and implementation

    Directory of Open Access Journals (Sweden)

    Marie-Josée Fleury

    2005-06-01

    Full Text Available Purpose: In the transformation of health care systems, the introduction of integrated service networks is considered to be one of the main solutions for enhancing efficiency. In the last few years, a wealth of literature has emerged on the topic of services integration. However, the question of how integrated service networks should be modelled to suit different implementation contexts has barely been touched. To fill that gap, this article presents four models for the organization of mental health integrated networks. Data sources: The proposed models are drawn from three recently published studies on mental health integrated services in the province of Quebec (Canada with the author as principal investigator. Description: Following an explanation of the concept of integrated service network and a description of the Quebec context for mental health networks, the models, applicable in all settings: rural, urban or semi-urban, and metropolitan, and summarized in four figures, are presented. Discussion and conclusion: To apply the models successfully, the necessity of rallying all the actors of a system, from the strategic, tactical and operational levels, according to the type of integration involved: functional/administrative, clinical and physician-system is highlighted. The importance of formalizing activities among organizations and actors in a network and reinforcing the governing mechanisms at the local level is also underlined. Finally, a number of integration strategies and key conditions of success to operationalize integrated service networks are suggested.

  17. Health Promoting Hospitals – Assessing developments in the network

    Directory of Open Access Journals (Sweden)

    Jürgen M. Pelikan

    2007-12-01

    Full Text Available Hospitals are specific organizational settings for health promotion efforts. As health care institutions they are already oriented at health, or better at ill health, but with a rather limited focus on health outcomes for patients. Therefore, the Ottawa Charter explicitly asks for the reorientation of health services. And, hospitals also have considerable health effects for other stakeholder populations. This specific potential and challenge has been taken up by the WHO network of Health Promoting Hospitals (HPH, in the last two decades. Based on available literature the article relates the HPH concept to a more general paradigm of health promoting organizational settings; reconstructs the developmental phases of the international WHO HPH Network; elaborates on its concept development and implementation experiences, and discusses its rather limited investments in evaluation studies and the few assessments from outside. HPH has developed a convincing comprehensive concept by demonstration projects, using systematically action and evaluation research. To a lesser degree, the same holds true for its developments of health promotion policies for selected vulnerable groups and linking HPH to quality methodology. But there is no systematic evaluation of health promotion in and by hospitals, especially for the networks and member hospitals of HPH. Even if much of the relevant evidence for HPH comes and will have to come from more general clinical epidemiological, health promotion, quality, organizational and management research, there is need for specific HPH evaluation research, to better utilize, what can be learned from the social experiment of HPH.

  18. State Support: A Prerequisite for Global Health Network Effectiveness

    Science.gov (United States)

    Marten, Robert; Smith, Richard D.

    2018-01-01

    Shiffman recently summarized lessons for network effectiveness from an impressive collection of case-studies. However, in common with most global health governance analysis in recent years, Shiffman underplays the important role of states in these global networks. As the body which decides and signs international agreements, often provides the resourcing, and is responsible for implementing initiatives all contributing to the prioritization of certain issues over others, state recognition and support is a prerequisite to enabling and determining global health networks’ success. The role of states deserves greater attention, analysis and consideration. We reflect upon the underappreciated role of the state within the current discourse on global health. We present the tobacco case study to illustrate the decisive role of states in determining progress for global health networks, and highlight how states use a legitimacy loop to gain legitimacy from and provide legitimacy to global health networks. Moving forward in assessing global health networks’ effectiveness, further investigating state support as a determinant of success will be critical. Understanding how global health networks and states interact and evolve to shape and support their respective interests should be a focus for future research. PMID:29524958

  19. [Overcoming the limitations of the descriptive and categorical approaches in psychiatric diagnosis: a proposal based on Bayesian networks].

    Science.gov (United States)

    Sorias, Soli

    2015-01-01

    Efforts to overcome the problems of descriptive and categorical approaches have not yielded results. In the present article, psychiatric diagnosis using Bayesian networks is proposed. Instead of a yes/no decision, Bayesian networks give the probability of diagnostic category inclusion, thereby yielding both a graded, i.e., dimensional diagnosis, and a value of the certainty of the diagnosis. With the use of Bayesian networks in the diagnosis of mental disorders, information about etiology, associated features, treatment outcome, and laboratory results may be used in addition to clinical signs and symptoms, with each of these factors contributing proportionally to their own specificity and sensitivity. Furthermore, a diagnosis (albeit one with a lower probability) can be made even with incomplete, uncertain, or partially erroneous information, and patients whose symptoms are below the diagnostic threshold can be evaluated. Lastly, there is no need of NOS or "unspecified" categories, and comorbid disorders become different dimensions of the diagnostic evaluation. Bayesian diagnoses allow the preservation of current categories and assessment methods, and may be used concurrently with criteria-based diagnoses. Users need not put in extra effort except to collect more comprehensive information. Unlike the Research Domain Criteria (RDoC) project, the Bayesian approach neither increases the diagnostic validity of existing categories nor explains the pathophysiological mechanisms of mental disorders. It, however, can be readily integrated to present classification systems. Therefore, the Bayesian approach may be an intermediate phase between criteria-based diagnosis and the RDoC ideal.

  20. Challenges of Health Games in the Social Network Environment.

    Science.gov (United States)

    Paredes, Hugo; Pinho, Anabela; Zagalo, Nelson

    2012-04-01

    Virtual communities and their benefits have been widely exploited to support patients, caregivers, families, and healthcare providers. The complexity of the social organization evolved the concept of virtual community to social networks, exploring the establishment of ties and relations between people. These technological platforms provide a way to keep up with one's connections network, through a set of communication and interaction tools. Games, as social interactive technologies, have great potential, ensuring a supportive community and thereby reducing social isolation. Serious social health games bring forward several research challenges. This article examines the potential benefits of the triad "health-serious games-social networks" and discusses some research challenges and opportunities of the liaison of serious health games and social networks.

  1. Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Bach Phi Duong

    2018-04-01

    Full Text Available The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs. The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance.

  2. Combined expert system/neural networks method for process fault diagnosis

    Science.gov (United States)

    Reifman, Jaques; Wei, Thomas Y. C.

    1995-01-01

    A two-level hierarchical approach for process fault diagnosis is an operating system employs a function-oriented approach at a first level and a component characteristic-oriented approach at a second level, where the decision-making procedure is structured in order of decreasing intelligence with increasing precision. At the first level, the diagnostic method is general and has knowledge of the overall process including a wide variety of plant transients and the functional behavior of the process components. An expert system classifies malfunctions by function to narrow the diagnostic focus to a particular set of possible faulty components that could be responsible for the detected functional misbehavior of the operating system. At the second level, the diagnostic method limits its scope to component malfunctions, using more detailed knowledge of component characteristics. Trained artificial neural networks are used to further narrow the diagnosis and to uniquely identify the faulty component by classifying the abnormal condition data as a failure of one of the hypothesized components through component characteristics. Once an anomaly is detected, the hierarchical structure is used to successively narrow the diagnostic focus from a function misbehavior, i.e., a function oriented approach, until the fault can be determined, i.e., a component characteristic-oriented approach.

  3. Combined expert system/neural networks method for process fault diagnosis

    Science.gov (United States)

    Reifman, J.; Wei, T.Y.C.

    1995-08-15

    A two-level hierarchical approach for process fault diagnosis of an operating system employs a function-oriented approach at a first level and a component characteristic-oriented approach at a second level, where the decision-making procedure is structured in order of decreasing intelligence with increasing precision. At the first level, the diagnostic method is general and has knowledge of the overall process including a wide variety of plant transients and the functional behavior of the process components. An expert system classifies malfunctions by function to narrow the diagnostic focus to a particular set of possible faulty components that could be responsible for the detected functional misbehavior of the operating system. At the second level, the diagnostic method limits its scope to component malfunctions, using more detailed knowledge of component characteristics. Trained artificial neural networks are used to further narrow the diagnosis and to uniquely identify the faulty component by classifying the abnormal condition data as a failure of one of the hypothesized components through component characteristics. Once an anomaly is detected, the hierarchical structure is used to successively narrow the diagnostic focus from a function misbehavior, i.e., a function oriented approach, until the fault can be determined, i.e., a component characteristic-oriented approach. 9 figs.

  4. [Computer aided diagnosis model for lung tumor based on ensemble convolutional neural network].

    Science.gov (United States)

    Wang, Yuanyuan; Zhou, Tao; Lu, Huiling; Wu, Cuiying; Yang, Pengfei

    2017-08-01

    The convolutional neural network (CNN) could be used on computer-aided diagnosis of lung tumor with positron emission tomography (PET)/computed tomography (CT), which can provide accurate quantitative analysis to compensate for visual inertia and defects in gray-scale sensitivity, and help doctors diagnose accurately. Firstly, parameter migration method is used to build three CNNs (CT-CNN, PET-CNN, and PET/CT-CNN) for lung tumor recognition in CT, PET, and PET/CT image, respectively. Then, we aimed at CT-CNN to obtain the appropriate model parameters for CNN training through analysis the influence of model parameters such as epochs, batchsize and image scale on recognition rate and training time. Finally, three single CNNs are used to construct ensemble CNN, and then lung tumor PET/CT recognition was completed through relative majority vote method and the performance between ensemble CNN and single CNN was compared. The experiment results show that the ensemble CNN is better than single CNN on computer-aided diagnosis of lung tumor.

  5. A comparative study of breast cancer diagnosis based on neural network ensemble via improved training algorithms.

    Science.gov (United States)

    Azami, Hamed; Escudero, Javier

    2015-08-01

    Breast cancer is one of the most common types of cancer in women all over the world. Early diagnosis of this kind of cancer can significantly increase the chances of long-term survival. Since diagnosis of breast cancer is a complex problem, neural network (NN) approaches have been used as a promising solution. Considering the low speed of the back-propagation (BP) algorithm to train a feed-forward NN, we consider a number of improved NN trainings for the Wisconsin breast cancer dataset: BP with momentum, BP with adaptive learning rate, BP with adaptive learning rate and momentum, Polak-Ribikre conjugate gradient algorithm (CGA), Fletcher-Reeves CGA, Powell-Beale CGA, scaled CGA, resilient BP (RBP), one-step secant and quasi-Newton methods. An NN ensemble, which is a learning paradigm to combine a number of NN outputs, is used to improve the accuracy of the classification task. Results demonstrate that NN ensemble-based classification methods have better performance than NN-based algorithms. The highest overall average accuracy is 97.68% obtained by NN ensemble trained by RBP for 50%-50% training-test evaluation method.

  6. Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis.

    Science.gov (United States)

    Duong, Bach Phi; Kim, Jong-Myon

    2018-04-07

    The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC) method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs). The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance.

  7. Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis

    Science.gov (United States)

    Kim, Jong-Myon

    2018-01-01

    The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC) method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs). The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance. PMID:29642466

  8. Networked Biomedical System for Ubiquitous Health Monitoring

    Directory of Open Access Journals (Sweden)

    Arjan Durresi

    2008-01-01

    Full Text Available We propose a distributed system that enables global and ubiquitous health monitoring of patients. The biomedical data will be collected by wearable health diagnostic devices, which will include various types of sensors and will be transmitted towards the corresponding Health Monitoring Centers. The permanent medical data of patients will be kept in the corresponding Home Data Bases, while the measured biomedical data will be sent to the Visitor Health Monitor Center and Visitor Data Base that serves the area of present location of the patient. By combining the measured biomedical data and the permanent medical data, Health Medical Centers will be able to coordinate the needed actions and help the local medical teams to make quickly the best decisions that could be crucial for the patient health, and that can reduce the cost of health service.

  9. Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network.

    Science.gov (United States)

    Jiang, Jiewei; Liu, Xiyang; Zhang, Kai; Long, Erping; Wang, Liming; Li, Wangting; Liu, Lin; Wang, Shuai; Zhu, Mingmin; Cui, Jiangtao; Liu, Zhenzhen; Lin, Zhuoling; Li, Xiaoyan; Chen, Jingjing; Cao, Qianzhong; Li, Jing; Wu, Xiaohang; Wang, Dongni; Wang, Jinghui; Lin, Haotian

    2017-11-21

    Ocular images play an essential role in ophthalmological diagnoses. Having an imbalanced dataset is an inevitable issue in automated ocular diseases diagnosis; the scarcity of positive samples always tends to result in the misdiagnosis of severe patients during the classification task. Exploring an effective computer-aided diagnostic method to deal with imbalanced ophthalmological dataset is crucial. In this paper, we develop an effective cost-sensitive deep residual convolutional neural network (CS-ResCNN) classifier to diagnose ophthalmic diseases using retro-illumination images. First, the regions of interest (crystalline lens) are automatically identified via twice-applied Canny detection and Hough transformation. Then, the localized zones are fed into the CS-ResCNN to extract high-level features for subsequent use in automatic diagnosis. Second, the impacts of cost factors on the CS-ResCNN are further analyzed using a grid-search procedure to verify that our proposed system is robust and efficient. Qualitative analyses and quantitative experimental results demonstrate that our proposed method outperforms other conventional approaches and offers exceptional mean accuracy (92.24%), specificity (93.19%), sensitivity (89.66%) and AUC (97.11%) results. Moreover, the sensitivity of the CS-ResCNN is enhanced by over 13.6% compared to the native CNN method. Our study provides a practical strategy for addressing imbalanced ophthalmological datasets and has the potential to be applied to other medical images. The developed and deployed CS-ResCNN could serve as computer-aided diagnosis software for ophthalmologists in clinical application.

  10. Artificial neural network application for space station power system fault diagnosis

    Science.gov (United States)

    Momoh, James A.; Oliver, Walter E.; Dias, Lakshman G.

    1995-01-01

    This study presents a methodology for fault diagnosis using a Two-Stage Artificial Neural Network Clustering Algorithm. Previously, SPICE models of a 5-bus DC power distribution system with assumed constant output power during contingencies from the DDCU were used to evaluate the ANN's fault diagnosis capabilities. This on-going study uses EMTP models of the components (distribution lines, SPDU, TPDU, loads) and power sources (DDCU) of Space Station Alpha's electrical Power Distribution System as a basis for the ANN fault diagnostic tool. The results from the two studies are contrasted. In the event of a major fault, ground controllers need the ability to identify the type of fault, isolate the fault to the orbital replaceable unit level and provide the necessary information for the power management expert system to optimally determine a degraded-mode load schedule. To accomplish these goals, the electrical power distribution system's architecture can be subdivided into three major classes: DC-DC converter to loads, DC Switching Unit (DCSU) to Main bus Switching Unit (MBSU), and Power Sources to DCSU. Each class which has its own electrical characteristics and operations, requires a unique fault analysis philosophy. This study identifies these philosophies as Riddles 1, 2 and 3 respectively. The results of the on-going study addresses Riddle-1. It is concluded in this study that the combination of the EMTP models of the DDCU, distribution cables and electrical loads yields a more accurate model of the behavior and in addition yielded more accurate fault diagnosis using ANN versus the results obtained with the SPICE models.

  11. Using Social Network Analysis to Assess Mentorship and Collaboration in a Public Health Network.

    Science.gov (United States)

    Petrescu-Prahova, Miruna; Belza, Basia; Leith, Katherine; Allen, Peg; Coe, Norma B; Anderson, Lynda A

    2015-08-20

    Addressing chronic disease burden requires the creation of collaborative networks to promote systemic changes and engage stakeholders. Although many such networks exist, they are rarely assessed with tools that account for their complexity. This study examined the structure of mentorship and collaboration relationships among members of the Healthy Aging Research Network (HAN) using social network analysis (SNA). We invited 97 HAN members and partners to complete an online social network survey that included closed-ended questions about HAN-specific mentorship and collaboration during the previous 12 months. Collaboration was measured by examining the activity of the network on 6 types of products: published articles, in-progress manuscripts, grant applications, tools, research projects, and presentations. We computed network-level measures such as density, number of components, and centralization to assess the cohesiveness of the network. Sixty-three respondents completed the survey (response rate, 65%). Responses, which included information about collaboration with nonrespondents, suggested that 74% of HAN members were connected through mentorship ties and that all 97 members were connected through at least one form of collaboration. Mentorship and collaboration ties were present both within and across boundaries of HAN member organizations. SNA of public health collaborative networks provides understanding about the structure of relationships that are formed as a result of participation in network activities. This approach may offer members and funders a way to assess the impact of such networks that goes beyond simply measuring products and participation at the individual level.

  12. Laboratory testing improves diagnosis and treatment outcomes in primary health care facilities

    Directory of Open Access Journals (Sweden)

    Jane Y. Carter

    2012-10-01

    Setting: Six rural health centres in Kenya. Design: Cross-sectional study to observe change in diagnosis and treatment made by clinical officers after laboratory testing in outpatients attending six rural health centres in Kenya. Subject: The diagnosis and treatment of 1134 patients attending outpatient services in six rural health centres were compared before and after basic laboratory testing. Essential clinical diagnostic equipment and laboratory tests were established at each health centre. Clinical officers and laboratory technicians received on-site refresher training in good diagnostic practices and laboratory procedures before the study began. Results: Laboratory tests were ordered on 704 (62.1% patients. Diagnosis and treatment were changed in 45% of tested patients who returned with laboratory results (21% of all patients attending the clinics. 166 (23.5% patients did not return to the clinician for a final diagnosis and management decision after laboratory testing. Blood slide examination for malaria parasites, wet preparations, urine microscopy and stool microscopy resulted in most changes to diagnosis. There was no significant change in drug costs after laboratory testing. The greatest changes in numbers of recorded diseases following laboratory testing was for intestinal worms (53% and malaria (21%. Conclusion: Effective use of basic laboratory tests at primary health care level significantly improves diagnosis and patient treatment. Use of laboratory testing can be readily incorporated into routine clinical practice. On-site refresher training is an effective means of improving the quality of patient care and communication between clinical and laboratory staff.

  13. The European network of Biosafety-Level-4 laboratories: enhancing European preparedness for new health threats.

    Science.gov (United States)

    Nisii, C; Castilletti, C; Di Caro, A; Capobianchi, M R; Brown, D; Lloyd, G; Gunther, S; Lundkvist, A; Pletschette, M; Ippolito, G

    2009-08-01

    Emerging and re-emerging infections and possible bioterrorism acts will continue to challenge both the medical community and civilian populations worldwide, urging health authorities to respond rapidly and effectively. Established in 2005, the European Community (EC)-funded European Network of Biosafety-Level-4 laboratories (Euronet-P4), which brings together the laboratories in Porton Down, London, Hamburg, Marburg, Solna, Lyon and Rome, seeks to increase international collaboration in the areas of high containment laboratory biosafety and viral diagnostic capability, to strengthen Europe's capacity to respond to an infectious disease emergency, and to offer assistance to countries not equipped with such costly facilities. Network partners have agreed on a common strategy to fill the gaps identified in the field of risk group-4 agents' laboratory diagnosis, namely the lack of standardization and of reference samples. The network has received a further 3-year funding, to offer assistance to external laboratories, and to start the planning of field activities.

  14. European consensus statement on diagnosis and treatment of adult ADHD: the European Network adult ADHD

    LENUS (Irish Health Repository)

    Kooij, Sandra JJ

    2010-09-03

    Abstract Background Attention deficit hyperactivity disorder (ADHD) is among the most common psychiatric disorders of childhood that persists into adulthood in the majority of cases. The evidence on persistence poses several difficulties for adult psychiatry considering the lack of expertise for diagnostic assessment, limited treatment options and patient facilities across Europe. Methods The European Network Adult ADHD, founded in 2003, aims to increase awareness of this disorder and improve knowledge and patient care for adults with ADHD across Europe. This Consensus Statement is one of the actions taken by the European Network Adult ADHD in order to support the clinician with research evidence and clinical experience from 18 European countries in which ADHD in adults is recognised and treated. Results Besides information on the genetics and neurobiology of ADHD, three major questions are addressed in this statement: (1) What is the clinical picture of ADHD in adults? (2) How can ADHD in adults be properly diagnosed? (3) How should ADHD in adults be effectively treated? Conclusions ADHD often presents as an impairing lifelong condition in adults, yet it is currently underdiagnosed and treated in many European countries, leading to ineffective treatment and higher costs of illness. Expertise in diagnostic assessment and treatment of ADHD in adults must increase in psychiatry. Instruments for screening and diagnosis of ADHD in adults are available and appropriate treatments exist, although more research is needed in this age group.

  15. A pre-trained convolutional neural network based method for thyroid nodule diagnosis.

    Science.gov (United States)

    Ma, Jinlian; Wu, Fa; Zhu, Jiang; Xu, Dong; Kong, Dexing

    2017-01-01

    In ultrasound images, most thyroid nodules are in heterogeneous appearances with various internal components and also have vague boundaries, so it is difficult for physicians to discriminate malignant thyroid nodules from benign ones. In this study, we propose a hybrid method for thyroid nodule diagnosis, which is a fusion of two pre-trained convolutional neural networks (CNNs) with different convolutional layers and fully-connected layers. Firstly, the two networks pre-trained with ImageNet database are separately trained. Secondly, we fuse feature maps learned by trained convolutional filters, pooling and normalization operations of the two CNNs. Finally, with the fused feature maps, a softmax classifier is used to diagnose thyroid nodules. The proposed method is validated on 15,000 ultrasound images collected from two local hospitals. Experiment results show that the proposed CNN based methods can accurately and effectively diagnose thyroid nodules. In addition, the fusion of the two CNN based models lead to significant performance improvement, with an accuracy of 83.02%±0.72%. These demonstrate the potential clinical applications of this method. Copyright © 2016 Elsevier B.V. All rights reserved.

  16. Empirical Mode Decomposition and Neural Networks on FPGA for Fault Diagnosis in Induction Motors

    Directory of Open Access Journals (Sweden)

    David Camarena-Martinez

    2014-01-01

    Full Text Available Nowadays, many industrial applications require online systems that combine several processing techniques in order to offer solutions to complex problems as the case of detection and classification of multiple faults in induction motors. In this work, a novel digital structure to implement the empirical mode decomposition (EMD for processing nonstationary and nonlinear signals using the full spline-cubic function is presented; besides, it is combined with an adaptive linear network (ADALINE-based frequency estimator and a feed forward neural network (FFNN-based classifier to provide an intelligent methodology for the automatic diagnosis during the startup transient of motor faults such as: one and two broken rotor bars, bearing defects, and unbalance. Moreover, the overall methodology implementation into a field-programmable gate array (FPGA allows an online and real-time operation, thanks to its parallelism and high-performance capabilities as a system-on-a-chip (SoC solution. The detection and classification results show the effectiveness of the proposed fused techniques; besides, the high precision and minimum resource usage of the developed digital structures make them a suitable and low-cost solution for this and many other industrial applications.

  17. Empirical Mode Decomposition and Neural Networks on FPGA for Fault Diagnosis in Induction Motors

    Science.gov (United States)

    Garcia-Perez, Arturo; Osornio-Rios, Roque Alfredo; Romero-Troncoso, Rene de Jesus

    2014-01-01

    Nowadays, many industrial applications require online systems that combine several processing techniques in order to offer solutions to complex problems as the case of detection and classification of multiple faults in induction motors. In this work, a novel digital structure to implement the empirical mode decomposition (EMD) for processing nonstationary and nonlinear signals using the full spline-cubic function is presented; besides, it is combined with an adaptive linear network (ADALINE)-based frequency estimator and a feed forward neural network (FFNN)-based classifier to provide an intelligent methodology for the automatic diagnosis during the startup transient of motor faults such as: one and two broken rotor bars, bearing defects, and unbalance. Moreover, the overall methodology implementation into a field-programmable gate array (FPGA) allows an online and real-time operation, thanks to its parallelism and high-performance capabilities as a system-on-a-chip (SoC) solution. The detection and classification results show the effectiveness of the proposed fused techniques; besides, the high precision and minimum resource usage of the developed digital structures make them a suitable and low-cost solution for this and many other industrial applications. PMID:24678281

  18. Automatic disease diagnosis using optimised weightless neural networks for low-power wearable devices.

    Science.gov (United States)

    Cheruku, Ramalingaswamy; Edla, Damodar Reddy; Kuppili, Venkatanareshbabu; Dharavath, Ramesh; Beechu, Nareshkumar Reddy

    2017-08-01

    Low-power wearable devices for disease diagnosis are used at anytime and anywhere. These are non-invasive and pain-free for the better quality of life. However, these devices are resource constrained in terms of memory and processing capability. Memory constraint allows these devices to store a limited number of patterns and processing constraint provides delayed response. It is a challenging task to design a robust classification system under above constraints with high accuracy. In this Letter, to resolve this problem, a novel architecture for weightless neural networks (WNNs) has been proposed. It uses variable sized random access memories to optimise the memory usage and a modified binary TRIE data structure for reducing the test time. In addition, a bio-inspired-based genetic algorithm has been employed to improve the accuracy. The proposed architecture is experimented on various disease datasets using its software and hardware realisations. The experimental results prove that the proposed architecture achieves better performance in terms of accuracy, memory saving and test time as compared to standard WNNs. It also outperforms in terms of accuracy as compared to conventional neural network-based classifiers. The proposed architecture is a powerful part of most of the low-power wearable devices for the solution of memory, accuracy and time issues.

  19. Does the upgrading of the radio communications network in health ...

    African Journals Online (AJOL)

    In an attempt to strengthen the obstetric referral system, the Safe Motherhood Project installed a repeater-based VHF radio communication system in three pilot districts. The overall goal of the new network was to enable the health centers to communicate directly to their district health offices (DHOs) for an ambulance when ...

  20. One Health in social networks and social media.

    Science.gov (United States)

    Mekaru, S R; Brownstein, J S

    2014-08-01

    In the rapidly evolving world of social media, social networks, mobile applications and citizen science, online communities can develop organically and separately from larger or more established organisations. The One Health online community is experiencing expansion from both the bottom up and the top down. In this paper, the authors review social media's strengths and weaknesses, earlier work examining Internet resources for One Health, the current state of One Health in social media (e.g. Facebook, Twitter, YouTube) and online social networking sites (e.g. LinkedIn and ResearchGate), as well as social media in One Health-related citizen science projects. While One Health has a fairly strong presence on websites, its social media presence is more limited and has an uneven geographic distribution. In work following the Stone Mountain Meeting,the One Health Global Network Task Force Report recommended the creation of an online community of practice. Professional social networks as well as the strategic use of social media should be employed in this effort. Finally, One Health-related research projects using volunteers (citizen science) often use social media to enhance their recruitment. Including these researchers in a community of practitioners would take full advantage of their existing social media presence. In conclusion, the interactive nature of social media, combined with increasing global Internet access, provides the One Health community with opportunities to meaningfully expand their community and promote their message.

  1. Social Networks, Interpersonal Social Support, and Health Outcomes: A Health Communication Perspective

    OpenAIRE

    Wright, Kevin

    2016-01-01

    This manuscript discusses the development, impact, and several major research findings of studies in the area of social network support and health outcomes. The review focuses largely on the development of online social support networks and the ways in which they may interact with face-to-face support networks to influence physical and psychological health outcomes. The manuscript discusses this area, and it presents a research agenda for future work in this area from an Associate Editor’s pe...

  2. State Support: A Prerequisite for Global Health Network Effectiveness; Comment on “Four Challenges that Global Health Networks Face”

    Directory of Open Access Journals (Sweden)

    Robert Marten

    2018-03-01

    Full Text Available Shiffman recently summarized lessons for network effectiveness from an impressive collection of case-studies. However, in common with most global health governance analysis in recent years, Shiffman underplays the important role of states in these global networks. As the body which decides and signs international agreements, often provides the resourcing, and is responsible for implementing initiatives all contributing to the prioritization of certain issues over others, state recognition and support is a prerequisite to enabling and determining global health networks’ success. The role of states deserves greater attention, analysis and consideration. We reflect upon the underappreciated role of the state within the current discourse on global health. We present the tobacco case study to illustrate the decisive role of states in determining progress for global health networks, and highlight how states use a legitimacy loop to gain legitimacy from and provide legitimacy to global health networks. Moving forward in assessing global health networks’ effectiveness, further investigating state support as a determinant of success will be critical. Understanding how global health networks and states interact and evolve to shape and support their respective interests should be a focus for future research.

  3. Network, anatomical, and non-imaging measures for the prediction of ADHD diagnosis in individual subjects

    Directory of Open Access Journals (Sweden)

    Jason W Bohland

    2012-12-01

    Full Text Available Brain imaging methods have long held promise as diagnostic aids for neuropsychiatric conditions with complex behavioral phenotypes such as Attention-Deficit/Hyperactivity Disorder. This promise has largely been unrealized, at least partly due to the heterogeneity of clinical populations and the small sample size of many studies. A large, multi-center dataset provided by the ADHD-200 Consortium affords new opportunities to test methods for individual diagnosis based on MRI-observable structural brain attributes and functional interactions observable from resting state fMRI. In this study, we systematically calculated a large set of standard and new quantitative markers from individual subject datasets. These features (>12,000 per subject consisted of local anatomical attributes such as cortical thickness and structure volumes and both local and global resting state network measures. Three methods were used to compute graphs representing interdependencies between activations in different brain areas, and a full set of network features was derived from each. Of these, features derived from the inverse of the time series covariance matrix, under an L1-norm regularization penalty, proved most powerful. Anatomical and network feature sets were used individually, and combined with non-imaging phenotypic features from each subject. Machine learning algorithms were used to rank attributes, and performance was assessed under cross-validation and on a separate test set of 168 subjects for a variety of feature set combinations. While non-imaging features gave highest performance in cross-validation, the addition of imaging features in sufficient numbers led to improved generalization to new data. Stratification by gender also proved to be a fruitful strategy to improve classifier performance. We describe the overall approach used, compare the predictive power of different classes of features, and describe the most impactful features in relation to the

  4. Networked Learning and Network Science: Potential Applications to Health Professionals' Continuing Education and Development.

    Science.gov (United States)

    Margolis, Alvaro; Parboosingh, John

    2015-01-01

    Prior interpersonal relationships and interactivity among members of professional associations may impact the learning process in continuing medical education (CME). On the other hand, CME programs that encourage interactivity between participants may impact structures and behaviors in these professional associations. With the advent of information and communication technologies, new communication spaces have emerged that have the potential to enhance networked learning in national and international professional associations and increase the effectiveness of CME for health professionals. In this article, network science, based on the application of network theory and other theories, is proposed as an approach to better understand the contribution networking and interactivity between health professionals in professional communities make to their learning and adoption of new practices over time. © 2015 The Alliance for Continuing Education in the Health Professions, the Society for Academic Continuing Medical Education, and the Council on Continuing Medical Education, Association for Hospital Medical Education.

  5. Global health research needs global networking

    NARCIS (Netherlands)

    Ignaciuk, A.; Leemans, R.

    2012-01-01

    To meet the challenges arising from global environmental change on human health, co-developing common approaches and new alliances of science and society are necessary. The first steps towards defining cross-cutting, health-environment issues were developed by the Global Environmental Change and

  6. Comparison of sputum collection methods for tuberculosis diagnosis: a systematic review and pairwise and network meta-analysis.

    Science.gov (United States)

    Datta, Sumona; Shah, Lena; Gilman, Robert H; Evans, Carlton A

    2017-08-01

    The performance of laboratory tests to diagnose pulmonary tuberculosis is dependent on the quality of the sputum sample tested. The relative merits of sputum collection methods to improve tuberculosis diagnosis are poorly characterised. We therefore aimed to investigate the effects of sputum collection methods on tuberculosis diagnosis. We did a systematic review and meta-analysis to investigate whether non-invasive sputum collection methods in people aged at least 12 years improve the diagnostic performance of laboratory testing for pulmonary tuberculosis. We searched PubMed, Google Scholar, ProQuest, Web of Science, CINAHL, and Embase up to April 14, 2017, to identify relevant experimental, case-control, or cohort studies. We analysed data by pairwise meta-analyses with a random-effects model and by network meta-analysis. All diagnostic performance data were calculated at the sputum-sample level, except where authors only reported data at the individual patient-level. Heterogeneity was assessed, with potential causes identified by logistic meta-regression. We identified 23 eligible studies published between 1959 and 2017, involving 8967 participants who provided 19 252 sputum samples. Brief, on-demand spot sputum collection was the main reference standard. Pooled sputum collection increased tuberculosis diagnosis by microscopy (odds ratio [OR] 1·6, 95% CI 1·3-1·9, pmeta-analysis confirmed these findings, and revealed that both pooled and instructed spot sputum collections were similarly effective techniques for increasing the diagnostic performance of microscopy. Tuberculosis diagnoses were substantially increased by either pooled collection or by providing instruction on how to produce a sputum sample taken at any time of the day. Both interventions had a similar effect to that reported for the introduction of new, expensive laboratory tests, and therefore warrant further exploration in the drive to end the global tuberculosis epidemic. Wellcome Trust

  7. Research on method of nuclear power plant operation fault diagnosis based on a combined artificial neural network

    International Nuclear Information System (INIS)

    Liu Feng; Yu Ren; Li Fengyu; Zhang Meng

    2007-01-01

    To solve the online real-time diagnosis problem of the nuclear power plant in operating condition, a method based on a combined artificial neural network is put forward in the paper. Its main principle is: using the BP neural network for the fast group diagnosis, and then using the RBF neural network for distinguishing and verifying the diagnostic result. The accuracy of the method is verified using the simulation values of the key parameters in normal status and malfunction status of a nuclear power plant. The results show that the method combining the advantages of the two neural networks can not only diagnose the learned faults in similar power level of the nuclear power plant quickly and accurately, but also can identify the faults in different power status, as well as the unlearned faults. The outputs of the diagnosis system are in form of the reliability of the faults, and are changing with the lasting of the operation time of the plant. This makes the diagnosis results be more acceptable to operators. (authors)

  8. Efficacy of an artificial neural network-based approach to endoscopic ultrasound elastography in diagnosis of focal pancreatic masses

    DEFF Research Database (Denmark)

    Săftoiu, Adrian; Vilmann, Peter; Gorunescu, Florin

    2012-01-01

    By using strain assessment, real-time endoscopic ultrasound (EUS) elastography provides additional information about a lesion's characteristics in the pancreas. We assessed the accuracy of real-time EUS elastography in focal pancreatic lesions using computer-aided diagnosis by artificial neural...... network analysis....

  9. Health care use after diagnosis of cancer in children.

    NARCIS (Netherlands)

    Heins, M.J.; Lorenzi, M.F.; Korevaar, J.C.; McBride, M.L.

    2014-01-01

    Purpose: Young patients with cancer often require extensive care during and shortly after cancer treatment for medical, psychosocial and educational problems. Approximately 85% are treated by an oncologist; however, their additional health care in this phase has barely been studied. The role of the

  10. Advancing Health Professions Education Research by Creating a Network of Networks.

    Science.gov (United States)

    Carney, Patricia A; Brandt, Barbara; Dekhtyar, Michael; Holmboe, Eric S

    2018-02-27

    Producing the best evidence to show educational outcomes, such as competency achievement and credentialing effectiveness, across the health professions education continuum will require large multisite research projects and longitudinal studies. Current limitations that must be overcome to reach this goal include the prevalence of single-institution study designs, assessments of a single curricular component, and cross-sectional study designs that provide only a snapshot in time of a program or initiative rather than a longitudinal perspective.One solution to overcoming these limitations is to develop a network of networks that collaborates, using longitudinal approaches, across health professions and regions of the United States. Currently, individual networks are advancing educational innovation toward understanding the effectiveness of educational and credentialing programs. Examples of such networks include: (1) the American Medical Association's Accelerating Change in Medical Education initiative, (2) the National Center for Interprofessional Practice and Education, and (3) the Accreditation Council for Graduate Medical Education's Accreditation System. In this Invited Commentary, the authors briefly profile these existing networks, identify their progress and the challenges they have encountered, and propose a vigorous way forward toward creating a national network of networks designed to determine the effectiveness of health professions education and credentialing.

  11. Proposed health state awareness of helicopter blades using an artificial neural network strategy

    Science.gov (United States)

    Lee, Andrew; Habtour, Ed; Gadsden, S. A.

    2016-05-01

    Structural health prognostics and diagnosis strategies can be classified as either model or signal-based. Artificial neural network strategies are popular signal-based techniques. This paper proposes the use of helicopter blades in order to study the sensitivity of an artificial neural network to structural fatigue. The experimental setup consists of a scale aluminum helicopter blade exposed to transverse vibratory excitation at the hub using single axis electrodynamic shaker. The intent of this study is to optimize an algorithm for processing high-dimensional data while retaining important information content in an effort to select input features and weights, as well as health parameters, for training a neural network. Data from accelerometers and piezoelectric transducers is collected from a known system designated as healthy. Structural damage will be introduced to different blades, which they will be designated as unhealthy. A variety of different tests will be performed to track the evolution and severity of the damage. A number of damage detection and diagnosis strategies will be implemented. A preliminary experiment was performed on aluminum cantilever beams providing a simpler model for implementation and proof of concept. Future work will look at utilizing the detection information as part of a hierarchical control system in order to mitigate structural damage and fatigue. The proposed approach may eliminate massive data storage on board of an aircraft through retaining relevant information only. The control system can then employ the relevant information to intelligently reconfigure adaptive maneuvers to avoid harmful regimes, thus, extending the life of the aircraft.

  12. Health 2.0-Lessons Learned: Social Networking With Patients for Health Promotion.

    Science.gov (United States)

    Sharma, Suparna; Kilian, Reena; Leung, Fok-Han

    2014-07-01

    The advent of social networking as a major platform for human interaction has introduced a new dimension into the physician-patient relationship, known as Health 2.0. The concept of Health 2.0 is young and evolving; so far, it has meant the use of social media by health professionals and patients to personalize health care and promote health education. Social networking sites like Facebook and Twitter offer promising platforms for health care providers to engage patients. Despite the vast potential of Health 2.0, usage by health providers remains relatively low. Using a pilot study as an example, this commentary reviews the ways in which physicians can effectively harness the power of social networking to meaningfully engage their patients in primary prevention. © The Author(s) 2014.

  13. [Organization of public oral health services for early diagnosis of potentially malignant disorders in the state of Rio de Janeiro, Brazil].

    Science.gov (United States)

    Casotti, Elisete; Monteiro, Ana Beatriz Fonseca; Castro Filho, Evelyn Lima de; Santos, Manuella Pires Dos

    2016-05-01

    This is a study of the organization of public health services in the state of Rio de Janeiro concerning the diagnosis of potentially malignant disorders. Secondary data from the database of the first phase of the Program for Enhancement for Access to and Quality of Primary Care were used. The implementation of actions at different levels for cancer prevention, the availability of diagnostic support services and the organization of the care network were assessed. The results show that only 58.8% of oral health teams record and monitor suspect cases; that only 47.1% reported having preferential channels for referring patients and there is great variation in waiting times to confirm the diagnosis. Local managerial and regional support actions can improve the organization of the care network for oral cancer prevention in the state.

  14. An artificial neural network ensemble method for fault diagnosis of proton exchange membrane fuel cell system

    International Nuclear Information System (INIS)

    Shao, Meng; Zhu, Xin-Jian; Cao, Hong-Fei; Shen, Hai-Feng

    2014-01-01

    The commercial viability of PEMFC (proton exchange membrane fuel cell) systems depends on using effective fault diagnosis technologies in PEMFC systems. However, many researchers have experimentally studied PEMFC (proton exchange membrane fuel cell) systems without considering certain fault conditions. In this paper, an ANN (artificial neural network) ensemble method is presented that improves the stability and reliability of the PEMFC systems. In the first part, a transient model giving it flexibility in application to some exceptional conditions is built. The PEMFC dynamic model is built and simulated using MATLAB. In the second, using this model and experiments, the mechanisms of four different faults in PEMFC systems are analyzed in detail. Third, the ANN ensemble for the fault diagnosis is built and modeled. This model is trained and tested by the data. The test result shows that, compared with the previous method for fault diagnosis of PEMFC systems, the proposed fault diagnosis method has higher diagnostic rate and generalization ability. Moreover, the partial structure of this method can be altered easily, along with the change of the PEMFC systems. In general, this method for diagnosis of PEMFC has value for certain applications. - Highlights: • We analyze the principles and mechanisms of the four faults in PEMFC (proton exchange membrane fuel cell) system. • We design and model an ANN (artificial neural network) ensemble method for the fault diagnosis of PEMFC system. • This method has high diagnostic rate and strong generalization ability

  15. Neural networks prediction and fault diagnosis applied to stationary and non stationary ARMA (Autoregressive moving average) modeled time series

    International Nuclear Information System (INIS)

    Marseguerra, M.; Minoggio, S.; Rossi, A.; Zio, E.

    1992-01-01

    The correlated noise affecting many industrial plants under stationary or cyclo-stationary conditions - nuclear reactors included -has been successfully modeled by autoregressive moving average (ARMA) due to the versatility of this technique. The relatively recent neural network methods have similar features and much effort is being devoted to exploring their usefulness in forecasting and control. Identifying a signal by means of an ARMA model gives rise to the problem of selecting its correct order. Similar difficulties must be faced when applying neural network methods and, specifically, particular care must be given to the setting up of the appropriate network topology, the data normalization procedure and the learning code. In the present paper the capability of some neural networks of learning ARMA and seasonal ARMA processes is investigated. The results of the tested cases look promising since they indicate that the neural networks learn the underlying process with relative ease so that their forecasting capability may represent a convenient fault diagnosis tool. (Author)

  16. High Health Care Utilization Preceding Diagnosis of Systemic Lupus Erythematosus in Youth.

    Science.gov (United States)

    Chang, Joyce C; Mandell, David S; Knight, Andrea M

    2017-12-01

    Childhood-onset systemic lupus erythematosus (SLE) is associated with high risk for organ damage, which may be mitigated by early diagnosis and treatment. We characterized health care utilization for youth in the year preceding SLE diagnosis compared to controls. Using Clinformatics ™ DataMart (OptumInsight, Eden Prairie, MN) de-identified administrative data from 2000 to 2013, we identified 682 youth ages 10-24 years with new-onset SLE (≥3 International Classification of Diseases, Ninth Revision (ICD-9) codes for SLE 710.0, each >30 days apart), and 1,364 age and sex-matched healthy controls. We compared the incidence of ambulatory, emergency, and inpatient visits 12 months before SLE diagnosis, and frequency of primary diagnoses. We examined subject characteristics associated with utilization preceding SLE diagnosis. Youth with SLE had significantly more visits in the year preceding diagnosis than controls across ambulatory (incidence rate ratio (IRR) 2.48, p<0.001), emergency (IRR 3.42, p<0.001) and inpatient settings (IRR 3.02, p<0.001). The most frequent acute care diagnoses and median days to SLE diagnosis were: venous thromboembolism (313, interquartile range (IQR) 18-356), thrombocytopenia (278, IQR 39-354), chest pain (73, IQR 29.5-168), fever (52, IQR 17-166), and acute kidney failure (14, IQR 5-168). Having a psychiatric diagnosis prior to SLE diagnosis was strongly associated with increased utilization across all settings. Youth with SLE have high health care utilization throughout the year preceding SLE diagnosis. Examining variable diagnostic trajectories of youth presenting for acute care preceding SLE diagnosis, and increased attention to psychiatric morbidity may help improve care for youth with new-onset SLE. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  17. DIAGNOSIS OF OSTEOARTHROSIS IN PRIMARY HEALTH CARE OF BULGARIA

    Directory of Open Access Journals (Sweden)

    Maria Panchovska

    2013-01-01

    Full Text Available Osteoarthrosis is the most common rheumatic disease and occurs in more than 50% of all rheumatic patients. These patients are diagnosed and treated by rheumatologists, orthopedists, and neurologists in the primary health care ofBulgaria. The problems in these patients are primarily encountered by general practitioners (family physicians who estimate the need for specialized medical care. The paper considers the organizational aspects of primary medical carefor patients with ostheoarthosis. Six-year data are analyzed.

  18. Shifts in the architecture of the Nationwide Health Information Network.

    Science.gov (United States)

    Lenert, Leslie; Sundwall, David; Lenert, Michael Edward

    2012-01-01

    In the midst of a US $30 billion USD investment in the Nationwide Health Information Network (NwHIN) and electronic health records systems, a significant change in the architecture of the NwHIN is taking place. Prior to 2010, the focus of information exchange in the NwHIN was the Regional Health Information Organization (RHIO). Since 2010, the Office of the National Coordinator (ONC) has been sponsoring policies that promote an internet-like architecture that encourages point to-point information exchange and private health information exchange networks. The net effect of these activities is to undercut the limited business model for RHIOs, decreasing the likelihood of their success, while making the NwHIN dependent on nascent technologies for community level functions such as record locator services. These changes may impact the health of patients and communities. Independent, scientifically focused debate is needed on the wisdom of ONC's proposed changes in its strategy for the NwHIN.

  19. HHT diagnosis by Mid-infrared spectroscopy and artificial neural network analysis.

    Science.gov (United States)

    Lux, Andreas; Müller, Ralf; Tulk, Mark; Olivieri, Carla; Zarrabeita, Roberto; Salonikios, Theresia; Wirnitzer, Bernhard

    2013-06-27

    The vascular disorder Hereditary Hemorrhagic Telangiectasia (HHT) is in general an inherited disease caused by mutations in the TGF-β/BMP receptors endoglin or ALK1 or in rare cases by mutations of the TGF-β signal transducer protein Smad4 leading to the combined syndrome of juvenile polyposis and HHT. HHT is characterized by several clinical symptoms like spontaneous and recurrent epistaxis, multiple telangiectases at sites like lips, oral cavity, fingers, nose, and visceral lesions like gastrointestinal telangiectasia, pulmonary, hepatic, cerebral or spinal arteriovenous malformations. The disease shows an inter- and intra-family variability in penetrance as well as symptoms from mild to life threatening. Penetrance is also depending on age. Diagnosis of the disease is based on the presence of some of the listed symptoms or by genetic testing. HHT diagnosis is laborious, time consuming, costly and sometimes uncertain. Not all typical symptoms may be present, especially at a younger age, and genetic testing does not always identify the disease causing mutation. Infrared (IR) spectroscopy was investigated as a potential alternative to the current diagnostic methods. IR-spectra were obtained by Fourier-transform Mid-IR spectroscopy from blood plasma from HHT patients and a healthy control group. Spectral data were mathematically processed and subsequently classified and analysed by artificial neural network (ANN) analyses and by visual analysis of scatter plots of the dominant principal components. The analyses showed that for HHT a disease specific IR-spectrum exists that is significantly different from the control group. Furthermore, at the current stage with the here used methods, HHT can be diagnosed by Mid-IR-spectroscopy in combination with ANN analysis with a sensitivity and specificity of at least 95%. Visual analysis of PCA scatter plots revealed an inter class variation of the HHT group. IR-spectroscopy in combination with ANN analysis can be considered

  20. Health impact assessment of cycling network expansions in European cities.

    Science.gov (United States)

    Mueller, Natalie; Rojas-Rueda, David; Salmon, Maëlle; Martinez, David; Ambros, Albert; Brand, Christian; de Nazelle, Audrey; Dons, Evi; Gaupp-Berghausen, Mailin; Gerike, Regine; Götschi, Thomas; Iacorossi, Francesco; Int Panis, Luc; Kahlmeier, Sonja; Raser, Elisabeth; Nieuwenhuijsen, Mark

    2018-04-01

    We conducted a health impact assessment (HIA) of cycling network expansions in seven European cities. We modeled the association between cycling network length and cycling mode share and estimated health impacts of the expansion of cycling networks. First, we performed a non-linear least square regression to assess the relationship between cycling network length and cycling mode share for 167 European cities. Second, we conducted a quantitative HIA for the seven cities of different scenarios (S) assessing how an expansion of the cycling network [i.e. 10% (S1); 50% (S2); 100% (S3), and all-streets (S4)] would lead to an increase in cycling mode share and estimated mortality impacts thereof. We quantified mortality impacts for changes in physical activity, air pollution and traffic incidents. Third, we conducted a cost-benefit analysis. The cycling network length was associated with a cycling mode share of up to 24.7% in European cities. The all-streets scenario (S4) produced greatest benefits through increases in cycling for London with 1,210 premature deaths (95% CI: 447-1,972) avoidable annually, followed by Rome (433; 95% CI: 170-695), Barcelona (248; 95% CI: 86-410), Vienna (146; 95% CI: 40-252), Zurich (58; 95% CI: 16-100) and Antwerp (7; 95% CI: 3-11). The largest cost-benefit ratios were found for the 10% increase in cycling networks (S1). If all 167 European cities achieved a cycling mode share of 24.7% over 10,000 premature deaths could be avoided annually. In European cities, expansions of cycling networks were associated with increases in cycling and estimated to provide health and economic benefits. Copyright © 2018 Elsevier Inc. All rights reserved.

  1. A comparative study of 11 local health department organizational networks.

    Science.gov (United States)

    Merrill, Jacqueline; Keeling, Jonathan W; Carley, Kathleen M

    2010-01-01

    Although the nation's local health departments (LHDs) share a common mission, variability in administrative structures is a barrier to identifying common, optimal management strategies. There is a gap in understanding what unifying features LHDs share as organizations that could be leveraged systematically for achieving high performance. To explore sources of commonality and variability in a range of LHDs by comparing intraorganizational networks. We used organizational network analysis to document relationships between employees, tasks, knowledge, and resources within LHDs, which may exist regardless of formal administrative structure. A national sample of 11 LHDs from seven states that differed in size, geographic location, and governance. Relational network data were collected via an on-line survey of all employees in 11 LHDs. A total of 1062 out of 1239 employees responded (84% response rate). Network measurements were compared using coefficient of variation. Measurements were correlated with scores from the National Public Health Performance Assessment and with LHD demographics. Rankings of tasks, knowledge, and resources were correlated across pairs of LHDs. We found that 11 LHDs exhibited compound organizational structures in which centralized hierarchies were coupled with distributed networks at the point of service. Local health departments were distinguished from random networks by a pattern of high centralization and clustering. Network measurements were positively associated with performance for 3 of 10 essential services (r > 0.65). Patterns in the measurements suggest how LHDs adapt to the population served. Shared network patterns across LHDs suggest where common organizational management strategies are feasible. This evidence supports national efforts to promote uniform standards for service delivery to diverse populations.

  2. EurOOHnet-the European research network for out-of-hours primary health care.

    Science.gov (United States)

    Huibers, Linda; Philips, Hilde; Giesen, Paul; Remmen, Roy; Christensen, Morten Bondo; Bondevik, Gunnar Tschudi

    2014-09-01

    European countries face similar challenges in the provision of health care. Demographic factors like ageing, population growth, changing patient behaviour, and lack of work force lead to increasing demands, costs, and overcrowding of out-of-hours (OOH) care (i.e. primary care services, emergency departments (EDs), and ambulance services). These developments strain services and imply safety risks. In the last few decades, countries have been re-organizing their OOH primary health care services. AIM AND SCOPE OF THE NETWORK: We established a European research network for out-of-hours primary health care (EurOOHnet), which aims to transfer knowledge, share experiences, and conduct research. Combining research competencies and integrating results can generate a profound information flow to European researchers and decision makers in health policy, contributing towards feasible and high-quality OOH care. It also contributes to a more comparable performance level within European regions. CONDUCTED RESEARCH PROJECTS: The European research network aims to conduct mutual research projects. At present, three projects have been accomplished, among others concerning the diagnostic scope in OOH primary care services and guideline adherence for diagnosis and treatment of cystitis in OOH primary care. Future areas of research will be organizational models for OOH care; appropriate use of the OOH services; quality of telephone triage; quality of medical care; patient safety issues; use of auxiliary personnel; collaboration with EDs and ambulance care; and the role of GPs in OOH care.

  3. Implementation and integration of regional health care data networks in the Hellenic National Health Service.

    Science.gov (United States)

    Lampsas, Petros; Vidalis, Ioannis; Papanikolaou, Christos; Vagelatos, Aristides

    2002-12-01

    Modern health care is provided with close cooperation among many different institutions and professionals, using their specialized expertise in a common effort to deliver best-quality and, at the same time, cost-effective services. Within this context of the growing need for information exchange, the demand for realization of data networks interconnecting various health care institutions at a regional level, as well as a national level, has become a practical necessity. To present the technical solution that is under consideration for implementing and interconnecting regional health care data networks in the Hellenic National Health System. The most critical requirements for deploying such a regional health care data network were identified as: fast implementation, security, quality of service, availability, performance, and technical support. The solution proposed is the use of proper virtual private network technologies for implementing functionally-interconnected regional health care data networks. The regional health care data network is considered to be a critical infrastructure for further development and penetration of information and communication technologies in the Hellenic National Health System. Therefore, a technical approach was planned, in order to have a fast cost-effective implementation, conforming to certain specifications.

  4. Diagnosis and Prognostic of Wastewater Treatment System Based on Bayesian Network

    Science.gov (United States)

    Li, Dan; Yang, Haizhen; Liang, XiaoFeng

    2010-11-01

    Wastewater treatment is a complicated and dynamic process. The treatment effect can be influenced by many variables in microbial, chemical and physical aspects. These variables are always uncertain. Due to the complex biological reaction mechanisms, the highly time-varying and multivariable aspects, the diagnosis and prognostic of wastewater treatment system are still difficult in practice. Bayesian network (BN) is one of the best methods for dealing with uncertainty in the artificial intelligence field. Because of the powerful inference ability and convenient decision mechanism, BN can be employed into the model description and influencing factor analysis of wastewater treatment system with great flexibility and applicability.In this paper, taking modified sequencing batch reactor (MSBR) as an analysis object, BN model was constructed according to the influent water quality, operational condition and effluent effect data of MSBR, and then a novel approach based on BN is proposed to analyze the influencing factors of the wastewater treatment system. The approach presented gives an effective tool for diagnosing and predicting analysis of the wastewater treatment system. On the basis of the influent water quality and operational condition, effluent effect can be predicted. Moreover, according to the effluent effect, the influent water quality and operational condition also can be deduced.

  5. Third-Order Spectral Techniques for the Diagnosis of Motor Bearing Condition Using Artificial Neural Networks

    Science.gov (United States)

    Yang, D.-M.; Stronach, A. F.; MacConnell, P.; Penman, J.

    2002-03-01

    This paper addresses the development of a novel condition monitoring procedure for rolling element bearings which involves a combination of signal processing, signal analysis and artificial intelligence methods. Seven approaches based on power spectrum, bispectral and bicoherence vibration analyses are investigated as signal pre-processing techniques for application in the diagnosis of a number of induction motor rolling element bearing conditions. The bearing conditions considered are a normal bearing and bearings with cage and inner and outer race faults. The vibration analysis methods investigated are based on the power spectrum, the bispectrum, the bicoherence, the bispectrum diagonal slice, the bicoherence diagonal slice, the summed bispectrum and the summed bicoherence. Selected features are extracted from the vibration signatures so obtained and these are used as inputs to an artificial neural network trained to identify the bearing conditions. Quadratic phase coupling (QPC), examined using the magnitude of bispectrum and bicoherence and biphase, is shown to be absent from the bearing system and it is therefore concluded that the structure of the bearing vibration signatures results from inter-modulation effects. In order to test the proposed procedure, experimental data from a bearing test rig are used to develop an example diagnostic system. Results show that the bearing conditions examined can be diagnosed with a high success rate, particularly when using the summed bispectrum signatures.

  6. Fault detection and diagnosis in asymmetric multilevel inverter using artificial neural network

    Science.gov (United States)

    Raj, Nithin; Jagadanand, G.; George, Saly

    2018-04-01

    The increased component requirement to realise multilevel inverter (MLI) fallout in a higher fault prospect due to power semiconductors. In this scenario, efficient fault detection and diagnosis (FDD) strategies to detect and locate the power semiconductor faults have to be incorporated in addition to the conventional protection systems. Even though a number of FDD methods have been introduced in the symmetrical cascaded H-bridge (CHB) MLIs, very few methods address the FDD in asymmetric CHB-MLIs. In this paper, the gate-open circuit FDD strategy in asymmetric CHB-MLI is presented. Here, a single artificial neural network (ANN) is used to detect and diagnose the fault in both binary and trinary configurations of the asymmetric CHB-MLIs. In this method, features of the output voltage of the MLIs are used as to train the ANN for FDD method. The results prove the validity of the proposed method in detecting and locating the fault in both asymmetric MLI configurations. Finally, the ANN response to the input parameter variation is also analysed to access the performance of the proposed ANN-based FDD strategy.

  7. External quality assessment of malaria microscopy diagnosis in selected health facilities in Western Oromia, Ethiopia.

    Science.gov (United States)

    Sori, Getachew; Zewdie, Olifan; Tadele, Geletta; Samuel, Abdi

    2018-06-18

    Accurate early diagnosis and prompt treatment are one of the key strategies to control and prevent malaria disease. External quality assessment is the most effective method for evaluation of the quality of malaria microscopy diagnosis. The aim of this study was to assess the quality of malaria microscopy diagnosis and its associated factors in selected public health facility laboratories in East Wollega Zone, Western Ethiopia. Facility-based cross-sectional study design was conducted in 30 randomly selected public health facility laboratories from November 2014 to January 2015 in East Wollega Zone, Western Ethiopia. Ten validated stained malaria panel slides with known Plasmodium species, developmental stage and parasite density were distributed. Data were captured; cleaned and analyzed using SPSS version 20 statistical software-multivariate logistic regressions and the agreement in reading between the peripheral diagnostic centers and the reference laboratory were done using kappa statistics. A total of 30 health facility laboratories were involved in the study and the overall quality of malaria microscopy diagnosis was poor (62.3%). The associated predictors of quality in this diagnosis were in-service training [(AOR = 16, 95% CI (1.3, 1.96)], smearing quality [(AOR = 24, 95% CI (1.8, 3.13)], staining quality [(AOR = 15, 95% CI (2.35, 8.61), parasite detection [(AOR = 9, 95% CI (1.1, 8.52)] and identification skills [(AOR = 8.6, 95% CI (1.21, 1.63)]. Eighteen (60%) of health facility laboratories had in-service trained laboratory professionals on malaria microscopy diagnosis. Overall quality of malaria microscopy diagnosis was poor and a significant gap in this service was observed that could impact on its diagnostic services.

  8. Automated diagnosis of prostate cancer in multi-parametric MRI based on multimodal convolutional neural networks

    Science.gov (United States)

    Le, Minh Hung; Chen, Jingyu; Wang, Liang; Wang, Zhiwei; Liu, Wenyu; (Tim Cheng, Kwang-Ting; Yang, Xin

    2017-08-01

    Automated methods for prostate cancer (PCa) diagnosis in multi-parametric magnetic resonance imaging (MP-MRIs) are critical for alleviating requirements for interpretation of radiographs while helping to improve diagnostic accuracy (Artan et al 2010 IEEE Trans. Image Process. 19 2444-55, Litjens et al 2014 IEEE Trans. Med. Imaging 33 1083-92, Liu et al 2013 SPIE Medical Imaging (International Society for Optics and Photonics) p 86701G, Moradi et al 2012 J. Magn. Reson. Imaging 35 1403-13, Niaf et al 2014 IEEE Trans. Image Process. 23 979-91, Niaf et al 2012 Phys. Med. Biol. 57 3833, Peng et al 2013a SPIE Medical Imaging (International Society for Optics and Photonics) p 86701H, Peng et al 2013b Radiology 267 787-96, Wang et al 2014 BioMed. Res. Int. 2014). This paper presents an automated method based on multimodal convolutional neural networks (CNNs) for two PCa diagnostic tasks: (1) distinguishing between cancerous and noncancerous tissues and (2) distinguishing between clinically significant (CS) and indolent PCa. Specifically, our multimodal CNNs effectively fuse apparent diffusion coefficients (ADCs) and T2-weighted MP-MRI images (T2WIs). To effectively fuse ADCs and T2WIs we design a new similarity loss function to enforce consistent features being extracted from both ADCs and T2WIs. The similarity loss is combined with the conventional classification loss functions and integrated into the back-propagation procedure of CNN training. The similarity loss enables better fusion results than existing methods as the feature learning processes of both modalities are mutually guided, jointly facilitating CNN to ‘see’ the true visual patterns of PCa. The classification results of multimodal CNNs are further combined with the results based on handcrafted features using a support vector machine classifier. To achieve a satisfactory accuracy for clinical use, we comprehensively investigate three critical factors which could greatly affect the performance of our

  9. Dual diagnosis: co-existence of drug, alcohol and mental health problems.

    Science.gov (United States)

    Manley, David

    The Department of Health (DoH) published a set of good practice implementation guidelines on dual diagnosis in May 2002. This guidance suggests that in order to improve the prognosis for clients who have mental health problems and who drink or take drugs problematically, mental health and substance misuse services should adopt an integrated service model. There is a considerable amount of American-based research supporting this approach, but little evidence from the UK researchers demonstrating its application in the UK. This article offers an example of a service that has been developed in the city of Nottingham and argues that this client group will be served most effectively if mental health services support specialist dual-diagnosis resources. Integrated care pathways for this client group can be developed and led by specialist clinicians acting as consultants to mental health services (DoH, 2002a). This consultancy role within mental health services enhances the links needed between substance misuse and mental health services. As a result, specialist dual-diagnosis teams are best placed to increase positive prognoses for clients by ensuring evidence-based substance misuse skills are utilized and adapted by mental health teams to ensure fully integrated care coordination.

  10. Exploring mobile health in a private online social network.

    Science.gov (United States)

    Memon, Qurban A; Mustafa, Asma Fayes

    2015-01-01

    Health information is very vulnerable. Certain individuals or corporate organisations will continue to steal it similar to bank account data once data is on wireless channels. Once health information is part of a social network, corresponding privacy issues also surface. Insufficiently trained employees at hospitals that pay less attention to creating a privacy-aware culture will suffer loss when mobile devices containing health information are lost, stolen or sniffed. In this work, a social network system is explored as a m-health system from a privacy perspective. A model is developed within a framework of data-driven privacy and implemented on Android operating system. In order to check feasibility of the proposed model, a prototype application is developed on Facebook for different services, including: i) sharing user location; ii) showing nearby friends; iii) calculating and sharing distance moved, and calories burned; iv) calculating, tracking and sharing user heart rate; etc.

  11. LORD: a phenotype-genotype semantically integrated biomedical data tool to support rare disease diagnosis coding in health information systems.

    Science.gov (United States)

    Choquet, Remy; Maaroufi, Meriem; Fonjallaz, Yannick; de Carrara, Albane; Vandenbussche, Pierre-Yves; Dhombres, Ferdinand; Landais, Paul

    Characterizing a rare disease diagnosis for a given patient is often made through expert's networks. It is a complex task that could evolve over time depending on the natural history of the disease and the evolution of the scientific knowledge. Most rare diseases have genetic causes and recent improvements of sequencing techniques contribute to the discovery of many new diseases every year. Diagnosis coding in the rare disease field requires data from multiple knowledge bases to be aggregated in order to offer the clinician a global information space from possible diagnosis to clinical signs (phenotypes) and known genetic mutations (genotype). Nowadays, the major barrier to the coding activity is the lack of consolidation of such information scattered in different thesaurus such as Orphanet, OMIM or HPO. The Linking Open data for Rare Diseases (LORD) web portal we developed stands as the first attempt to fill this gap by offering an integrated view of 8,400 rare diseases linked to more than 14,500 signs and 3,270 genes. The application provides a browsing feature to navigate through the relationships between diseases, signs and genes, and some Application Programming Interfaces to help its integration in health information systems in routine.

  12. How do people with long-term mental health problems negotiate relationships with network members at times of crisis?

    Science.gov (United States)

    Walker, Sandra; Kennedy, Anne; Vassilev, Ivaylo; Rogers, Anne

    2018-02-01

    Social network processes impact on the genesis and management of mental health problems. There is currently less understanding of the way people negotiate networked relationships in times of crisis compared to how they manage at other times. This paper explores the patterns and nature of personal network involvement at times of crises and how these may differ from day-to-day networks of recovery and maintenance. Semi-structured interviews with 25 participants with a diagnosis of long-term mental health (MH) problems drawn from recovery settings in the south of England. Interviews centred on personal network mapping of members and resources providing support. The mapping interviews explored the work of network members and changes in times of crisis. Interviews were recorded, transcribed and analysed using a framework analysis. Three key themes were identified: the fluidity of network relationality between crisis and recovery; isolation as a means of crises management; leaning towards peer support. Personal network input retreated at times of crisis often as result of "ejection" from the network by participants who used self-isolation as a personal management strategy in an attempt to deal with crises. Peer support is considered useful during a crisis, whilst the role of services was viewed with some ambiguity. Social networks membership, and type and depth of involvement, is subject to change between times of crisis and everyday support. This has implications for managing mental health in terms of engaging with network support differently in times of crises versus recovery and everyday living. © 2017 The Authors Health Expectations Published by John Wiley & Sons Ltd.

  13. An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

    Science.gov (United States)

    Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng

    2017-01-01

    A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment. PMID:28230767

  14. Potential and challenges of body area networks for personal health.

    Science.gov (United States)

    Penders, Julien; van de Molengraft, Jef; Brown, Lindsay; Grundlehner, Bernard; Gyselinckx, Bert; Van Hoof, Chris

    2009-01-01

    This paper illustrates how body area network technology may enable new personal health concepts. A BAN technology platform is presented, which integrates technology building blocks from the Human++ research program on autonomous wireless sensors. Technology evaluation for the case of wireless sleep staging and real-time arousal monitoring is reported. Key technology challenges are discussed. The ultimate target is the development of miniaturized body sensor nodes powered by body-energy, anticipating the needs of emerging personal health applications.

  15. Local Health Integration Networks: Build on their purpose.

    Science.gov (United States)

    MacLeod, Hugh

    2015-11-01

    This article provides a high-level overview on the creation of Local Health Integration Networks (LHINs) and illustrates the complexities involved in their implementation. To understand regional structures such as LHINs, one must understand the context in which design and execution takes place. The article ends with a commentary on how Ontario is performing post-LHINs and discusses next steps. © 2015 The Canadian College of Health Leaders.

  16. Computer-aided diagnosis workstation and teleradiology network system for chest diagnosis using the web medical image conference system with a new information security solution

    Science.gov (United States)

    Satoh, Hitoshi; Niki, Noboru; Eguchi, Kenji; Ohmatsu, Hironobu; Kaneko, Masahiro; Kakinuma, Ryutaro; Moriyama, Noriyuki

    2010-03-01

    Diagnostic MDCT imaging requires a considerable number of images to be read. Moreover, the doctor who diagnoses a medical image is insufficient in Japan. Because of such a background, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images, a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification and a vertebra body analysis algorithm for quantitative evaluation of osteoporosis. We also have developed the teleradiology network system by using web medical image conference system. In the teleradiology network system, the security of information network is very important subjects. Our teleradiology network system can perform Web medical image conference in the medical institutions of a remote place using the web medical image conference system. We completed the basic proof experiment of the web medical image conference system with information security solution. We can share the screen of web medical image conference system from two or more web conference terminals at the same time. An opinion can be exchanged mutually by using a camera and a microphone that are connected with the workstation that builds in some diagnostic assistance methods. Biometric face authentication used on site of teleradiology makes "Encryption of file" and "Success in login" effective. Our Privacy and information security technology of information security solution ensures compliance with Japanese regulations. As a result, patients' private information is protected. Based on these diagnostic assistance methods, we have developed a new computer-aided workstation and a new teleradiology network that can display suspected lesions three-dimensionally in a short time. The results of this study indicate that our radiological information system without film by using computer-aided diagnosis

  17. Linear and Non-Linear Associations of Gonorrhea Diagnosis Rates with Social Determinants of Health

    Directory of Open Access Journals (Sweden)

    Hazel D. Dean

    2012-09-01

    Full Text Available Identifying how social determinants of health (SDH influence the burden of disease in communities and populations is critically important to determine how to target public health interventions and move toward health equity. A holistic approach to disease prevention involves understanding the combined effects of individual, social, health system, and environmental determinants on geographic area-based disease burden. Using 2006–2008 gonorrhea surveillance data from the National Notifiable Sexually Transmitted Disease Surveillance and SDH variables from the American Community Survey, we calculated the diagnosis rate for each geographic area and analyzed the associations between those rates and the SDH and demographic variables. The estimated product moment correlation (PMC between gonorrhea rate and SDH variables ranged from 0.11 to 0.83. Proportions of the population that were black, of minority race/ethnicity, and unmarried, were each strongly correlated with gonorrhea diagnosis rates. The population density, female proportion, and proportion below the poverty level were moderately correlated with gonorrhea diagnosis rate. To better understand relationships among SDH, demographic variables, and gonorrhea diagnosis rates, more geographic area-based estimates of additional variables are required. With the availability of more SDH variables and methods that distinguish linear from non-linear associations, geographic area-based analysis of disease incidence and SDH can add value to public health prevention and control programs.

  18. Managing health physics departmental data via a local area network

    International Nuclear Information System (INIS)

    Weber, P.J.; Castronovo, F.P. Jr.

    1993-01-01

    The authors describe the multiuser data management system that can be accessed simultaneously by all department members, in use at the Dept of Health Physics, Brigham and Women's Hospital, U.S.A., which makes use of the Local Area Network. (UK)

  19. Audit Trail Management System in Community Health Care Information Network.

    Science.gov (United States)

    Nakamura, Naoki; Nakayama, Masaharu; Nakaya, Jun; Tominaga, Teiji; Suganuma, Takuo; Shiratori, Norio

    2015-01-01

    After the Great East Japan Earthquake we constructed a community health care information network system. Focusing on the authentication server and portal server capable of SAML&ID-WSF, we proposed an audit trail management system to look over audit events in a comprehensive manner. Through implementation and experimentation, we verified the effectiveness of our proposed audit trail management system.

  20. Job attributes, job satisfaction and the return to health after breast cancer diagnosis and treatment.

    Science.gov (United States)

    Barnes, Andrew J; Robert, Nicholas; Bradley, Cathy J

    2014-02-01

    As detection and treatment of cancer has advanced, the number of working age women with breast cancer has increased. This study provides new information on the intersection of breast cancer treatment and job tasks and how, together, they impact employed and newly diagnosed women. The sample comprised 493 employed women within 2 months of initiating treatment. Job satisfaction and demands were assessed by a pre-diagnosis recall along with measures of mental and physical health and assessed again 9 months after initiating treatment. Using seemingly unrelated regression, we tested the effect of job tasks and satisfaction on mental and physical health 9 months post-treatment initiation, controlling for pre-diagnosis health status, patient characteristics, and job tasks. Physical job demands prior to diagnosis were not significantly associated with mental or physical health 9 months after treatment initiation. Employment in cognitively demanding and less satisfying jobs was associated with decreases in mental health and increases in problems with work or daily activities 9 months post-treatment initiation (pWomen who received five or more cycles of chemotherapy reported lower vitality, social functioning, and worse measures of physical health compared with those who did not receive chemotherapy (pjobs may impede mental health recovery, particularly in patients who receive longer chemotherapy regimens. Such information may be used by patients and clinicians in deciding when to undergo chemotherapy and whether job tasks can be restructured to hasten recovery. Copyright © 2013 John Wiley & Sons, Ltd. Copyright © 2013 John Wiley & Sons, Ltd.

  1. Effects of Teaching Health Care Workers on Diagnosis and Treatment of Pesticide Poisonings in Uganda.

    Science.gov (United States)

    Sibani, Claudia; Jessen, Kristian Kjaer; Tekin, Bircan; Nabankema, Victoria; Jørs, Erik

    2017-01-01

    Acute pesticide poisoning in developing countries is a considerable problem, requiring diagnosis and treatment. This study describes how training of health care workers in Uganda affects their ability to diagnose and manage acute pesticide poisoning. A postintervention cross-sectional study was conducted using a standardized questionnaire. A total of 326 health care workers in Uganda were interviewed on knowledge and handling of acute pesticide poisoning. Of those, 173 health care workers had received training, whereas 153 untrained health care workers from neighboring regions served as controls. Trained health care workers scored higher on knowledge of pesticide toxicity and handling of acute pesticide poisoning. Stratification by sex, profession, experience, and health center level did not have any influence on the outcome. Training health care workers can improve their knowledge and treatment of pesticide poisonings. Knowledge of the subject is still insufficient among health care workers and further training is needed.

  2. Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI.

    Science.gov (United States)

    Song, Yang; Zhang, Yu-Dong; Yan, Xu; Liu, Hui; Zhou, Minxiong; Hu, Bingwen; Yang, Guang

    2018-04-16

    Deep learning is the most promising methodology for automatic computer-aided diagnosis of prostate cancer (PCa) with multiparametric MRI (mp-MRI). To develop an automatic approach based on deep convolutional neural network (DCNN) to classify PCa and noncancerous tissues (NC) with mp-MRI. Retrospective. In all, 195 patients with localized PCa were collected from a PROSTATEx database. In total, 159/17/19 patients with 444/48/55 observations (215/23/23 PCas and 229/25/32 NCs) were randomly selected for training/validation/testing, respectively. T 2 -weighted, diffusion-weighted, and apparent diffusion coefficient images. A radiologist manually labeled the regions of interest of PCas and NCs and estimated the Prostate Imaging Reporting and Data System (PI-RADS) scores for each region. Inspired by VGG-Net, we designed a patch-based DCNN model to distinguish between PCa and NCs based on a combination of mp-MRI data. Additionally, an enhanced prediction method was used to improve the prediction accuracy. The performance of DCNN prediction was tested using a receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Moreover, the predicted result was compared with the PI-RADS score to evaluate its clinical value using decision curve analysis. Two-sided Wilcoxon signed-rank test with statistical significance set at 0.05. The DCNN produced excellent diagnostic performance in distinguishing between PCa and NC for testing datasets with an AUC of 0.944 (95% confidence interval: 0.876-0.994), sensitivity of 87.0%, specificity of 90.6%, PPV of 87.0%, and NPV of 90.6%. The decision curve analysis revealed that the joint model of PI-RADS and DCNN provided additional net benefits compared with the DCNN model and the PI-RADS scheme. The proposed DCNN-based model with enhanced prediction yielded high performance in statistical analysis, suggesting

  3. Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer's Disease Diagnosis.

    Science.gov (United States)

    Liu, Manhua; Cheng, Danni; Wang, Kundong; Wang, Yaping

    2018-03-23

    Accurate and early diagnosis of Alzheimer's disease (AD) plays important role for patient care and development of future treatment. Structural and functional neuroimages, such as magnetic resonance images (MRI) and positron emission tomography (PET), are providing powerful imaging modalities to help understand the anatomical and functional neural changes related to AD. In recent years, machine learning methods have been widely studied on analysis of multi-modality neuroimages for quantitative evaluation and computer-aided-diagnosis (CAD) of AD. Most existing methods extract the hand-craft imaging features after image preprocessing such as registration and segmentation, and then train a classifier to distinguish AD subjects from other groups. This paper proposes to construct cascaded convolutional neural networks (CNNs) to learn the multi-level and multimodal features of MRI and PET brain images for AD classification. First, multiple deep 3D-CNNs are constructed on different local image patches to transform the local brain image into more compact high-level features. Then, an upper high-level 2D-CNN followed by softmax layer is cascaded to ensemble the high-level features learned from the multi-modality and generate the latent multimodal correlation features of the corresponding image patches for classification task. Finally, these learned features are combined by a fully connected layer followed by softmax layer for AD classification. The proposed method can automatically learn the generic multi-level and multimodal features from multiple imaging modalities for classification, which are robust to the scale and rotation variations to some extent. No image segmentation and rigid registration are required in pre-processing the brain images. Our method is evaluated on the baseline MRI and PET images of 397 subjects including 93 AD patients, 204 mild cognitive impairment (MCI, 76 pMCI +128 sMCI) and 100 normal controls (NC) from Alzheimer's Disease Neuroimaging

  4. Reliable Fault Diagnosis of Rotary Machine Bearings Using a Stacked Sparse Autoencoder-Based Deep Neural Network

    Directory of Open Access Journals (Sweden)

    Muhammad Sohaib

    2018-01-01

    Full Text Available Due to enhanced safety, cost-effectiveness, and reliability requirements, fault diagnosis of bearings using vibration acceleration signals has been a key area of research over the past several decades. Many fault diagnosis algorithms have been developed that can efficiently classify faults under constant speed conditions. However, the performances of these traditional algorithms deteriorate with fluctuations of the shaft speed. In the past couple of years, deep learning algorithms have not only improved the classification performance in various disciplines (e.g., in image processing and natural language processing, but also reduced the complexity of feature extraction and selection processes. In this study, using complex envelope spectra and stacked sparse autoencoder- (SSAE- based deep neural networks (DNNs, a fault diagnosis scheme is developed that can overcome fluctuations of the shaft speed. The complex envelope spectrum made the frequency components associated with each fault type vibrant, hence helping the autoencoders to learn the characteristic features from the given input signals more readily. Moreover, the implementation of SSAE-DNN for bearing fault diagnosis has avoided the need of handcrafted features that are used in traditional fault diagnosis schemes. The experimental results demonstrate that the proposed scheme outperforms conventional fault diagnosis algorithms in terms of fault classification accuracy when tested with variable shaft speed data.

  5. Singapore Cancer Network (SCAN) Guidelines for the Initial Evaluation, Diagnosis, and Management of Extremity Soft Tissue Sarcoma and Osteosarcoma.

    Science.gov (United States)

    2015-10-01

    The SCAN sarcoma workgroup aimed to develop Singapore Cancer Network (SCAN) clinical practice guidelines for the initial evaluation, diagnosis, and management of extremity soft tissue sarcoma and osteosarcoma. The workgroup utilised a consensus approach to create high quality evidence-based clinical practice guidelines suited for our local setting. Various international guidelines from the fields of radiology, pathology, orthopaedic surgery, medical, radiation and paediatric oncology were reviewed, including those developed by von Mehren Metal (J Natl Compr Canc Netw 2014), the National Collaborating Centre for Cancer (2006), the European Sarcoma Network Working Group (2012) and Grimer RJ et al (Sarcoma 2008). Our clinical practice guidelines contextualised to the local patient will streamline care and improve clinical outcomes for patients with extremity soft tissue and osteosarcoma. These guidelines form the SCAN Guidelines 2015 for the initial evaluation, diagnosis, and management of extremity soft tissue sarcoma and osteosarcoma.

  6. Cooperation in health: mapping collaborative networks on the web.

    Science.gov (United States)

    Lang, Pamela Barreto; Gouveia, Fábio Castro; Leta, Jacqueline

    2013-01-01

    To map and investigate the relationships established on the web between leading health-research institutions around the world. Sample selection was based on the World Health Organization (WHO) Collaborating Centres (CCs). Data on the 768 active CCs in 89 countries were retrieved from the WHO's database. The final sample consisted of 190 institutions devoted to health sciences in 42 countries. Data on each institution's website were retrieved using webometric techniques (interlinking), and an asymmetric matrix was generated for social network analysis. The results showed that American and European institutions, such as the Centers for Disease Control and Prevention (CDC), the National Institutes of Health (NIH) and the National Institute of Health and Medical Research (INSERM), are the most highly connected on the web and have a higher capacity to attract hyperlinks. The Karolinska Institute (KI-SE) in Sweden is well placed as an articulation point between several integrants of the network and the component's core but lacks general recognition on the web by hyperlinks. Regarding the north-south divide, Mexico and Brazil appear to be key southern players on the web. The results showed that the hyperlinks exchanged between northern and southern countries present an abysmal gap: 99.49% of the hyperlinks provided by the North are directed toward the North itself, in contrast to 0.51% that are directed toward the South. Regarding the South, its institutions are more connected to its northern partners, with 98.46% of its hyperlinks directed toward the North, and mainly toward the United States, compared with 1.54% toward southern neighbors. It is advisable to strengthen integration policies on the web and to increase web networking through hyperlink exchange. In this way, the web could actually reflect international cooperation in health and help to legitimize and enhance the visibility of the many existing south-south collaboration networks.

  7. Cooperation in health: mapping collaborative networks on the web.

    Directory of Open Access Journals (Sweden)

    Pamela Barreto Lang

    Full Text Available OBJECTIVE: To map and investigate the relationships established on the web between leading health-research institutions around the world. METHODS: Sample selection was based on the World Health Organization (WHO Collaborating Centres (CCs. Data on the 768 active CCs in 89 countries were retrieved from the WHO's database. The final sample consisted of 190 institutions devoted to health sciences in 42 countries. Data on each institution's website were retrieved using webometric techniques (interlinking, and an asymmetric matrix was generated for social network analysis. FINDINGS: The results showed that American and European institutions, such as the Centers for Disease Control and Prevention (CDC, the National Institutes of Health (NIH and the National Institute of Health and Medical Research (INSERM, are the most highly connected on the web and have a higher capacity to attract hyperlinks. The Karolinska Institute (KI-SE in Sweden is well placed as an articulation point between several integrants of the network and the component's core but lacks general recognition on the web by hyperlinks. Regarding the north-south divide, Mexico and Brazil appear to be key southern players on the web. The results showed that the hyperlinks exchanged between northern and southern countries present an abysmal gap: 99.49% of the hyperlinks provided by the North are directed toward the North itself, in contrast to 0.51% that are directed toward the South. Regarding the South, its institutions are more connected to its northern partners, with 98.46% of its hyperlinks directed toward the North, and mainly toward the United States, compared with 1.54% toward southern neighbors. CONCLUSION: It is advisable to strengthen integration policies on the web and to increase web networking through hyperlink exchange. In this way, the web could actually reflect international cooperation in health and help to legitimize and enhance the visibility of the many existing south

  8. Social networks and mental health among people living with human immunodeficiency virus (HIV) in Johannesburg, South Africa.

    Science.gov (United States)

    Odek, Willis Omondi

    2014-01-01

    People living with human immunodeficiency virus (PLHIV) in developing countries can live longer due to improved treatment access, and a deeper understanding of determinants of their quality of life is critical. This study assessed the link between social capital, operationally defined in terms of social networks (group-based and personal social networks) and access to network resources (access to material and non-material resources and social support) and health-related quality of life (HRQoL) among 554 (55% female) adults on HIV treatment through South Africa's public health system. Female study participants were involved with more group-based social networks but had fewer personal social networks in comparison to males. Access to network resources was higher among females and those from larger households but lower among older study participants. Experience of social support significantly increased with household economic status and duration at current residence. Social capital indicators were unrelated to HIV disease status indicators, including duration since diagnosis, CD4 count and viral load. Only a minority (13%) of study participants took part in groups formed by and for predominantly PLHIV (HIV support groups), and participation in such groups was unrelated to their mental or physical health. Personal rather than group-linked social networks and access to network resources were significantly associated with mental but not physical health, after controlling for sociodemographic characteristics. The findings of limited participation in HIV support groups and that the participation in such groups was not significantly associated with physical or mental health may suggest efforts among PLHIV in South Africa to normalise HIV as a chronic illness through broad-based rather than HIV-status bounded social participation, as a strategy for deflecting stigma. Further research is required to examine the effects of HIV treatment on social networking and participation

  9. Mayo Clinic Care Network: A Collaborative Health Care Model.

    Science.gov (United States)

    Wald, John T; Lowery-Schrandt, Sherri; Hayes, David L; Kotsenas, Amy L

    2018-01-01

    By leveraging its experience and expertise as a consultative clinical partner, the Mayo Clinic developed an innovative, scalable care model to accomplish several strategic goals: (1) create and sustain high-value relationships that benefit patients and providers, (2) foster relationships with like-minded partners to act as a strategy against the development of narrow health care networks, and (3) increase national and international brand awareness of Mayo Clinic. The result was the Mayo Clinic Care Network. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

  10. Development of an online tool for public health: the European Public Health Law Network.

    Science.gov (United States)

    Basak, P

    2011-09-01

    The European Public Health Law Network was established in 2007 as part of the European Union (EU) co-funded Public Health Law Flu project. The aims of the website consisted of designing an interactive network of specialist information and encouraging an exchange of expertise amongst members. The website sought to appeal to academics, public health professionals and lawyers. The Public Health Law Flu project team designed and managed the website. Registered network members were recruited through publicity, advertising and word of mouth. Details of the network were sent to health organizations and universities throughout Europe. Corresponding website links attracted many new visitors. Publications, news, events and a pandemic glossary became popular features on the site. Although the website initially focused only on pandemic diseases it has grown into a multidisciplinary website covering a range of public health law topics. The network contains over 700 publications divided into 28 public health law categories. News, events, front page content, legislation and the francophone section are updated on a regular basis. Since 2007 the website has received over 15,000 views from 156 countries. Newsletter subscribers have risen to 304. There are now 723 followers on the associated Twitter site. The European Public Health Law Network has been a successful and innovative site in the area of public health law. Interest in the site continues to grow. Future funding can contribute to a bigger site with interactive features and pages in a wider variety of languages to attract a wider global audience. Copyright © 2011 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

  11. Health Care Engagement Among LGBT Older Adults: The Role of Depression Diagnosis and Symptomatology.

    Science.gov (United States)

    Shiu, Chengshi; Kim, Hyun-Jun; Fredriksen-Goldsen, Karen

    2017-02-01

    Optimal engagement in health care plays a critical role in the success of disease prevention and treatment, particularly for older adults who are often in greater need of health care services. However, to date, there is still limited knowledge about the relationship between depression and health care engagement among lesbian, gay, bisexual, and transgender (LGBT) older adults. This study utilized data from Aging with Pride: National Health, Aging, Sexuality/Gender Study, from the 2014 survey with 2,450 LGBT adults 50 years old and older. Multiple-variable regression was utilized to evaluate relationships between three indicators of health care engagement and four depression groups after controlling for background characteristics and discrimination in health care. Health care engagement indicators were "not using preventive care," "not seeking care when needed," and "difficulty in adhering to treatments." Depression groups were defined by depression diagnosis and symptomatology, including Diagnosed-Symptomatic group (Diag-Sympt), Diagnosed-Nonsymptomatic group (Diag-NoSympt), Nondiagnosed-Symptomatic group (NoDiag-Sympt), and Nondiagnosed-Nonsymptomatic group (NoDiag-NoSympt). Depression groups displayed different patterns and levels of health care engagement. The Diag-Sympt group displayed the highest "difficulty in adhering to treatments." Diag-NoSympt group displayed the lowest "not using preventive care." The NoDiag-Sympt group reported the highest "not using preventive care" and "not seeking care when needed." The NoDiag-NoSympt group had the lowest "not seeking care when needed" and "difficulty in adhering to treatments." Depression diagnosis and symptomatology are jointly associated with health care engagement among LGBT older adults. Interventions aiming to promote health care engagement among this population should simultaneously consider both depression diagnosis and symptomatology. © The Author 2017. Published by Oxford University Press on behalf of The

  12. Reducing age of autism diagnosis: developmental social neuroscience meets public health challenge.

    Science.gov (United States)

    Klin, Ami; Klaiman, Cheryl; Jones, Warren

    2015-02-25

    Autism spectrum disorder (autism) is a highly prevalent and heterogeneous family of neurodevelopmental disorders of genetic origins with potentially devastating implications for child, family, health and educational systems. Despite advances in paper-and-pencil screening and in standardization of diagnostic procedures, diagnosis of autism in the US still hovers around the ages of four or five years, later still in disadvantaged communities, and several years after the age of two to three years when the condition can be reliably diagnosed by expert clinicians. As early detection and treatment are two of the most important factors optimizing outcome, and given that diagnosis is typically a necessary condition for families to have access to early treatment, reducing age of diagnosis has become one of the greatest priorities of the field. Recent advances in developmental social neuroscience promise the advent of cost-effective and community-viable, performance-based procedures, and suggest a complementary method for promoting universal screening and much greater access to the diagnosis process. Small but critical studies have already reported on experiments that differentiate groups of children at risk for autism from controls, and at least one study so far could predict diagnostic classification and level of disability on the basis of a brief experiment. Although the road to translating such procedures into effective devices for screening and diagnosis is still a long one, and premature claims should be avoided, this effort could be critical in addressing this worldwide public health challenge.

  13. Research on Fault Diagnosis for Pumping Station Based on T-S Fuzzy Fault Tree and Bayesian Network

    Directory of Open Access Journals (Sweden)

    Zhuqing Bi

    2017-01-01

    Full Text Available According to the characteristics of fault diagnosis for pumping station, such as the complex structure, multiple mappings, and numerous uncertainties, a new approach combining T-S fuzzy gate fault tree and Bayesian network (BN is proposed. On the one hand, traditional fault tree method needs the logical relationship between events and probability value of events and can only represent the events with two states. T-S fuzzy gate fault tree method can solve these disadvantages but still has weaknesses in complex reasoning and only one-way reasoning. On the other hand, the BN is suitable for fault diagnosis of pumping station because of its powerful ability to deal with uncertain information. However, it is difficult to determine the structure and conditional probability tables of the BN. Therefore, the proposed method integrates the advantages of the two methods. Finally, the feasibility of the method is verified through a fault diagnosis model of the rotor in the pumping unit, the accuracy of the method is verified by comparing with the methods based on traditional Bayesian network and BP neural network, respectively, when the historical data is sufficient, and the results are more superior to the above two when the historical data is insufficient.

  14. Diagnosis of compliance of health care product processing in Primary Health Care.

    Science.gov (United States)

    Roseira, Camila Eugenia; Silva, Darlyani Mariano da; Passos, Isis Pienta Batista Dias; Orlandi, Fabiana Souza; Padoveze, Maria Clara; Figueiredo, Rosely Moralez de

    2016-11-21

    identify the compliance of health care product processing in Primary Health Care and assess possible differences in the compliance among the services characterized as Primary Health Care Service and Family Health Service. quantitative, observational, descriptive and inferential study with the application of structure, process and outcome indicators of the health care product processing at ten services in an interior city of the State of São Paulo - Brazil. for all indicators, the compliance indices were inferior to the ideal levels. No statistically significant difference was found in the indicators between the two types of services investigated. The health care product cleaning indicators obtained the lowest compliance index, while the indicator technical-operational resources for the preparation, conditioning, disinfection/sterilization, storage and distribution of health care products obtained the best index. the diagnosis of compliance of health care product processing at the services assessed indicates that the quality of the process is jeopardized, as no results close to ideal levels were obtained at any service. In addition, no statistically significant difference in these indicators was found between the two types of services studied. identificar a conformidade do processamento de produtos para saúde na Atenção Primária à Saúde e avaliar possível diferença na conformidade entre as unidades caracterizadas como Unidade Básica de Saúde e Unidade Saúde da Família. estudo quantitativo, observacional, descritivo e inferencial, com a aplicação de indicadores de estrutura, processo e resultado referentes ao processamento de produtos para a saúde em dez unidades, de um município do interior de São Paulo - Brasil. todos os indicadores obtiveram índice de conformidade inferior ao ideal. Não houve diferença estatisticamente significante nos indicadores entre os dois tipos de unidades investigadas, sendo o indicador de limpeza de produtos para sa

  15. Validation of organ procurement and transplant network (OPTN)/united network for organ sharing (UNOS) criteria for imaging diagnosis of hepatocellular carcinoma.

    Science.gov (United States)

    Fowler, Kathryn J; Karimova, E Jane; Arauz, Anthony R; Saad, Nael E; Brunt, Elizabeth M; Chapman, William C; Heiken, Jay P

    2013-06-27

    Imaging diagnosis of hepatocellular carcinoma (HCC) presents an important pathway for transplant exception points and priority for cirrhotic patients. The purpose of this retrospective study is to evaluate the validity of the new Organ Procurement and Transplant Network (OPTN) classification system on patients undergoing transplantation for HCC. One hundred twenty-nine patients underwent transplantation for HCC from April 14, 2006 to April 18, 2011; a total of 263 lesions were reported as suspicious for HCC on pretransplantation magnetic resonance imaging. Magnetic resonance imaging examinations were reviewed independently by two experienced radiologists, blinded to final pathology. Reviewers identified major imaging features and an OPTN classification was assigned to each lesion. Final proof of diagnosis was pathology on explant or necrosis along with imaging findings of ablation after transarterial chemoembolization. Application of OPTN imaging criteria in our population resulted in high specificity for the diagnosis of HCC. Sensitivity in diagnosis of small lesions (≥1 and based on preoperative imaging but would not have met criteria under the new system. Eleven percent of the patients not meeting OPTN criteria were found to have T2 stage tumor burden on pathology. The OPTN imaging policy introduces a high level of specificity for HCC but may decrease sensitivity for small lesions. Management may be impacted in a number of patients, potentially requiring longer surveillance periods or biopsy to confirm diagnosis.

  16. The city, territoriality and networks in mental health policies

    Directory of Open Access Journals (Sweden)

    Luciana Assis Costa

    2014-09-01

    Full Text Available The understanding of territory, made evident by a decentralized, local based, and non-institutionalized mental health model, is a fundamental element in building a renewed network. The objective of this essay is to understand how mental health policies gradually favor local actions, organized in terms of territories, to develop strategies of care that support the new model of mental health. From this perspective, the aim of this research is to reflect on the possibilities of establishing new social relations that can, in fact, widen the sense of community belonging in the daily living of those presenting mental health conditions. This study draws from theoretical concepts and frameworks of the social sciences, describing the diverse positions held by the main schools of urban sociology with regards to the understanding of territories. The multiple conceptions of territories and their relations to mental health are analyzed. Historical data about mental health in Brazil show a heterogeneous development of mental health policies in different areas of the country. Finally, social inclusion in the cities depends on an effective expansion of territory-based mental health services, as well as an amplification of the access to consumer goods and services not necessarily connected to health care, but to basic social and civil rights. Hopefully, new rules of social interaction will not be restricted to the mental health universe, but will promote new encounters in the urban space, with respect for differences and appreciation of diversity.

  17. Is diagnosis enough to guide interventions in mental health? Using case formulation in clinical practice

    Directory of Open Access Journals (Sweden)

    Macneil Craig A

    2012-09-01

    Full Text Available Abstract While diagnosis has traditionally been viewed as an essential concept in medicine, particularly when selecting treatments, we suggest that the use of diagnosis alone may be limited, particularly within mental health. The concept of clinical case formulation advocates for collaboratively working with patients to identify idiosyncratic aspects of their presentation and select interventions on this basis. Identifying individualized contributing factors, and how these could influence the person's presentation, in addition to attending to personal strengths, may allow the clinician a deeper understanding of a patient, result in a more personalized treatment approach, and potentially provide a better clinical outcome.

  18. Social networks, substance use, and mental health in college students.

    Science.gov (United States)

    Mason, Michael J; Zaharakis, Nikola; Benotsch, Eric G

    2014-01-01

    The relationship between social network risk (alcohol-using close friends), perceived peer closeness, substance use, and psychiatric symptoms was examined to identify risk and protective features of college students' social context. Six hundred and seventy undergraduate students enrolled in a large southeastern university. An online survey was administered to consenting students. Students with risky networks were at a 10-fold increase of hazardous drinking, 6-fold increase for weekly marijuana use, and 3-fold increase for weekly tobacco use. College students' who feel very close to their peers were protected against psychiatric symptoms yet were at increased risk for marijuana use. Perceived closeness of peers was highly protective against psychiatric symptoms, adding a natural preventive effect for a population at great risk for mental illness. RESULTS support targeting college students through network-oriented preventive interventions to address substance use as well as mental health.

  19. Study on intelligence fault diagnosis method for nuclear power plant equipment based on rough set and fuzzy neural network

    International Nuclear Information System (INIS)

    Liu Yongkuo; Xia Hong; Xie Chunli; Chen Zhihui; Chen Hongxia

    2007-01-01

    Rough set theory and fuzzy neural network are combined, to take full advantages of the two of them. Based on the reduction technology to knowledge of Rough set method, and by drawing the simple rule from a large number of initial data, the fuzzy neural network was set up, which was with better topological structure, improved study speed, accurate judgment, strong fault-tolerant ability, and more practical. In order to test the validity of the method, the inverted U-tubes break accident of Steam Generator and etc are used as examples, and many simulation experiments are performed. The test result shows that it is feasible to incorporate the fault intelligence diagnosis method based on rough set and fuzzy neural network in the nuclear power plant equipment, and the method is simple and convenience, with small calculation amount and reliable result. (authors)

  20. Methodology developed for the energy-productive diagnosis and evaluation in health buildings

    Energy Technology Data Exchange (ETDEWEB)

    Martini, I.; Discoli, C.; Rosenfeld, E. [Instituto de Estudios del Habitat (IDEHAB), Facultad de Arquitectura y Urbanismo, Universidad Nacional de La Plata, La Plata, Buenos Aires (Argentina)

    2007-07-01

    The public health network in Argentina consists of a wide variety of buildings presenting a complex system of services and structures. In order to modulate and study the energy behaviour of each type of health facility, a database of Energy-Productive Building Modules (Modulos Edilicios Energeticos Productivos: MEEP) was built. This involved evaluating the interactions among physical spaces, building envelope, infrastructure, and equipment usage with the energy consumption, for each specialty service provided in the most common buildings present in the health service network. The MEEP database enables investigators to: (i) Obtain detailed information on each facility. (ii) Identify variables critical to an energy consumption perspective. (iii) Detect areas of over consumption and/or inadequate infrastructure. (iv) Gather essential reference material for the design of health facilities and other similar sectors. The information of each MEEP can be summarized in typological charts. (author)

  1. City networks collaboration and planning for health and sustainability

    CERN Document Server

    Migdalas, Athanasios; Rassia, Stamatina; Pardalos, Panos

    2017-01-01

    Sustainable development within urban and rural areas, transportation systems, logistics, supply chain management, urban health, social services, and architectural design are taken into consideration in the cohesive network models provided in this book. The ideas, methods, and models presented consider city landscapes and quality of life conditions based on mathematical network models and optimization. Interdisciplinary Works from prominent researchers in mathematical modeling, optimization, architecture, engineering, and physics are featured in this volume to promote health and well-being through design.   Specific topics include: -          Current technology that form the basis of future living in smart cities -          Interdisciplinary design and networking of large-scale urban systems  -          Network communication and route traffic optimization -          Carbon dioxide emission reduction -          Closed-loop logistics chain management and operation ...

  2. Investigation of Wireless Sensor Networks for Structural Health Monitoring

    Directory of Open Access Journals (Sweden)

    Ping Wang

    2012-01-01

    Full Text Available Wireless sensor networks (WSNs are one of the most able technologies in the structural health monitoring (SHM field. Through intelligent, self-organising means, the contents of this paper will test a variety of different objects and different working principles of sensor nodes connected into a network and integrated with data processing functions. In this paper the key issues of WSN applied in SHM are discussed, including the integration of different types of sensors with different operational modalities, sampling frequencies, issues of transmission bandwidth, real-time ability, and wireless transmitter frequency. Furthermore, the topology, data fusion, integration, energy saving, and self-powering nature of different systems will be investigated. In the FP7 project “Health Monitoring of Offshore Wind Farms,” the above issues are explored.

  3. Your Health Buddies Matter: Preferential Selection and Social Influence on Weight Management in an Online Health Social Network.

    Science.gov (United States)

    Meng, Jingbo

    2016-12-01

    A growing number of online social networks are designed with the intention to promote health by providing virtual space wherein individuals can seek and share information and support with similar others. Research has shown that real-world social networks have a significant influence on one's health behavior and outcomes. However, there is a dearth of studies on how individuals form social networks in virtual space and whether such online social networks exert any impact on individuals' health outcomes. Built on the Multi-Theoretical Multilevel (MTML) framework and drawing from literature on social influence, this study examined the mechanisms underlying the formation of an online health social network and empirically tested social influence on individual health outcomes through the network. Situated in a weight management social networking site, the study tracked a health buddy network of 709 users and their weight management activities and outcomes for 4 months. Actor-based modeling was used to test the joint dynamics of preferential selection and social influence among health buddies. The results showed that baseline, inbreeding, and health status homophily significantly predicted preferential selection of health buddies in the weight management social networking site, whereas self-interest in seeking experiential health information did not. The study also found peer influence of online health buddy networks on individual weight outcomes, such that an individual's odds of losing weight increased if, on average, the individual's health buddies were losing weight.

  4. Social networks and health: a systematic review of sociocentric network studies in low- and middle-income countries.

    Science.gov (United States)

    Perkins, Jessica M; Subramanian, S V; Christakis, Nicholas A

    2015-01-01

    In low- and middle-income countries (LMICs), naturally occurring social networks may be particularly vital to health outcomes as extended webs of social ties often are the principal source of various resources. Understanding how social network structure, and influential individuals within the network, may amplify the effects of interventions in LMICs, by creating, for example, cascade effects to non-targeted participants, presents an opportunity to improve the efficiency and effectiveness of public health interventions in such settings. We conducted a systematic review of PubMed, Econlit, Sociological Abstracts, and PsycINFO to identify a sample of 17 sociocentric network papers (arising from 10 studies) that specifically examined health issues in LMICs. We also separately selected to review 19 sociocentric network papers (arising from 10 other studies) on development topics related to wellbeing in LMICs. First, to provide a methodological resource, we discuss the sociocentric network study designs employed in the selected papers, and then provide a catalog of 105 name generator questions used to measure social ties across all the LMIC network papers (including both ego- and sociocentric network papers) cited in this review. Second, we show that network composition, individual network centrality, and network structure are associated with important health behaviors and health and development outcomes in different contexts across multiple levels of analysis and across distinct network types. Lastly, we highlight the opportunities for health researchers and practitioners in LMICs to 1) design effective studies and interventions in LMICs that account for the sociocentric network positions of certain individuals and overall network structure, 2) measure the spread of outcomes or intervention externalities, and 3) enhance the effectiveness and efficiency of aid based on knowledge of social structure. In summary, human health and wellbeing are connected through complex

  5. Social Networks and Health: A Systematic Review of Sociocentric Network Studies in Low- and Middle-Income Countries

    Science.gov (United States)

    Perkins, Jessica M; Subramanian, S V; Christakis, Nicholas A

    2015-01-01

    In low- and middle-income countries (LMICs), naturally occurring social networks may be particularly vital to health outcomes as extended webs of social ties often are the principal source of various resources. Understanding how social network structure, and influential individuals within the network, may amplify the effects of interventions in LMICs, by creating, for example, cascade effects to non-targeted participants, presents an opportunity to improve the efficiency and effectiveness of public health interventions in such settings. We conducted a systematic review of PubMed, Econlit, Sociological Abstracts, and PsycINFO to identify a sample of 17 sociocentric network papers (arising from 10 studies) that specifically examined health issues in LMICs. We also separately selected to review 19 sociocentric network papers (arising from 10 other studies) on development topics related to wellbeing in LMICs. First, to provide a methodological resource, we discuss the sociocentric network study designs employed in the selected papers, and then provide a catalog of 105 name generator questions used to measure social ties across all the LMIC network papers (including both ego- and sociocentric network papers) cited in this review. Second, we show that network composition, individual network centrality, and network structure are associated with important health behaviors and health and development outcomes in different contexts across multiple levels of analysis and across distinct network types. Lastly, we highlight the opportunities for health researchers and practitioners in LMICs to 1) design effective studies and interventions in LMICs that account for the sociocentric network positions of certain individuals and overall network structure, 2) measure the spread of outcomes or intervention externalities, and 3) enhance the effectiveness and efficiency of aid based on knowledge of social structure. In summary, human health and wellbeing are connected through complex

  6. Bayesian Computational Sensor Networks for Aircraft Structural Health Monitoring

    Science.gov (United States)

    2016-02-02

    Virginia 22203 Air Force Research Laboratory Air Force Materiel Command 1 Final Performance Report: AFOSR T.C. Henderson , V.J. Mathews, and D...AFRL-AFOSR-VA-TR-2016-0094 Bayesian Computational Sensor Networks for Aircraft Structural Health Monitoring. Thomas Henderson UNIVERSITY OF UTAH SALT...The people who worked on this project include: Thomas C. Henderson , John Mathews, Jingru Zhou, Daimei Zhij, Ahmad Zoubi, Sabita Nahata, Dan Adams

  7. Why common carrier and network neutrality principles apply to the Nationwide Health Information Network (NWHIN).

    Science.gov (United States)

    Gaynor, Mark; Lenert, Leslie; Wilson, Kristin D; Bradner, Scott

    2014-01-01

    The Office of the National Coordinator will be defining the architecture of the Nationwide Health Information Network (NWHIN) together with the proposed HealtheWay public/private partnership as a development and funding strategy. There are a number of open questions--for example, what is the best way to realize the benefits of health information exchange? How valuable are regional health information organizations in comparison with a more direct approach? What is the role of the carriers in delivering this service? The NWHIN is to exist for the public good, and thus shares many traits of the common law notion of 'common carriage' or 'public calling,' the modern term for which is network neutrality. Recent policy debates in Congress and resulting potential regulation have implications for key stakeholders within healthcare that use or provide services, and for those who exchange information. To date, there has been little policy debate or discussion about the implications of a neutral NWHIN. This paper frames the discussion for future policy debate in healthcare by providing a brief education and summary of the modern version of common carriage, of the key stakeholder positions in healthcare, and of the potential implications of the network neutrality debate within healthcare.

  8. Satellite -Based Networks for U-Health & U-Learning

    Science.gov (United States)

    Graschew, G.; Roelofs, T. A.; Rakowsky, S.; Schlag, P. M.

    2008-08-01

    The use of modern Information and Communication Technologies (ICT) as enabling tools for healthcare services (eHealth) introduces new ways of creating ubiquitous access to high-level medical care for all, anytime and anywhere (uHealth). Satellite communication constitutes one of the most flexible methods of broadband communication offering high reliability and cost-effectiveness of connections meeting telemedicine communication requirements. Global networks and the use of computers for educational purposes stimulate and support the development of virtual universities for e-learning. Especially real-time interactive applications can play an important role in tailored and personalised services.

  9. Making health policy: networks in research and policy after 1945.

    Science.gov (United States)

    Berridge, Virginia

    2005-01-01

    Science and policy in health and medicine have interacted in new ways in Britain since 1945. The relationship between research and policy has a history. The changing role of social medicine, the rise of health services research and "customer contractor" policies in government have been important. The relationship between research and policy has been analysed by different schools of thought. This chapter categorises them as several groups: "evidence-based", "journalism", "sociology of scientific knowledge" and "science policy studies". The chapters in the book illuminate aspects of these changing relationships. The role of chronic disease epidemiology, of new networks in public health, of media-focussed activism, and of health technology and its advocates have been more important than political interest.

  10. Family health history communication networks of older adults: importance of social relationships and disease perceptions.

    Science.gov (United States)

    Ashida, Sato; Kaphingst, Kimberly A; Goodman, Melody; Schafer, Ellen J

    2013-10-01

    Older individuals play a critical role in disseminating family health history (FHH) information that can facilitate disease prevention among younger family members. This study evaluated the characteristics of older adults and their familial networks associated with two types of communication (have shared and intend to share new FHH information with family members) to inform public health efforts to facilitate FHH dissemination. Information on 970 social network members enumerated by 99 seniors (aged 57 years and older) at 3 senior centers in Memphis, Tennessee, through face-to-face interviews was analyzed. Participants shared FHH information with 27.5% of the network members; 54.7% of children and 24.4% of siblings. Two-level logistic regression models showed that participants had shared FHH with those to whom they provided emotional support (odds ratio [OR] = 1.836) and felt close to (OR = 1.757). Network-members were more likely to have received FHH from participants with a cancer diagnosis (OR = 2.617) and higher familiarity with (OR = 1.380) and importance of sharing FHH with family (OR = 1.474). Participants intended to share new FHH with those who provide tangible support to (OR = 1.804) and were very close to them (OR = 2.112). Members with whom participants intend to share new FHH were more likely to belong to the network of participants with higher perceived severity if family members encountered heart disease (OR = 1.329). Many first-degree relatives were not informed of FHH. Perceptions about FHH and disease risk as well as quality of social relationships may play roles in whether seniors communicate FHH with their families. Future studies may consider influencing these perceptions and relationships.

  11. Intelligent Wireless Sensor Networks for System Health Monitoring

    Science.gov (United States)

    Alena, Rick

    2011-01-01

    Wireless sensor networks (WSN) based on the IEEE 802.15.4 Personal Area Network (PAN) standard are finding increasing use in the home automation and emerging smart energy markets. The network and application layers, based on the ZigBee 2007 Standard, provide a convenient framework for component-based software that supports customer solutions from multiple vendors. WSNs provide the inherent fault tolerance required for aerospace applications. The Discovery and Systems Health Group at NASA Ames Research Center has been developing WSN technology for use aboard aircraft and spacecraft for System Health Monitoring of structures and life support systems using funding from the NASA Engineering and Safety Center and Exploration Technology Development and Demonstration Program. This technology provides key advantages for low-power, low-cost ancillary sensing systems particularly across pressure interfaces and in areas where it is difficult to run wires. Intelligence for sensor networks could be defined as the capability of forming dynamic sensor networks, allowing high-level application software to identify and address any sensor that joined the network without the use of any centralized database defining the sensors characteristics. The IEEE 1451 Standard defines methods for the management of intelligent sensor systems and the IEEE 1451.4 section defines Transducer Electronic Datasheets (TEDS), which contain key information regarding the sensor characteristics such as name, description, serial number, calibration information and user information such as location within a vehicle. By locating the TEDS information on the wireless sensor itself and enabling access to this information base from the application software, the application can identify the sensor unambiguously and interpret and present the sensor data stream without reference to any other information. The application software is able to read the status of each sensor module, responding in real-time to changes of

  12. Frequent Surfing on Social Health Networks is Associated With Increased Knowledge and Patient Health Activation.

    Science.gov (United States)

    Grosberg, Dafna; Grinvald, Haya; Reuveni, Haim; Magnezi, Racheli

    2016-08-10

    The advent of the Internet has driven a technological revolution that has changed our lives. As part of this phenomenon, social networks have attained a prominent role in health care. A variety of medical services is provided over the Internet, including home monitoring, interactive communications between the patient and service providers, and social support, among others. This study emphasizes some of the practical implications of Web-based health social networks for patients and for health care systems. The objective of this study was to assess how participation in a social network among individuals with a chronic condition contributed to patient activation, based on the Patient Activation Measure (PAM). A prospective, cross-sectional survey with a retrospective component was conducted. Data were collected from Camoni, a Hebrew-language Web-based social health network, participants in the diabetes mellitus, pain, hypertension, and depression/anxiety forums, during November 2012 to 2013. Experienced users (enrolled at least 6 months) and newly enrolled received similar versions of the same questionnaire including sociodemographics and PAM. Among 686 participants, 154 of 337 experienced and 123 of 349 newly enrolled completed the questionnaire. Positive correlations (Psocial relationships, and chronic disease knowledge. Men surfed longer than women (χ²3=10.104, Psocial health network use were correlated with increased knowledge about a chronic disease. Experienced surfers had higher PAM than newly enrolled, suggesting that continued site use may contribute to increased activation. Web-based social health networks offer an opportunity to expand patient knowledge and increase involvement in personal health, thereby increasing patient activation. Further studies are needed to examine these changes on other aspects of chronic illnesses such as quality of life and costs.

  13. Parasite-based malaria diagnosis: are health systems in Uganda equipped enough to implement the policy?

    Science.gov (United States)

    Kyabayinze, Daniel J; Achan, Jane; Nakanjako, Damalie; Mpeka, Betty; Mawejje, Henry; Mugizi, Rukaaka; Kalyango, Joan N; D'Alessandro, Umberto; Talisuna, Ambrose; Jean-Pierre, Van geertruyden

    2012-08-24

    Malaria case management is a key strategy for malaria control. Effective coverage of parasite-based malaria diagnosis (PMD) remains limited in malaria endemic countries. This study assessed the health system's capacity to absorb PMD at primary health care facilities in Uganda. In a cross sectional survey, using multi-stage cluster sampling, lower level health facilities (LLHF) in 11 districts in Uganda were assessed for 1) tools, 2) skills, 3) staff and infrastructure, and 4) structures, systems and roles necessary for the implementing of PMD. Tools for PMD (microscopy and/or RDTs) were available at 30 (24%) of the 125 LLHF. All LLHF had patient registers and 15% had functional in-patient facilities. Three months' long stock-out periods were reported for oral and parenteral quinine at 39% and 47% of LLHF respectively. Out of 131 health workers interviewed, 86 (66%) were nursing assistants; 56 (43%) had received on-job training on malaria case management and 47 (36%) had adequate knowledge in malaria case management. Overall, only 18% (131/730) Ministry of Health approved staff positions were filled by qualified personnel and 12% were recruited or transferred within six months preceding the survey. Of 186 patients that received referrals from LLHF, 130(70%) had received pre-referral anti-malarial drugs, none received pre-referral rectal artesunate and 35% had been referred due to poor response to antimalarial drugs. Primary health care facilities had inadequate human and infrastructural capacity to effectively implement universal parasite-based malaria diagnosis. The priority capacity building needs identified were: 1) recruitment and retention of qualified staff, 2) comprehensive training of health workers in fever management, 3) malaria diagnosis quality control systems and 4) strengthening of supply chain, stock management and referral systems.

  14. The Central American Network for Disaster and Health Information.

    Science.gov (United States)

    Arnesen, Stacey J; Cid, Victor H; Scott, John C; Perez, Ricardo; Zervaas, Dave

    2007-07-01

    This paper describes an international outreach program to support rebuilding Central America's health information infrastructure after several natural disasters in the region, including Hurricane Mitch in 1998 and two major earthquakes in 2001. The National Library of Medicine joined forces with the Pan American Health Organization/World Health Organization, the United Nations International Strategy for Disaster Reduction, and the Regional Center of Disaster Information for Latin America and the Caribbean (CRID) to strengthen libraries and information centers in Central America and improve the availability of and access to health and disaster information in the region by developing the Central American Network for Disaster and Health Information (CANDHI). Through CRID, the program created ten disaster health information centers in medical libraries and disaster-related organizations in six countries. This project served as a catalyst for the modernization of several medical libraries in Central America. The resulting CANDHI provides much needed electronic access to public health "gray literature" on disasters, as well as access to numerous health information resources. CANDHI members assist their institutions and countries in a variety of disaster preparedness activities through collecting and disseminating information.

  15. International Voluntary Health Networks (IVHNs). A social-geographical framework.

    Science.gov (United States)

    Reid, Benet; Laurie, Nina; Smith, Matt Baillie

    2018-03-01

    Trans-national medicine, historically associated with colonial politics, is now central to discourses of global health and development, thrust into mainstream media by catastrophic events (earthquakes, disease epidemics), and enshrined in the 2015 Sustainable Development Goals. Volunteer human-resource is an important contributor to international health-development work. International Voluntary Health Networks (IVHNs, that connect richer and poorer countries through healthcare) are situated at a meeting-point between geographies and sociologies of health. More fully developed social-geographic understandings will illuminate this area, currently dominated by instrumental health-professional perspectives. The challenge we address is to produce a geographically and sociologically-robust conceptual framework that appropriately recognises IVHNs' potentials for valuable impacts, while also unlocking spaces of constructive critique. We examine the importance of the social in health geography, and geographical potentials in health sociology (focusing on professional knowledge construction, inequality and capital, and power), to highlight the mutual interests of these two fields in relation to IVHNs. We propose some socio-geographical theories of IVHNs that do not naturalise inequality, that understand health as a form of capital, prioritise explorations of power and ethical practice, and acknowledge the more-than-human properties of place. This sets an agenda for theoretically-supported empirical work on IVHNs. Copyright © 2018 Elsevier Ltd. All rights reserved.

  16. Dental health economics and diagnosis related groups/casemix in Indonesian dentistry

    Directory of Open Access Journals (Sweden)

    Ronnie Rivany

    2009-12-01

    Full Text Available Background: Dental Health Economics is a branch of transdiciplinary science that refers to the Economic and Public Health science. On the other hand, in other developed countries, Diagnosis Related Groups (DRG’s /Casemix has been used as a basic in creating the same perception between providers, patients and insurance companies in many aspects such as health planning, healthcare financing and quality assurance. Purpose: The objective of this review is to propose a new paradigm of economics to be applied in Indonesian Dentistry. Reviews: The Dental Health Economics should be considered as an important aspect in Indonesian Dentistry, which is used to determine the dental treatment fee based on unit cost, cost containment, and cost recovery rate analysis. Referring to Australian Refined Diagnosis Related Group, health care industry in Indonesia has starting to try a more structured way in grouping disease pattern in order to come up with more precise health care services to their patients. The on going development of Indonesian DRG’s is meant to confirm the disease pattern and partition. Conclusion: The development of Indonesian DRG’s concept, especially the Dental & Oral Disorders, needs a new paradigm, so the practitioners and academics could group and calculate the unit cost from each dental treatment according to the Indonesian DRG version (INA-DRG’s.

  17. Occupational health and safety in the Moroccan construction sites: preliminary diagnosis

    Science.gov (United States)

    Tarik, Bakeli; Adil, Hafidi Alaoui

    2018-05-01

    Managing occupational health and safety on Moroccan construction sector represents the first step for projects' success. In fact, by avoiding accidents, all the related direct and indirect costs and delays can be prevented. That leads to an important question always asked by any project manager: what are the factors responsible for accidents? How can they be avoided? Through this research, the aim is to go through these questions, to contribute in occupational health and safety principles understanding, to identify construction accidentology and risk management opportunities and to approach the case of Moroccan construction sites by an accurate diagnosis. The approach is to make researchers, managers, stakeholders and deciders aware about the criticality of construction sites health and safety situation. And, to do the first step for a scientific research project in relation with health and safety in the Moroccan construction sector. For this, the paper will study the related state of art namely about construction sites accidents causation, and will focus on Reason's `Swiss cheese' model and its utilization for Moroccan construction sites health and safety diagnosis. The research will end with an estimation of an accidents fatality rate in the Moroccan construction sector and a benchmarking with the international rates. Finally, conclusions will be presented about the necessity of Occupational Health and Safety Management System (OHSMS) implementation, which shall cover all risk levels, and insure, at the same time, that the necessary defenses against accidents are on place.

  18. Can mental health interventions change social networks? A systematic review.

    Science.gov (United States)

    Anderson, Kimberley; Laxhman, Neelam; Priebe, Stefan

    2015-11-21

    Social networks of patients with psychosis can provide social support, and improve health and social outcomes, including quality of life. However, patients with psychosis often live rather isolated with very limited social networks. Evidence for interventions targeting symptoms or social skills, are largely unsuccessful at improving social networks indirectly. As an alternative, interventions may directly focus on expanding networks. In this systematic review, we assessed what interventions have previously been tested for this and to what extent they have been effective. A systematic review was conducted of randomised controlled trials, testing psychosocial interventions designed to directly increase the social networks of patients with psychosis. Searches of five online databases (PsycINFO, CINAHL, Cochrane Database, MEDLINE, Embase), hand searching of grey literature, and both forward and backward snowballing of key papers were conducted and completed on 12 December 2014. Trial reports were included if they were written in English, the social network size was the primary outcome, participants were ≥ 18 years old and diagnosed with a psychotic disorder. Five studies (n = 631 patients) met the complete inclusion criteria. Studies were from different countries and published since 2008. Four trials had significant positive results, i.e. an observable increase in patients' social network size at the end of the intervention. The interventions included: guided peer support, a volunteer partner scheme, supported engagement in social activity, dog-assisted integrative psychological therapy and psychosocial skills training. Other important elements featured were the presence of a professional, and a focus on friendships and peers outside of services and the immediate family. Despite the small number and heterogeneity of included studies, the results suggest that interventions directly targeting social isolation can be effective and achieve a meaningful increase

  19. Knowledge brokers in a knowledge network: the case of Seniors Health Research Transfer Network knowledge brokers.

    Science.gov (United States)

    Conklin, James; Lusk, Elizabeth; Harris, Megan; Stolee, Paul

    2013-01-09

    The purpose of this paper is to describe and reflect on the role of knowledge brokers (KBs) in the Seniors Health Research Transfer Network (SHRTN). The paper reviews the relevant literature on knowledge brokering, and then describes the evolving role of knowledge brokering in this knowledge network. The description of knowledge brokering provided here is based on a developmental evaluation program and on the experiences of the authors. Data were gathered through qualitative and quantitative methods, analyzed by the evaluators, and interpreted by network members who participated in sensemaking forums. The results were fed back to the network each year in the form of formal written reports that were widely distributed to network members, as well as through presentations to the network's members. The SHRTN evaluation and our experiences as evaluators and KBs suggest that a SHRTN KB facilitates processes of learning whereby people are connected with tacit or explicit knowledge sources that will help them to resolve work-related challenges. To make this happen, KBs engage in a set of relational, technical, and analytical activities that help communities of practice (CoPs) to develop and operate, facilitate exchanges among people with similar concerns and interests, and help groups and individuals to create, explore, and apply knowledge in their practice. We also suggest that the role is difficult to define, emergent, abstract, episodic, and not fully understood. The KB role within this knowledge network has developed and matured over time. The KB adapts to the social and technical affordances of each situation, and fashions a unique and relevant process to create relationships and promote learning and change. The ability to work with teams and to develop relevant models and feasible approaches are critical KB skills. The KB is a leader who wields influence rather than power, and who is prepared to adopt whatever roles and approaches are needed to bring about a valuable

  20. Health behaviour modelling for prenatal diagnosis in Australia: a geodemographic framework for health service utilisation and policy development

    Directory of Open Access Journals (Sweden)

    Halliday Jane L

    2006-09-01

    Full Text Available Abstract Background Despite the wide availability of prenatal screening and diagnosis, a number of studies have reported no decrease in the rate of babies born with Down syndrome. The objective of this study was to investigate the geodemographic characteristics of women who have prenatal diagnosis in Victoria, Australia, by applying a novel consumer behaviour modelling technique in the analysis of health data. Methods A descriptive analysis of data on all prenatal diagnostic tests, births (1998 and 2002 and births of babies with Down syndrome (1998 to 2002 was undertaken using a Geographic Information System and socioeconomic lifestyle segmentation classifications. Results Most metropolitan women in Victoria have average or above State average levels of uptake of prenatal diagnosis. Inner city women residing in high socioeconomic lifestyle segments who have high rates of prenatal diagnosis spend 20% more on specialist physician's fees when compared to those whose rates are average. Rates of prenatal diagnosis are generally low amongst women in rural Victoria, with the lowest rates observed in farming districts. Reasons for this are likely to be a combination of lack of access to services (remoteness and individual opportunity (lack of transportation, low levels of support and income. However, there are additional reasons for low uptake rates in farming areas that could not be explained by the behaviour modelling. These may relate to women's attitudes and choices. Conclusion A lack of statewide geodemographic consistency in uptake of prenatal diagnosis implies that there is a need to target health professionals and pregnant women in specific areas to ensure there is increased equity of access to services and that all pregnant women can make informed choices that are best for them. Equally as important is appropriate health service provision for families of children with Down syndrome. Our findings show that these potential interventions are

  1. A hybrid optic-fiber sensor network with the function of self-diagnosis and self-healing

    Science.gov (United States)

    Xu, Shibo; Liu, Tiegen; Ge, Chunfeng; Chen, Cheng; Zhang, Hongxia

    2014-11-01

    We develop a hybrid wavelength division multiplexing optical fiber network with distributed fiber-optic sensors and quasi-distributed FBG sensor arrays which detect vibrations, temperatures and strains at the same time. The network has the ability to locate the failure sites automatically designated as self-diagnosis and make protective switching to reestablish sensing service designated as self-healing by cooperative work of software and hardware. The processes above are accomplished by master-slave processors with the help of optical and wireless telemetry signals. All the sensing and optical telemetry signals transmit in the same fiber either working fiber or backup fiber. We take wavelength 1450nm as downstream signal and wavelength 1350nm as upstream signal to control the network in normal circumstances, both signals are sent by a light emitting node of the corresponding processor. There is also a continuous laser wavelength 1310nm sent by each node and received by next node on both working and backup fibers to monitor their healthy states, but it does not carry any message like telemetry signals do. When fibers of two sensor units are completely damaged, the master processor will lose the communication with the node between the damaged ones.However we install RF module in each node to solve the possible problem. Finally, the whole network state is transmitted to host computer by master processor. Operator could know and control the network by human-machine interface if needed.

  2. Oral health considerations in anorexia and bulimia nervosa. 1. Symptomatology and diagnosis.

    Science.gov (United States)

    Bassiouny, Mohamed A

    2017-01-01

    Eating disorders have captured the attention of medical and dental professionals as well as the public for decades and continue to raise concern today. The literature devoted to anorexia and bulimia highlights myriad psychological, systemic, and dental health complications. Dental practitioners are in a unique position to discover early manifestations of these disorders. The present article reviews anorexia and bulimia, summarizing telltale behavioral traits, systemic manifestations, and dental features to facilitate recognition and enable accurate diagnosis.

  3. Reducing age of autism diagnosis: developmental social neuroscience meets public health challenge

    OpenAIRE

    Klin, Ami; Klaiman, Cheryl; Jones, Warren

    2015-01-01

    Autism spectrum disorder (autism) is a highly prevalent and heterogeneous family of neurodevelopmental disorders of genetic origins with potentially devastating implications for child, family, health and educational systems. Despite advances in paper-and-pencil screening and in standardization of diagnostic procedures, diagnosis of autism in the US still hovers around the ages of four or five years, later still in disadvantaged communities, and several years after the age of two to three year...

  4. From diagnosis through survivorship: health-care experiences of colorectal cancer survivors with ostomies

    Science.gov (United States)

    Grant, Marcia; McMullen, Carmit K.; Altschuler, Andrea; Mohler, M. Jane; Hornbrook, Mark C.; Herrinton, Lisa J.; Krouse, Robert S.

    2014-01-01

    Purpose The journey from diagnosis through treatment to survivorship can be challenging for colorectal cancer (CRC) survivors with permanent ostomies. Memories of both the positive and negative health-care interactions can persist years after the initial diagnosis and treatment. The purpose of this paper is to describe the health-care experiences of long-term (>5 years) CRC survivors with ostomies. Methods Thirty-three CRC survivors with ostomies who were members of Kaiser Permanente, an integrated care organization, in Oregon, southwestern Washington and northern California participated in eight focus groups. Discussions from the focus groups were recorded, transcribed, and analyzed for potential categories and themes. Results Health-care-related themes described CRC survivors’ experiences with diagnosis, treatment decision-making, initial experiences with ostomy, and survivorship. Participants discussed both positive and negative health-care-related experiences, including the need for continued access to trained nurses for ostomy self-care, access to peer support, and resources related to managing persistent, debilitating symptoms. Conclusions Long-term CRC survivors with ostomies have both positive and negative health-care experiences, regardless of health-related quality of life (HRQOL) and gender. Long-term support mechanisms and quality survivorship care that CRC survivors with ostomies can access are needed to promote positive adjustments and improved HRQOL. Structured abstract The current literature in CRC survivor-ship suggests that HRQOL concerns can persist years after treatment completion. The coordination of care to manage persistent late- and long-term effects are still lacking for CRC survivors living with an ostomy. Findings from this qualitative analysis will aid in the development of support strategies that foster more positive adjustments for CRC survivors living with an ostomy and support their ongoing ostomy-related needs. PMID:24442998

  5. Avian Colibacillosis and Salmonellosis: A Closer Look at Epidemiology, Pathogenesis, Diagnosis, Control and Public Health Concerns

    Directory of Open Access Journals (Sweden)

    S. M. Lutful Kabir

    2010-01-01

    Full Text Available Avian colibacillosis and salmonellosis are considered to be the major bacterial diseases in the poultry industry world-wide. Colibacillosis and salmonellosis are the most common avian diseases that are communicable to humans. This article provides the vital information on the epidemiology, pathogenesis, diagnosis, control and public health concerns of avian colibacillosis and salmonellosis. A better understanding of the information addressed in this review article will assist the poultry researchers and the poultry industry in continuing to make progress in reducing and eliminating avian colibacillosis and salmonellosis from the poultry flocks, thereby reducing potential hazards to the public health posed by these bacterial diseases.

  6. Coordinators for health science libraries in the Midwest Health Science Library Network.

    Science.gov (United States)

    Holtum, E A; McKloskey, J; Mahan, R

    1977-04-01

    In the summer of 1973 one resource library in each of the six states of the Midwest Health Science Library Network received funding from the National Library of Medicine to hire a coordinator for health science libraries. The development of the role of coordinator is examined and evaluated. The coordinators have proved valuable in the areas of consortium formation, basic unit development, communication facilitation, and program initiation. The function of the coordinators in the extensive planning effort now being undertaken by the network and the future need for the coordinator positions are discussed.

  7. Single and combined fault diagnosis of reciprocating compressor valves using a hybrid deep belief network

    NARCIS (Netherlands)

    Tran, Van Tung; Thobiani, Faisal Al; Tinga, Tiedo; Ball, Andrew David; Niu, Gang

    2017-01-01

    In this paper, a hybrid deep belief network is proposed to diagnose single and combined faults of suction and discharge valves in a reciprocating compressor. This hybrid integrates the deep belief network structured by multiple stacked restricted Boltzmann machines for pre-training and simplified

  8. European health telematics networks for positron emission tomography

    Science.gov (United States)

    Kontaxakis, George; Pozo, Miguel Angel; Ohl, Roland; Visvikis, Dimitris; Sachpazidis, Ilias; Ortega, Fernando; Guerra, Pedro; Cheze-Le Rest, Catherine; Selby, Peter; Pan, Leyun; Diaz, Javier; Dimitrakopoulou-Strauss, Antonia; Santos, Andres; Strauss, Ludwig; Sakas, Georgios

    2006-12-01

    A pilot network of positron emission tomography centers across Europe has been setup employing telemedicine services. The primary aim is to bring all PET centers in Europe (and beyond) closer, by integrating advanced medical imaging technology and health telematics networks applications into a single, easy to operate health telematics platform, which allows secure transmission of medical data via a variety of telecommunications channels and fosters the cooperation between professionals in the field. The platform runs on PCs with Windows 2000/XP and incorporates advanced techniques for image visualization, analysis and fusion. The communication between two connected workstations is based on a TCP/IP connection secured by secure socket layers and virtual private network or jabber protocols. A teleconsultation can be online (with both physicians physically present) or offline (via transmission of messages which contain image data and other information). An interface sharing protocol enables online teleconsultations even over low bandwidth connections. This initiative promotes the cooperation and improved communication between nuclear medicine professionals, offering options for second opinion and training. It permits physicians to remotely consult patient data, even if they are away from the physical examination site.

  9. European health telematics networks for positron emission tomography

    International Nuclear Information System (INIS)

    Kontaxakis, George; Pozo, Miguel Angel; Ohl, Roland; Visvikis, Dimitris; Sachpazidis, Ilias; Ortega, Fernando; Guerra, Pedro; Cheze-Le Rest, Catherine; Selby, Peter; Pan, Leyun; Diaz, Javier; Dimitrakopoulou-Strauss, Antonia; Santos, Andres; Strauss, Ludwig; Sakas, Georgios

    2006-01-01

    A pilot network of positron emission tomography centers across Europe has been setup employing telemedicine services. The primary aim is to bring all PET centers in Europe (and beyond) closer, by integrating advanced medical imaging technology and health telematics networks applications into a single, easy to operate health telematics platform, which allows secure transmission of medical data via a variety of telecommunications channels and fosters the cooperation between professionals in the field. The platform runs on PCs with Windows 2000/XP and incorporates advanced techniques for image visualization, analysis and fusion. The communication between two connected workstations is based on a TCP/IP connection secured by secure socket layers and virtual private network or jabber protocols. A teleconsultation can be online (with both physicians physically present) or offline (via transmission of messages which contain image data and other information). An interface sharing protocol enables online teleconsultations even over low bandwidth connections. This initiative promotes the cooperation and improved communication between nuclear medicine professionals, offering options for second opinion and training. It permits physicians to remotely consult patient data, even if they are away from the physical examination site

  10. European health telematics networks for positron emission tomography

    Energy Technology Data Exchange (ETDEWEB)

    Kontaxakis, George [Universidad Politecnica de Madrid, ETSI Telecomunicacion, Madrid 28040 (Spain)]. E-mail: g.kontaxakis@upm.es; Pozo, Miguel Angel [Centro PET Complutense, Madrid 28040 (Spain); Universidad Complutense de Madrid, Instituto Pluridisciplinar, Madrid 28040 (Spain); Ohl, Roland [MedCom Gesellschaft fuer medizinische Bildverarbeitung mbH, Darmstadt 64283 (Germany); Visvikis, Dimitris [U650 INSERM, Lab. du Traitement de L' Information Medicale, University of Brest Occidentale, CHU Morvan, Brest 29609 (France); Sachpazidis, Ilias [Fraunhofer Institute for Computer Graphics, Darmstadt 64283 (Germany); Ortega, Fernando [Fundacion Instituto Valenciano de Oncologia, Valencia 46009 (Spain); Guerra, Pedro [Universidad Politecnica de Madrid, ETSI Telecomunicacion, Madrid 28040 (Spain); Cheze-Le Rest, Catherine [Dept. Medicine Nucleaire, CHU Morvan, Brest 29609 (France); Selby, Peter [MedCom Gesellschaft fuer medizinische Bildverarbeitung mbH, Darmstadt 64283 (Germany); Pan, Leyun [German Cancer Research Centre, Clinical Cooperation Unit Nuclear Medicine, Heidelberg 69120 (Germany); Diaz, Javier [Fundacion Instituto Valenciano de Oncologia, Valencia 46009 (Spain); Dimitrakopoulou-Strauss, Antonia [German Cancer Research Centre, Clinical Cooperation Unit Nuclear Medicine, Heidelberg 69120 (Germany); Santos, Andres [Universidad Politecnica de Madrid, ETSI Telecomunicacion, Madrid 28040 (Spain); Strauss, Ludwig [German Cancer Research Centre, Clinical Cooperation Unit Nuclear Medicine, Heidelberg 69120 (Germany); Sakas, Georgios [MedCom Gesellschaft fuer medizinische Bildverarbeitung mbH, Darmstadt 64283 (Germany); Fraunhofer Institute for Computer Graphics, Darmstadt 64283 (Germany)

    2006-12-20

    A pilot network of positron emission tomography centers across Europe has been setup employing telemedicine services. The primary aim is to bring all PET centers in Europe (and beyond) closer, by integrating advanced medical imaging technology and health telematics networks applications into a single, easy to operate health telematics platform, which allows secure transmission of medical data via a variety of telecommunications channels and fosters the cooperation between professionals in the field. The platform runs on PCs with Windows 2000/XP and incorporates advanced techniques for image visualization, analysis and fusion. The communication between two connected workstations is based on a TCP/IP connection secured by secure socket layers and virtual private network or jabber protocols. A teleconsultation can be online (with both physicians physically present) or offline (via transmission of messages which contain image data and other information). An interface sharing protocol enables online teleconsultations even over low bandwidth connections. This initiative promotes the cooperation and improved communication between nuclear medicine professionals, offering options for second opinion and training. It permits physicians to remotely consult patient data, even if they are away from the physical examination site.

  11. A Study on the Effects of Sympathetic Skin Response Parameters in Diagnosis of Fibromyalgia Using Artificial Neural Networks.

    Science.gov (United States)

    Ozkan, Ozhan; Yildiz, Murat; Arslan, Evren; Yildiz, Sedat; Bilgin, Suleyman; Akkus, Selami; Koyuncuoglu, Hasan R; Koklukaya, Etem

    2016-03-01

    Fibromyalgia syndrome (FMS), usually observed commonly in females over age 30, is a rheumatic disease accompanied by extensive chronic pain. In the diagnosis of the disease non-objective psychological tests and physiological tests and laboratory test results are evaluated and clinical experiences stand out. However, these tests are insufficient in differentiating FMS with similar diseases that demonstrate symptoms of extensive pain. Thus, objective tests that would help the diagnosis are needed. This study analyzes the effect of sympathetic skin response (SSR) parameters on the auxiliary tests used in FMS diagnosis, the laboratory tests and physiological tests. The study was conducted in Suleyman Demirel University, Faculty of Medicine, Physical Medicine and Rehabilitation Clinic in Turkey with 60 patients diagnosed with FMS for the first time and a control group of 30 healthy individuals. In the study all participants underwent laboratory tests (blood tests), certain physiological tests (pulsation, skin temperature, respiration) and SSR measurements. The test data and SSR parameters obtained were classified using artificial neural network (ANN). Finally, in the ANN framework, where only laboratory and physiological test results were used as input, a simulation result of 96.51 % was obtained, which demonstrated diagnostic accuracy. This data, with the addition of SSR parameter values obtained increased to 97.67 %. This result including SSR parameters - meaning a higher diagnostic accuracy - demonstrated that SSR could be a new auxillary diagnostic method that could be used in the diagnosis of FMS.

  12. Condition monitoring and fault diagnosis of motor bearings using undersampled vibration signals from a wireless sensor network

    Science.gov (United States)

    Lu, Siliang; Zhou, Peng; Wang, Xiaoxian; Liu, Yongbin; Liu, Fang; Zhao, Jiwen

    2018-02-01

    Wireless sensor networks (WSNs) which consist of miscellaneous sensors are used frequently in monitoring vital equipment. Benefiting from the development of data mining technologies, the massive data generated by sensors facilitate condition monitoring and fault diagnosis. However, too much data increase storage space, energy consumption, and computing resource, which can be considered fatal weaknesses for a WSN with limited resources. This study investigates a new method for motor bearings condition monitoring and fault diagnosis using the undersampled vibration signals acquired from a WSN. The proposed method, which is a fusion of the kurtogram, analog domain bandpass filtering, bandpass sampling, and demodulated resonance technique, can reduce the sampled data length while retaining the monitoring and diagnosis performance. A WSN prototype was designed, and simulations and experiments were conducted to evaluate the effectiveness and efficiency of the proposed method. Experimental results indicated that the sampled data length and transmission time of the proposed method result in a decrease of over 80% in comparison with that of the traditional method. Therefore, the proposed method indicates potential applications on condition monitoring and fault diagnosis of motor bearings installed in remote areas, such as wind farms and offshore platforms.

  13. Social Network Analysis of Elders' Health Literacy and their Use of Online Health Information.

    Science.gov (United States)

    Jang, Haeran; An, Ji-Young

    2014-07-01

    Utilizing social network analysis, this study aimed to analyze the main keywords in the literature regarding the health literacy of and the use of online health information by aged persons over 65. Medical Subject Heading keywords were extracted from articles on the PubMed database of the National Library of Medicine. For health literacy, 110 articles out of 361 were initially extracted. Seventy-one keywords out of 1,021 were finally selected after removing repeated keywords and applying pruning. Regarding the use of online health information, 19 articles out of 26 were selected. One hundred forty-four keywords were initially extracted. After removing the repeated keywords, 74 keywords were finally selected. Health literacy was found to be strongly connected with 'Health knowledge, attitudes, practices' and 'Patient education as topic.' 'Computer literacy' had strong connections with 'Internet' and 'Attitude towards computers.' 'Computer literacy' was connected to 'Health literacy,' and was studied according to the parameters 'Attitude towards health' and 'Patient education as topic.' The use of online health information was strongly connected with 'Health knowledge, attitudes, practices,' 'Consumer health information,' 'Patient education as topic,' etc. In the network, 'Computer literacy' was connected with 'Health education,' 'Patient satisfaction,' 'Self-efficacy,' 'Attitude to computer,' etc. Research on older citizens' health literacy and their use of online health information was conducted together with study of computer literacy, patient education, attitude towards health, health education, patient satisfaction, etc. In particular, self-efficacy was noted as an important keyword. Further research should be conducted to identify the effective outcomes of self-efficacy in the area of interest.

  14. Mobile Network Data for Public-Health: Opportunities and Challenges

    Directory of Open Access Journals (Sweden)

    Nuria eOliver

    2015-08-01

    Full Text Available The ubiquity of mobile phones worldwide is generating an unprecedented amount of human behavioral data both at an individual and aggregated levels. The study of this data as a rich source of information about human behavior emerged almost a decade ago. Since then it has grown into a fertile area of research named computational social sciences with a wide variety of applications in different fields such as social networks, urban and transport planning, economic development, emergency relief and, recently, public health. In this paper we briefly describe the state of the art on using mobile phone data for public health, and present the opportunities and challenges that this kind of data presents for public health.

  15. Mobile Network Data for Public Health: Opportunities and Challenges

    Science.gov (United States)

    Oliver, Nuria; Matic, Aleksandar; Frias-Martinez, Enrique

    2015-01-01

    The ubiquity of mobile phones worldwide is generating an unprecedented amount of human behavioral data both at an individual and aggregated levels. The study of this data as a rich source of information about human behavior emerged almost a decade ago. Since then, it has grown into a fertile area of research named computational social sciences with a wide variety of applications in different fields such as social networks, urban and transport planning, economic development, emergency relief, and, recently, public health. In this paper, we briefly describe the state of the art on using mobile phone data for public health, and present the opportunities and challenges that this kind of data presents for public health. PMID:26301211

  16. [Histological diagnosis of bone tumors: Guidelines of the French committee of bone pathologists reference network on bone tumors (RESOS)].

    Science.gov (United States)

    Galant, Christine; Bouvier, Corinne; Larousserie, Frédérique; Aubert, Sébastien; Audard, Virginie; Brouchet, Anne; Marie, Béatrice; Guinebretière, Jean-Marc; de Pinieux du Bouexic, Gonzague

    2018-04-01

    The management of patients having a bone lesion requires in many cases the realization of a histological sample in order to obtain a diagnosis. However, with the technological evolution, CT-guided biopsies are performed more frequently, often in outpatient clinics. Interpretation of these biopsies constitutes new challenges for the pathologists within the wide spectrum of bone entities. The purpose of the document is to propose guidelines based on the experience of the French committee of bone pathologists of the reference network on bone tumors (RESOS) regarding the indications and limitations of the diagnosis on restricted material. Copyright © 2018 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.

  17. Bearings Fault Diagnosis Based on Convolutional Neural Networks with 2-D Representation of Vibration Signals as Input

    Directory of Open Access Journals (Sweden)

    Zhang Wei

    2017-01-01

    Full Text Available Periodic vibration signals captured by the accelerometers carry rich information for bearing fault diagnosis. Existing methods mostly rely on hand-crafted time-consuming preprocessing of data to acquire suitable features. In this paper, we use an easy and effective method to transform the 1-D temporal vibration signal into a 2-D image. With the signal image, convolutional Neural Network (CNN is used to train the raw vibration data. As powerful feature extractor and classifier for image recognition, CNN can learn to acquire features most suitable for the classification task by being trained. With the image format of vibration signals, the neuron in fully-connected layer of CNN can see farther and capture the periodic feature of signals. According to the results of the experiments, when fed in enough training samples, the proposed method outperforms other common methods. The proposed method can also be applied to solve intelligent diagnosis problems of other machine systems.

  18. Fault Diagnosis System of Induction Motors Based on Neural Network and Genetic Algorithm Using Stator Current Signals

    Directory of Open Access Journals (Sweden)

    Tian Han

    2006-01-01

    Full Text Available This paper proposes an online fault diagnosis system for induction motors through the combination of discrete wavelet transform (DWT, feature extraction, genetic algorithm (GA, and neural network (ANN techniques. The wavelet transform improves the signal-to-noise ratio during a preprocessing. Features are extracted from motor stator current, while reducing data transfers and making online application available. GA is used to select the most significant features from the whole feature database and optimize the ANN structure parameter. Optimized ANN is trained and tested by the selected features of the measurement data of stator current. The combination of advanced techniques reduces the learning time and increases the diagnosis accuracy. The efficiency of the proposed system is demonstrated through motor faults of electrical and mechanical origins on the induction motors. The results of the test indicate that the proposed system is promising for the real-time application.

  19. Effect of health belief model and health promotion model on breast cancer early diagnosis behavior: a systematic review.

    Science.gov (United States)

    Ersin, Fatma; Bahar, Zuhal

    2011-01-01

    Breast cancer is an important public health problem on the grounds that it is frequently seen and it is a fatal disease. The objective of this systematic analysis is to indicate the effects of interventions performed by nurses by using the Health Belief Model (HBM) and Health Promotion Model (HPM) on the breast cancer early diagnosis behaviors and on the components of the Health Belief Model and Health Promotion Model. The reveiw was created in line with the Centre for Reviews and Dissemination guide dated 2009 (CRD) and developed by York University National Institute of Health Researches. Review was conducted by using PUBMED, OVID, EBSCO and COCHRANE databases. Six hundred seventy eight studies (PUBMED: 236, OVID: 162, EBSCO: 175, COCHRANE:105) were found in total at the end of the review. Abstracts and full texts of these six hundred seventy eight studies were evaluated in terms of inclusion and exclusion criteria and 9 studies were determined to meet the criteria. Samplings of the studies varied between ninety four and one thousand six hundred fifty five. It was detected in the studies that educations provided by taking the theories as basis became effective on the breast cancer early diagnosis behaviors. When the literature is examined, it is observed that the experimental researches which compare the concepts of Health Belief Model (HBM) and Health Promotion Model (HPM) preoperatively and postoperatively and show the effect of these concepts on education and are conducted by nurses are limited in number. Randomized controlled studies which compare HBM and HPM concepts preoperatively and postoperatively and show the efficiency of the interventions can be useful in evaluating the efficiency of the interventions.

  20. The diagnosis and management of progressive dysfunction of health care organizations.

    Science.gov (United States)

    Chervenak, Frank A; McCullough, Laurence B

    2005-04-01

    This paper presents an ethically justified approach to the diagnosis and management of progressive dysfunction of health care organizational cultures. We explain the concept of professional integrity in terms of the ethical concept of the cofiduciary responsibility of physicians and health care organizations. We identify the ethical features of a healthy health care organization and the spectrum of progressive dysfunction of organizational cultures from cynical through wonderland and Kafkaesque to postmodern. Physicians should respond to cynical health care organizations by creating moral enclaves of professional integrity for the main purpose of confrontation and reform, to wonderland organizations by strengthening moral enclaves for the main purpose of resisting self-deception, to Kafkaesque organizations by strengthening moral enclaves still further for the main purpose of defending professional integrity (adopting a Machiavellian appearance of virtue as necessary), and to postmodern organizations by creating moral fortresses and, should these fail, quitting.

  1. Patient Health Monitoring Using Wireless Body Area Network

    Directory of Open Access Journals (Sweden)

    Hsu Myat Thwe

    2015-06-01

    Full Text Available Abstract Nowadays remote patient health monitoring using wireless technology plays very vigorous role in a society. Wireless technology helps monitoring of physiological parameters like body temperature heart rate respiration blood pressure and ECG. The main aim of this paper is to propose a wireless sensor network system in which both heart rate and body temperature ofmultiplepatients can monitor on PC at the same time via RF network. The proposed prototype system includes two sensor nodes and receiver node base station. The sensor nodes are able to transmit data to receiver using wireless nRF transceiver module.The nRF transceiver module is used to transfer the data from microcontroller to PC and a graphical user interface GUI is developed to display the measured data and save to database. This system can provide very cheaper easier and quick respondent history of patient.

  2. A diagnosis model for early Tourette syndrome children based on brain structural network characteristics

    Science.gov (United States)

    Wen, Hongwei; Liu, Yue; Wang, Jieqiong; Zhang, Jishui; Peng, Yun; He, Huiguang

    2016-03-01

    Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder characterized by the presence of multiple motor and vocal tics. Tic generation has been linked to disturbed networks of brain areas involved in planning, controlling and execution of action. The aim of our work is to select topological characteristics of structural network which were most efficient for estimating the classification models to identify early TS children. Here we employed the diffusion tensor imaging (DTI) and deterministic tractography to construct the structural networks of 44 TS children and 48 age and gender matched healthy children. We calculated four different connection matrices (fiber number, mean FA, averaged fiber length weighted and binary matrices) and then applied graph theoretical methods to extract the regional nodal characteristics of structural network. For each weighted or binary network, nodal degree, nodal efficiency and nodal betweenness were selected as features. Support Vector Machine Recursive Feature Extraction (SVM-RFE) algorithm was used to estimate the best feature subset for classification. The accuracy of 88.26% evaluated by a nested cross validation was achieved on combing best feature subset of each network characteristic. The identified discriminative brain nodes mostly located in the basal ganglia and frontal cortico-cortical networks involved in TS children which was associated with tic severity. Our study holds promise for early identification and predicting prognosis of TS children.

  3. Building research infrastructure in community health centers: a Community Health Applied Research Network (CHARN) report.

    Science.gov (United States)

    Likumahuwa, Sonja; Song, Hui; Singal, Robbie; Weir, Rosy Chang; Crane, Heidi; Muench, John; Sim, Shao-Chee; DeVoe, Jennifer E

    2013-01-01

    This article introduces the Community Health Applied Research Network (CHARN), a practice-based research network of community health centers (CHCs). Established by the Health Resources and Services Administration in 2010, CHARN is a network of 4 community research nodes, each with multiple affiliated CHCs and an academic center. The four nodes (18 individual CHCs and 4 academic partners in 9 states) are supported by a data coordinating center. Here we provide case studies detailing how CHARN is building research infrastructure and capacity in CHCs, with a particular focus on how community practice-academic partnerships were facilitated by the CHARN structure. The examples provided by the CHARN nodes include many of the building blocks of research capacity: communication capacity and "matchmaking" between providers and researchers; technology transfer; research methods tailored to community practice settings; and community institutional review board infrastructure to enable community oversight. We draw lessons learned from these case studies that we hope will serve as examples for other networks, with special relevance for community-based networks seeking to build research infrastructure in primary care settings.

  4. Improving diagnosis in health care: perspectives from the American College of Radiology.

    Science.gov (United States)

    Allen, Bibb; Chatfield, Mythreyi; Burleson, Judy; Thorwarth, William T

    2017-09-26

    In September of 2014, the American College of Radiology joined a number of other organizations in sponsoring the 2015 National Academy of Medicine report, Improving Diagnosis In Health Care. Our presentation to the Academy emphasized that although diagnostic errors in imaging are commonly considered to result only from failures in disease detection or misinterpretation of a perceived abnormality, most errors in diagnosis result from failures in information gathering, aggregation, dissemination and ultimately integration of that information into our patients' clinical problems. Diagnostic errors can occur at any point on the continuum of imaging care from when imaging is first considered until results and recommendations are fully understood by our referring physicians and patients. We used the concept of the Imaging Value Chain and the ACR's Imaging 3.0 initiative to illustrate how better information gathering and integration at each step in imaging care can mitigate many of the causes of diagnostic errors. Radiologists are in a unique position to be the aggregators, brokers and disseminators of information critical to making an informed diagnosis, and if radiologists were empowered to use our expertise and informatics tools to manage the entire imaging chain, diagnostic errors would be reduced and patient outcomes improved. Heath care teams should take advantage of radiologists' ability to fully manage information related to medical imaging, and simultaneously, radiologists must be ready to meet these new challenges as health care evolves. The radiology community stands ready work with all stakeholders to design and implement solutions that minimize diagnostic errors.

  5. Classification of Microcalcifications for the Diagnosis of Breast Cancer Using Artificial Neural Networks

    National Research Council Canada - National Science Library

    Wu, Yuzheng

    1997-01-01

    .... A convolution neural network (CNN) was employed to classify benign and malignant microcalcifications in the radiographs of pathological specimen that were digitized at a high resolution of 21 microns x 21 microns...

  6. Choosing your network: social preferences in an online health community.

    Science.gov (United States)

    Centola, Damon; van de Rijt, Arnout

    2015-01-01

    A growing number of online health communities offer individuals the opportunity to receive information, advice, and support from peers. Recent studies have demonstrated that these new online contacts can be important informational resources, and can even exert significant influence on individuals' behavior in various contexts. However little is known about how people select their health contacts in these virtual domains. This is because selection preferences in peer networks are notoriously difficult to detect. In existing networks, unobserved pressures on tie formation--such as common organizational memberships, introductions to friends of friends, or limitations on accessibility--may mistakenly be interpreted as individual preferences for interacting/not interacting with others. We address these issues by adopting a social media approach to studying network formation. We study social selection using an in vivo study within an online exercise program, in which anonymous participants have equal opportunities for initiating relationships with other program members. This design allows us to identify individuals' preferences for health contacts, and to evaluate what these preferences imply for members' access to new kinds of health information, and for the kinds of social influences to which they are exposed. The study was conducted within a goal-oriented fitness competition, in which participation was greatest among a small core of active individuals. Our results show that the active participants displayed indifference to the fitness and exercise profiles of others, disregarding information about others' fitness levels, exercise preferences, and workout experiences, instead selecting partners almost entirely on the basis of similarities on gender, age, and BMI. Interestingly, the findings suggest that rather than expanding and diversifying their sources of health information, participants' choices limited the value of their online resources by selecting contacts

  7. Vertical integration and organizational networks in health care.

    Science.gov (United States)

    Robinson, J C; Casalino, L P

    1996-01-01

    This paper documents the growing linkages between primary care-centered medical groups and specialists and between physicians and hospitals under managed care. We evaluate the two alternative forms of organizational coordination: "vertical integration," based on unified ownership, and "virtual integration," based on contractual networks. Excess capacity and the need for investment capital are major short-term determinants of these vertical versus virtual integration decisions in health care. In the longer term, the principal determinants are economies of scale, risk-bearing ability, transaction costs, and the capacity for innovation in methods of managing care.

  8. Networks as a type of social entrepreneurship to advance population health.

    Science.gov (United States)

    Wei-Skillern, Jane

    2010-11-01

    A detailed case study from the field of social entrepreneurship is used to illustrate the network approach, which does not require more resources but rather makes better use of existing resources. Leaders in public health can use networks to overcome some of the barriers that inhibit the widespread adoption of a population health approach to community health. Public health leaders who embrace social entrepreneurship may be better able to accomplish their missions by building their networks rather than just their organizations.

  9. Networks as a Type of Social Entrepreneurship to Advance Population Health

    OpenAIRE

    Wei-Skillern, Jane

    2010-01-01

    A detailed case study from the field of social entrepreneurship is used to illustrate the network approach, which does not require more resources but rather makes better use of existing resources. Leaders in public health can use networks to overcome some of the barriers that inhibit the widespread adoption of a population health approach to community health. Public health leaders who embrace social entrepreneurship may be better able to accomplish their missions by building their networks ra...

  10. Health-related quality of life in patients with dual diagnosis: clinical correlates

    Directory of Open Access Journals (Sweden)

    Benaiges Irina

    2012-09-01

    Full Text Available Abstract Background Although the studies published so far have found an affectation in the Health Related Quality of Life (HRQOL in both psychiatric and substance use dependence disorders, very few studies have applied HRQOL as an assessment measure in patients suffering both comorbid conditions, or Dual Diagnosis. The aim of the current study was to assess HRQOL in a group of patients with Dual Diagnosis compared to two other non-comorbid groups and to determine what clinical factors are related to HRQOL. Methods Cross-sectional assessment of three experimental groups was made through the Short Form – 36 Item Health Survey (SF-36. The sample consisted of a group with Dual Diagnosis (DD; N = 35, one with Severe Mental Illness alone (SMI; N = 35 and another one with Substance Use Dependence alone (SUD; N = 35. The sample was composed only by males. To assess the clinical correlates of SF-36 HRQOL, lineal regression analyses were carried out. Results The DD group showed lower scores in most of the subscales, and in the mental health domain. The group with SUD showed in general a better state in the HRQOL while the group with SMI held an intermediate position with respect to the other two groups. Daily medication, suicidal attempts and daily number of coffees were significantly associated to HRQOL, especially in the DD group. Conclusions The DD group showed lower self-reported mental health quality of life. Assessment of HRQOL in dual patients allows to identify specific needs in this population, and may help to establish therapeutic goals to improve interventions.

  11. Contradictions In Mental Health: Stigma, Mental Health Literacy And Disclosure (Or Not Of A Mental Disorder Diagnosis.

    Directory of Open Access Journals (Sweden)

    manuel torres cubeiro

    2018-05-01

    Full Text Available Mental illnesses affect 25% of any given population. The literacy of human population about mental health doesn’t not much the scientific knowledge available about Mental disorders (MDs. Developed countries invest in mental health less than their 9% of their GDPs. There is a contradiction, or discrepancy, between the incidence of MD in human population and how human societies react about them. This discrepancy has long been evident in the literature of medical sociology. In this article we analyze three medical sociology related concepts that have been coined to understand this contradiction: first, mental health literacy; second, stigma of mental ailments; and finally, the disclosure (or not of the diagnosis of a mental illness. With this article we try to solve short use of these concepts in medical sociology in Spanish.

  12. Combined Geometric and Neural Network Approach to Generic Fault Diagnosis in Satellite Reaction Wheels

    DEFF Research Database (Denmark)

    Baldi, P.; Blanke, Mogens; Castaldi, P.

    2015-01-01

    This paper suggests a novel diagnosis scheme for detection, isolation and estimation of faults affecting satellite reaction wheels. Both spin rate measurements and actuation torque defects are dealt with. The proposed system consists of a fault detection and isolation module composed by a bank of...

  13. Combined Geometric and Neural Network Approach to Generic Fault Diagnosis in Satellite Actuators and Sensors

    DEFF Research Database (Denmark)

    Baldi, P.; Blanke, Mogens; Castaldi, P.

    2016-01-01

    This paper presents a novel scheme for diagnosis of faults affecting the sensors measuring the satellite attitude, body angular velocity and flywheel spin rates as well as defects related to the control torques provided by satellite reaction wheels. A nonlinear geometric design is used to avoid t...

  14. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm.

    Science.gov (United States)

    Lee, Jae-Hong; Kim, Do-Hyung; Jeong, Seong-Nyum; Choi, Seong-Ho

    2018-04-01

    The aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT). Combining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python. The periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%-91.2%) for premolars and 73.4% (95% CI, 59.9%-84.0%) for molars. We demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.

  15. The Impact of Online Social Networks on Health and Health Systems: A Scoping Review and Case Studies.

    Science.gov (United States)

    Griffiths, Frances; Dobermann, Tim; Cave, Jonathan A K; Thorogood, Margaret; Johnson, Samantha; Salamatian, Kavé; Gomez Olive, Francis X; Goudge, Jane

    2015-12-01

    Interaction through online social networks potentially results in the contestation of prevailing ideas about health and health care, and to mass protest where health is put at risk or health care provision is wanting. Through a review of the academic literature and case studies of four social networking health sites (PatientsLikeMe, Mumsnet, Treatment Action Campaign, and My Pro Ana), we establish the extent to which this phenomenon is documented, seek evidence of the prevalence and character of health-related networks, and explore their structure, function, participants, and impact, seeking to understand how they came into being and how they sustain themselves. Results indicate mass protest is not arising from these established health-related networking platforms. There is evidence of changes in policy following campaigning activity prompted by experiences shared through social networking such as improved National Health Service care for miscarriage (a Mumsnet campaign). Platform owners and managers have considerable power to shape these campaigns. Social networking is also influencing health policy indirectly through increasing awareness and so demand for health care. Transient social networking about health on platforms such as Twitter were not included as case studies but may be where the most radical or destabilizing influence on health care policy might arise.

  16. A network approach for researching partnerships in health.

    Science.gov (United States)

    Lewis, Jenny M

    2005-10-07

    The last decade has witnessed a significant move towards new modes of governing that are based on coordination and collaboration. In particular, local level partnerships have been widely introduced around the world. There are few comprehensive approaches for researching the effects of these partnerships. The aim of this paper is to outline a network approach that combines structure and agency based explanations to research partnerships in health. Network research based on two Primary Care Partnerships (PCPs) in Victoria is used to demonstrate the utility of this approach. The paper examines multiple types of ties between people (structure), and the use and value of relationships to partners (agency), using interviews with the people involved in two PCPs--one in metropolitan Melbourne and one in a rural area. Network maps of ties based on work, strategic information and policy advice, show that there are many strong connections in both PCPs. Not surprisingly, PCP staff are central and highly connected. Of more interest are the ties that are dependent on these dedicated partnership staff, as they reveal which actors become weakly linked or disconnected without them. Network measures indicate that work ties are the most dispersed and strategic information ties are the most concentrated around fewer people. Divisions of general practice are weakly linked, while local government officials and Department of Human Services (DHS) regional staff appear to play important bridging roles. Finally, the relationships between partners have changed and improved, and most of those interviewed value their new or improved links with partners. Improving service coordination and health promotion planning requires engaging people and building strong relationships. Mapping ties is a useful means for assessing the strengths and weaknesses of partnerships, and network analysis indicates concentration and dispersion, the importance of particular individuals, and the points at which they

  17. Social networks and mental health among a farming population.

    Science.gov (United States)

    Stain, Helen J; Kelly, Brian; Lewin, Terry J; Higginbotham, Nick; Beard, John R; Hourihan, Fleur

    2008-10-01

    The study investigated the associations between mental health and measures of community support, social support networks, sense of place, adversity, and perceived problems in a rural Australian population. There was a specific focus on farming communities due to previous qualitative research by the authors indicating distress by farmers in response to drought (Sartore et al. Aust Fam Phys 36(12), 990-993, 2007). A survey was mailed to adults randomly selected from the Australian Electoral Roll and residing within four local government areas (LGAs) of varying remoteness in rural New South Wales (NSW). Survey measures included: support networks and community attachment; recent stressors (including drought-related stress); and measures of health and related functioning. The Kessler-10 provided an index of current psychological distress. The sample (n = 449; response rate 24%) was predominantly female (58.4%) and 18.9% were farmers or farm workers. Moderate to very high psychological distress was reported for 20.7% of the sample. Half (56.1%) of all respondents, and specifically 71.8% of farmers or farm workers, reported high levels of perceived stress due to drought. Psychological distress was associated with recent adverse life events, increased alcohol use and functional impairment. Hierarchical regression analysis demonstrated an independent effect of the number of stressful life events including drought related stress, perceived social support (community and individual), alcohol use and physical functioning ability on levels of psychological distress. This model accounted for 43% of the variance in current levels of distress. Lower community support had a more marked impact on distress levels for non-farming than farming participants. This study has highlighted the association between unique rural community characteristics and rural stressors (such as drought) and measures of mental health, suggesting the important mediating role of social factors and community

  18. Breast cancer and menopause: perceptions of diagnosis, menopausal therapies and health behaviors.

    Science.gov (United States)

    Sayakhot, P; Vincent, A; Teede, H

    2012-02-01

    The aim of this study was to investigate the perception and experience of menopause diagnosis and therapies, the information provided and health behaviors in younger women with breast cancer. The questionnaire study was completed by 114 women, aged 40-51 years, with non-metastatic breast cancer. Women were recruited from outpatient clinics and the community. Descriptive statistics were completed. Most women were satisfied with the manner in which they were informed of the breast cancer (69%) and the menopause (59%) diagnoses. Although 80% of women were given breast cancer information, only 54% were given menopause information at diagnosis. Women were least satisfied (26%) with information regarding the long-term complications of menopause. Women perceived exercise (68%) and improving lifestyle (61%) as most effective in alleviating symptoms of menopause. The majority of women reported that they did not understand the risks/benefits of 'bioidentical' hormones (79%) and herbal therapies (78%), while 58% perceived hormone replacement therapies as associated with an increased risk of breast cancer. Most women reported weight gain (68%) and osteoporosis (67%) as the most common problems/fears regarding menopause. However, regarding health behaviors, only 56% reported having relevant tests including a blood sugar test or a bone density test. While information needs regarding breast cancer appear well met in younger women, unmet information needs regarding menopause after breast cancer persist. Further education and support are required for these women to optimize health screening and prevention behaviors and to ensure informed decision-making regarding menopause treatment options.

  19. Network resiliency through memory health monitoring and proactive management

    Science.gov (United States)

    Andrade Costa, Carlos H.; Cher, Chen-Yong; Park, Yoonho; Rosenburg, Bryan S.; Ryu, Kyung D.

    2017-11-21

    A method for managing a network queue memory includes receiving sensor information about the network queue memory, predicting a memory failure in the network queue memory based on the sensor information, and outputting a notification through a plurality of nodes forming a network and using the network queue memory, the notification configuring communications between the nodes.

  20. Social networking in online support groups for health: how online social networking benefits patients.

    Science.gov (United States)

    Chung, Jae Eun

    2014-01-01

    An increasing number of online support groups (OSGs) have embraced the features of social networking. So far, little is known about how patients use and benefit from these features. By implementing the uses-and-gratifications framework, the author conducted an online survey with current users of OSGs to examine associations among motivation, use of specific features of OSG, and support outcomes. Findings suggest that OSG users make selective use of varied features depending on their needs, and that perceptions of receiving emotional and informational support are associated more with the use of some features than others. For example, those with strong motivation for social interaction use diverse features of OSG and make one-to-one connections with other users by friending. In contrast, those with strong motivation for information seeking limit their use primarily to discussion boards. Results also show that online social networking features, such as friending and sharing of personal stories on blogs, are helpful in satisfying the need for emotional support. The present study sheds light on online social networking features in the context of health-related OSGs and provides practical lessons on how to improve the capacity of OSGs to serve the needs of their users.

  1. Towards a Versatile Problem Diagnosis Infrastructure for LargeWireless Sensor Networks

    NARCIS (Netherlands)

    Iwanicki, Konrad; Steen, van Maarten

    2007-01-01

    In this position paper, we address the issue of durable maintenance of a wireless sensor network, which will be crucial if the vision of large, long-lived sensornets is to become reality. Durable maintenance requires tools for diagnosing and fixing occurring problems, which can range from

  2. Modules, networks and systems medicine for understanding disease and aiding diagnosis

    DEFF Research Database (Denmark)

    Gustafsson, Mika; Nestor, Colm E.; Zhang, Huan

    2014-01-01

    Many common diseases, such as asthma, diabetes or obesity, involve altered interactions between thousands of genes. High-throughput techniques (omics) allow identification of such genes and their products, but functional understanding is a formidable challenge. Network-based analyses of omics dat...

  3. Modules, networks and systems medicine for understanding disease and aiding diagnosis

    NARCIS (Netherlands)

    Gustafsson, Mika; Nestor, Colm E.; Zhang, Huan; Barabási, Albert-László; Baranzini, Sergio; Brunak, Sören; Chung, Kian Fan; Federoff, Howard J.; Gavin, Anne-Claude; Meehan, Richard R.; Picotti, Paola; Pujana, Miguel Àngel; Rajewsky, Nikolaus; Smith, Kenneth Gc; Sterk, Peter J.; Villoslada, Pablo; Benson, Mikael

    2014-01-01

    Many common diseases, such as asthma, diabetes or obesity, involve altered interactions between thousands of genes. High-throughput techniques (omics) allow identification of such genes and their products, but functional understanding is a formidable challenge. Network-based analyses of omics data

  4. Diagnosis of Cognitive Impairment Compatible with Early Diagnosis of Alzheimer's Disease. A Bayesian Network Model based on the Analysis of Oral Definitions of Semantic Categories.

    Science.gov (United States)

    Guerrero, J M; Martínez-Tomás, R; Rincón, M; Peraita, H

    2016-01-01

    Early detection of Alzheimer's disease (AD) has become one of the principal focuses of research in medicine, particularly when the disease is incipient or even prodromic, because treatments are more effective in these stages. Lexical-semantic-conceptual deficit (LSCD) in the oral definitions of semantic categories for basic objects is an important early indicator in the evaluation of the cognitive state of patients. The objective of this research is to define an economic procedure for cognitive impairment (CI) diagnosis, which may be associated with early stages of AD, by analysing cognitive alterations affecting declarative semantic memory. Because of its low cost, it could be used for routine clinical evaluations or screenings, leading to more expensive and selective tests that confirm or rule out the disease accurately. It should necessarily be an explanatory procedure, which would allow us to study the evolution of the disease in relation to CI, the irregularities in different semantic categories, and other neurodegenerative diseases. On the basis of these requirements, we hypothesise that Bayesian networks (BNs) are the most appropriate tool for this purpose. We have developed a BN for CI diagnosis in mild and moderate AD patients by analysing the oral production of semantic features. The BN causal model represents LSCD in certain semantic categories, both of living things (dog, pine, and apple) and non-living things (chair, car, and trousers), as symptoms of CI. The model structure, the qualitative part of the model, uses domain knowledge obtained from psychology experts and epidemiological studies. Further, the model parameters, the quantitative part of the model, are learnt automatically from epidemiological studies and Peraita and Grasso's linguistic corpus of oral definitions. This corpus was prepared with an incidental sampling and included the analysis of the oral linguistic production of 81 participants (42 cognitively healthy elderly people and 39

  5. Leveraging Social Networks to Support Reproductive Health and Economic Wellbeing among Guatemalan Maya Women

    Science.gov (United States)

    Prescott, Alexandra S.; Luippold-Roge, Genevieve P.; Gurman, Tilly A.

    2016-01-01

    Objective: Maya women in Guatemala are disproportionately affected by poverty and negative reproductive health outcomes. Although social networks are valued in many Indigenous cultures, few studies have explored whether health education programmes can leverage these networks to improve reproductive health and economic wellbeing. Design: This…

  6. Count Your Calories and Share Them: Health Benefits of Sharing mHealth Information on Social Networking Sites.

    Science.gov (United States)

    Oeldorf-Hirsch, Anne; High, Andrew C; Christensen, John L

    2018-04-23

    This study investigates the relationship between sharing tracked mobile health (mHealth) information online, supportive communication, feedback, and health behavior. Based on the Integrated Theory of mHealth, our model asserts that sharing tracked health information on social networking sites benefits users' perceptions of their health because of the supportive communication they gain from members of their online social networks and that the amount of feedback people receive moderates these associations. Users of mHealth apps (N = 511) completed an online survey, and results revealed that both sharing tracked health information and receiving feedback from an online social network were positively associated with supportive communication. Network support both corresponded with improved health behavior and mediated the association between sharing health information and users' health behavior. As users received greater amounts of feedback from their online social networks, however, the association between sharing tracked health information and health behavior decreased. Theoretical implications for sharing tracked health information and practical implications for using mHealth apps are discussed.

  7. Autism spectrum disorder in adults: diagnosis, management, and health services development

    Science.gov (United States)

    Murphy, Clodagh M; Wilson, C Ellie; Robertson, Dene M; Ecker, Christine; Daly, Eileen M; Hammond, Neil; Galanopoulos, Anastasios; Dud, Iulia; Murphy, Declan G; McAlonan, Grainne M

    2016-01-01

    Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by pervasive difficulties since early childhood across reciprocal social communication and restricted, repetitive interests and behaviors. Although early ASD research focused primarily on children, there is increasing recognition that ASD is a lifelong neurodevelopmental disorder. However, although health and education services for children with ASD are relatively well established, service provision for adults with ASD is in its infancy. There is a lack of health services research for adults with ASD, including identification of comorbid health difficulties, rigorous treatment trials (pharmacological and psychological), development of new pharmacotherapies, investigation of transition and aging across the lifespan, and consideration of sex differences and the views of people with ASD. This article reviews available evidence regarding the etiology, legislation, diagnosis, management, and service provision for adults with ASD and considers what is needed to support adults with ASD as they age. We conclude that health services research for adults with ASD is urgently warranted. In particular, research is required to better understand the needs of adults with ASD, including health, aging, service development, transition, treatment options across the lifespan, sex, and the views of people with ASD. Additionally, the outcomes of recent international legislative efforts to raise awareness of ASD and service provision for adults with ASD are to be determined. Future research is required to identify high-quality, evidence-based, and cost-effective models of care. Furthermore, future health services research is also required at the beginning and end of adulthood, including improved transition from youth to adult health care and increased understanding of aging and health in older adults with ASD. PMID:27462160

  8. Polycentrism in Global Health Governance Scholarship Comment on "Four Challenges That Global Health Networks Face".

    Science.gov (United States)

    Tosun, Jale

    2017-05-23

    Drawing on an in-depth analysis of eight global health networks, a recent essay in this journal argued that global health networks face four challenges to their effectiveness: problem definition, positioning, coalition-building, and governance. While sharing the argument of the essay concerned, in this commentary, we argue that these analytical concepts can be used to explicate a concept that has implicitly been used in global health governance scholarship for quite a few years. While already prominent in the discussion of climate change governance, for instance, global health governance scholarship could make progress by looking at global health governance as being polycentric. Concisely, polycentric forms of governance mix scales, mechanisms, and actors. Drawing on the essay, we propose a polycentric approach to the study of global health governance that incorporates coalitionbuilding tactics, internal governance and global political priority as explanatory factors. © 2018 The Author(s); Published by Kerman University of Medical Sciences. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

  9. Autism spectrum disorder in adults: diagnosis, management, and health services development

    Directory of Open Access Journals (Sweden)

    Murphy CM

    2016-07-01

    Full Text Available Clodagh M Murphy,1,2 C Ellie Wilson,1–3 Dene M Robertson,1,2 Christine Ecker,1,4 Eileen M Daly,1,2 Neil Hammond,1,2 Anastasios Galanopoulos,1,2 Iulia Dud,1,2 Declan G Murphy,1,2 Grainne M McAlonan1,2 1Sackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodevelopmental Sciences, King’s College London, Institute of Psychiatry, Psychology & Neuroscience, 2Behavioural and Developmental Psychiatry Clinical Academic Group, Behavioural Genetics Clinic, National Adult Autism Service, South London and Maudsley Foundation NHS Trust, London, UK; 3Individual Differences, Language and Cognition Lab, Department of Developmental and Educational Psychology, University of Seville, Spain; 4Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe-University, Frankfurt am Main, Germany Abstract: Autism spectrum disorder (ASD is a common neurodevelopmental disorder characterized by pervasive difficulties since early childhood across reciprocal social communication and restricted, repetitive interests and behaviors. Although early ASD research focused primarily on children, there is increasing recognition that ASD is a lifelong neurodevelopmental disorder. However, although health and education services for children with ASD are relatively well established, service provision for adults with ASD is in its infancy. There is a lack of health services research for adults with ASD, including identification of comorbid health difficulties, rigorous treatment trials (pharmacological and psychological, development of new pharmacotherapies, investigation of transition and aging across the lifespan, and consideration of sex differences and the views of people with ASD. This article reviews available evidence regarding the etiology, legislation, diagnosis, management, and service provision for adults with ASD and considers what is needed to support adults with ASD as they age. We conclude

  10. Sharing for Health: A Study of Chinese Adolescents' Experiences and Perspectives on Using Social Network Sites to Share Health Information.

    Science.gov (United States)

    Zhang, Ni; Teti, Michele; Stanfield, Kellie; Campo, Shelly

    2017-07-01

    This exploratory qualitative study examines Chinese adolescents' health information sharing habits on social network sites. Ten focus group meetings with 76 adolescents, ages 12 to 17 years, were conducted at community-based organizations in Chicago's Chinatown. The research team transcribed the recording and analyzed the transcripts using ATLAS.ti. Chinese adolescents are using different social network sites for various topics of health information including food, physical activity, and so on. Adolescents would share useful and/or interesting health information. Many adolescents raised credibility concerns regarding health information and suggested evaluating the information based on self-experience or intuition, word-of-mouth, or information online. The findings shed lights on future intervention using social network sites to promote health among Chinese adolescents in the United States. Future interventions should provide adolescents with interesting and culturally sensitive health information and educate them to critically evaluate health information on social network sites.

  11. The Health and Occupation Research Network: An Evolving Surveillance System.

    Science.gov (United States)

    Carder, Melanie; Hussey, Louise; Money, Annemarie; Gittins, Matthew; McNamee, Roseanne; Stocks, Susan Jill; Sen, Dil; Agius, Raymond M

    2017-09-01

    Vital to the prevention of work-related ill-health (WRIH) is the availability of good quality data regarding WRIH burden and risks. Physician-based surveillance systems such as The Health and Occupation Research (THOR) network in the UK are often established in response to limitations of statutory, compensation-based systems for addressing certain epidemiological aspects of disease surveillance. However, to fulfil their purpose, THOR and others need to have methodologic rigor in capturing and ascertaining cases. This article describes how data collected by THOR and analogous systems can inform WRIH incidence, trends, and other determinants. An overview of the different strands of THOR research is provided, including methodologic advancements facilitated by increased data quantity/quality over time and the value of the research outputs for informing Government and other policy makers. In doing so, the utility of data collected by systems such as THOR to address a wide range of research questions, both in relation to WRIH and to wider issues of public and social health, is demonstrated.

  12. Development of 'health and environmental safety assessment network system (HESANS)'

    International Nuclear Information System (INIS)

    Nakamura, Yuji

    1994-01-01

    With the recent advance of the utilization of nuclear energy in a large scale, social interest is being focussed in the potential risk which the nuclear technology will accompany. Especially after the accidents in Chernobyl and other nuclear facilities, serious anxiety to the utilization of nuclear energy is prevailing among the general public. In order to meet the anxiety and distrust of the population in the use of the nuclear power, the health effect or risk which radioactive materials released into the environment will bring about should be comprehensively and properly evaluated, and then should be widely reported to the population. The development of HESANS code system (Health and Environmental Safety Assessment Network System) was planned to set up such a comprehensive computer code that covers a whole pathway of radioactive material from its release to estimates of derived health effects in the population, including the countermeasures for intervention as well. Though the whole system is not totally completed yet so far, the framework of the system has been concreted together with many sub-systems which compose the main part of the code. This report puts main stress on the objective of the development project and the main frame or the structure of the code system. (author)

  13. The Health and Occupation Research Network: An Evolving Surveillance System

    Directory of Open Access Journals (Sweden)

    Melanie Carder

    2017-09-01

    Full Text Available Vital to the prevention of work-related ill-health (WRIH is the availability of good quality data regarding WRIH burden and risks. Physician-based surveillance systems such as The Health and Occupation Research (THOR network in the UK are often established in response to limitations of statutory, compensation-based systems for addressing certain epidemiological aspects of disease surveillance. However, to fulfil their purpose, THOR and others need to have methodologic rigor in capturing and ascertaining cases. This article describes how data collected by THOR and analogous systems can inform WRIH incidence, trends, and other determinants. An overview of the different strands of THOR research is provided, including methodologic advancements facilitated by increased data quantity/quality over time and the value of the research outputs for informing Government and other policy makers. In doing so, the utility of data collected by systems such as THOR to address a wide range of research questions, both in relation to WRIH and to wider issues of public and social health, is demonstrated.

  14. Queering the relationship between evidence-based mental health and psychiatric diagnosis: Some implications for international mental health nurse curricular development

    OpenAIRE

    Grant, Alec; Zeeman, Laetitia; Aranda, Kay

    2015-01-01

    We critique EB mental healthcare’s relationship with psychiatric diagnosis from a queer paradigm position. We sketch out some initial principles that will hopefully stimulate and contribute to the advancement of mental health nurse educational curricula internationally. This will help bring mental health nurse education more in-line with contemporary developments in narrative psychiatry and formulation as an emerging alternative to psychiatric diagnosis in UK clinical psychology.

  15. Application of power spectrum, cepstrum, higher order spectrum and neural network analyses for induction motor fault diagnosis

    Science.gov (United States)

    Liang, B.; Iwnicki, S. D.; Zhao, Y.

    2013-08-01

    The power spectrum is defined as the square of the magnitude of the Fourier transform (FT) of a signal. The advantage of FT analysis is that it allows the decomposition of a signal into individual periodic frequency components and establishes the relative intensity of each component. It is the most commonly used signal processing technique today. If the same principle is applied for the detection of periodicity components in a Fourier spectrum, the process is called the cepstrum analysis. Cepstrum analysis is a very useful tool for detection families of harmonics with uniform spacing or the families of sidebands commonly found in gearbox, bearing and engine vibration fault spectra. Higher order spectra (HOS) (also known as polyspectra) consist of higher order moment of spectra which are able to detect non-linear interactions between frequency components. For HOS, the most commonly used is the bispectrum. The bispectrum is the third-order frequency domain measure, which contains information that standard power spectral analysis techniques cannot provide. It is well known that neural networks can represent complex non-linear relationships, and therefore they are extremely useful for fault identification and classification. This paper presents an application of power spectrum, cepstrum, bispectrum and neural network for fault pattern extraction of induction motors. The potential for using the power spectrum, cepstrum, bispectrum and neural network as a means for differentiating between healthy and faulty induction motor operation is examined. A series of experiments is done and the advantages and disadvantages between them are discussed. It has been found that a combination of power spectrum, cepstrum and bispectrum plus neural network analyses could be a very useful tool for condition monitoring and fault diagnosis of induction motors.

  16. Public health educational comprehensiveness: The strategic rationale in establishing networks among schools of public health.

    Science.gov (United States)

    Otok, Robert; Czabanowska, Katarzyna; Foldspang, Anders

    2017-11-01

    The establishment and continuing development of a sufficient and competent public health workforce is fundamental for the planning, implementation, evaluation, effect and ethical validity of public health strategies and policies and, thus, for the development of the population's health and the cost-effectiveness of health and public health systems and interventions. Professional public health strategy-making demands a background of a comprehensive multi-disciplinary curriculum including mutually, dynamically coherent competences - not least, competences in sociology and other behavioural sciences and their interaction with, for example, epidemiology, biostatistics, qualitative methods and health promotion and disease prevention. The size of schools and university departments of public health varies, and smaller entities may run into problems if seeking to meet the comprehensive curriculum challenge entirely by use of in-house resources. This commentary discusses the relevance and strength of establishing comprehensive curriculum development networks between schools and university departments of public health, as one means to meet the comprehensiveness challenge. This commentary attempts to consider a two-stage strategy to develop complete curricula at the bachelor and master's as well as PhD levels.

  17. Energy Harvesting for Structural Health Monitoring Sensor Networks

    Energy Technology Data Exchange (ETDEWEB)

    Park, G.; Farrar, C. R.; Todd, M. D.; Hodgkiss, T.; Rosing, T.

    2007-02-26

    This report has been developed based on information exchanges at a 2.5-day workshop on energy harvesting for embedded structural health monitoring (SHM) sensing systems that was held June 28-30, 2005, at Los Alamos National Laboratory. The workshop was hosted by the LANL/UCSD Engineering Institute (EI). This Institute is an education- and research-focused collaboration between Los Alamos National Laboratory (LANL) and the University of California, San Diego (UCSD), Jacobs School of Engineering. A Statistical Pattern Recognition paradigm for SHM is first presented and the concept of energy harvesting for embedded sensing systems is addressed with respect to the data acquisition portion of this paradigm. Next, various existing and emerging sensing modalities used for SHM and their respective power requirements are summarized, followed by a discussion of SHM sensor network paradigms, power requirements for these networks and power optimization strategies. Various approaches to energy harvesting and energy storage are discussed and limitations associated with the current technology are addressed. This discussion also addresses current energy harvesting applications and system integration issues. The report concludes by defining some future research directions and possible technology demonstrations that are aimed at transitioning the concept of energy harvesting for embedded SHM sensing systems from laboratory research to field-deployed engineering prototypes.

  18. Health system tests CRM data base. Community Health Network uses direct mail to boost physicians.

    Science.gov (United States)

    Botvin, Judith D

    2003-01-01

    A six-month pilot patient retention project for Community Health Network (CHN), Indianapolis, ran from July 2002 to January 2003. It was a direct mail campaign on behalf of some members of the group practices owned by CHN, designed to test the use of the system's CRM database. Patients of the physicians received personal, dynamically-generated cards reminding them to schedule appointments and tests. Each mailing cost $1.76, including production and mailing.

  19. An evaluation of communication barriers and facilitators at the time of a mental health diagnosis: a survey of health professional practices.

    Science.gov (United States)

    Milton, A C; Mullan, B; MacCann, C; Hunt, C

    2017-01-24

    To examine health professionals' views and practices relating to the specific barriers to communication that arise at the time of mental health diagnosis, and the strategies used to support individuals throughout this process. An online survey of the beliefs and practices of 131 mental health clinicians working in different clinical settings across Australia was conducted. Exploratory factor analysis of the items relating to barriers to communication resulted in three latent factors ('stigma, diagnosis and risk'; 'service structure'; and 'individual circumstances' such as the person receiving the diagnosis being young, having a culturally and linguistically diverse background or being unwell at the time of conversation). Using linear regression it was found that variance in 'stigma, diagnosis and risk' was significantly explained by whether participating clinicians had medical training, their experience working with serious mental health problems, their confidence handling distress and attitude towards diagnosis. Variance in 'individual circumstances' was significantly explained by participating clinicians' confidence handling distress. The most frequently used strategies to support diagnostic discussions centred on the health professionals' communication skills, gauging the individual's perception of their circumstances, responding with empathy, following-up after discussion, addressing stigma concerns, using collaborative practice and setting up for the conversation. Three main areas for health professionals to reflect on, plan for and ultimately address when discussing news with the individual concerned emerged ('stigma, diagnosis and risk'; 'service structure'; and 'individual circumstances'). Variations in practice indicate that practitioners should be cognisant of their own beliefs and background and how this impacts their communication practice.

  20. Social Network Assessments and Interventions for Health Behavior Change: A Critical Review.

    Science.gov (United States)

    Latkin, Carl A; Knowlton, Amy R

    2015-01-01

    Social networks provide a powerful approach for health behavior change. This article documents how social network interventions have been successfully used for a range of health behaviors, including HIV risk practices, smoking, exercise, dieting, family planning, bullying, and mental health. We review the literature that suggests the relationship between health behaviors and social network attributes demonstrates a high degree of specificity. The article then examines hypothesized social influence mechanisms including social norms, modeling, and social rewards and the factors of social identity and social rewards that can be employed to sustain social network interventions. Areas of future research avenues are highlighted, including the need to examine and to adjust analytically for contamination and social diffusion, social influence versus differential affiliation, and network change. Use and integration of mhealth and face-to-face networks for promoting health behavior change are also critical research areas.

  1. Health implications of social networks for children living in public housing.

    Science.gov (United States)

    Kennedy-Hendricks, Alene; Schwartz, Heather L; Griffin, Beth Ann; Burkhauser, Susan; Green, Harold D; Kennedy, David P; Pollack, Craig Evan

    2015-11-01

    This study sought to examine whether: (1) the health composition of the social networks of children living in subsidized housing within market rate developments (among higher-income neighbors) differs from the social network composition of children living in public housing developments (among lower-income neighbors); and (2) children's social network composition is associated with children's own health. We found no significant differences in the health characteristics of the social networks of children living in these different types of public housing. However, social network composition was significantly associated with several aspects of children's own health, suggesting the potential importance of social networks for the health of vulnerable populations. Copyright © 2015 Elsevier Ltd. All rights reserved.

  2. Towards holistic dual diagnosis care: physical health screening in a Victorian community-based alcohol and drug treatment service.

    Science.gov (United States)

    Jackson, Lara; Felstead, Boyce; Bhowmik, Jahar; Avery, Rachel; Nelson-Hearity, Rhonda

    2016-01-01

    The poorer health outcomes experienced by people with mental illness have led to new directions in policy for routine physical health screening of service users. By contrast, little attention has been paid to the physical health needs of consumers of alcohol and other drug (AOD) services, despite a similar disparity in physical health outcomes compared with the general population. The majority of people with problematic AOD use have comorbid mental illness, known as a dual diagnosis, likely to exacerbate their vulnerability to poor physical health. With the potential for physical health screening to improve health outcomes for AOD clients, a need exists for systematic identification and management of common health conditions. Within the current health service system, those with a dual diagnosis are more likely to have their physical health surveyed and responded to if they present for treatment in the mental health system. In this study, a physical health screening tool was administered to clients attending a community-based AOD service. The tool was administered by a counsellor during the initial phase of treatment, and referrals to health professionals were made as appropriate. Findings are discussed in terms of prevalence, types of problems identified and subsequent rates of referral. The results corroborate the known link between mental and physical ill health, and contribute to developing evidence that AOD clients present with equally concerning physical ill health to that of mental health clients and should equally be screened for such when presenting for AOD treatment.

  3. Study of Aided Diagnosis of Hepatic Carcinoma Based on Artificial Neural Network Combined with Tumor Marker Group

    Science.gov (United States)

    Tan, Shanjuan; Feng, Feifei; Wu, Yongjun; Wu, Yiming

    To develop a computer-aided diagnostic scheme by using an artificial neural network (ANN) combined with tumor markers for diagnosis of hepatic carcinoma (HCC) as a clinical assistant method. 140 serum samples (50 malignant, 40 benign and 50 normal) were analyzed for α-fetoprotein (AFP), carbohydrate antigen 125 (CA125), carcinoembryonic antigen (CEA), sialic acid (SA) and calcium (Ca). The five tumor marker values were then used as ANN inputs data. The result of ANN was compared with that of discriminant analysis by receiver operating characteristic (ROC) curve (AUC) analysis. The diagnostic accuracy of ANN and discriminant analysis among all samples of the test group was 95.5% and 79.3%, respectively. Analysis of multiple tumor markers based on ANN may be a better choice than the traditional statistical methods for differentiating HCC from benign or normal.

  4. Neural network-based robust actuator fault diagnosis for a non-linear multi-tank system.

    Science.gov (United States)

    Mrugalski, Marcin; Luzar, Marcel; Pazera, Marcin; Witczak, Marcin; Aubrun, Christophe

    2016-03-01

    The paper is devoted to the problem of the robust actuator fault diagnosis of the dynamic non-linear systems. In the proposed method, it is assumed that the diagnosed system can be modelled by the recurrent neural network, which can be transformed into the linear parameter varying form. Such a system description allows developing the designing scheme of the robust unknown input observer within H∞ framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error, while guaranteeing the convergence of the observer. The application of the robust unknown input observer enables actuator fault estimation, which allows applying the developed approach to the fault tolerant control tasks. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  5. Semantic Web, Reusable Learning Objects, Personal Learning Networks in Health: Key Pieces for Digital Health Literacy.

    Science.gov (United States)

    Konstantinidis, Stathis Th; Wharrad, Heather; Windle, Richard; Bamidis, Panagiotis D

    2017-01-01

    The knowledge existing in the World Wide Web is exponentially expanding, while continuous advancements in health sciences contribute to the creation of new knowledge. There are a lot of efforts trying to identify how the social connectivity can endorse patients' empowerment, while other studies look at the identification and the quality of online materials. However, emphasis has not been put on the big picture of connecting the existing resources with the patients "new habits" of learning through their own Personal Learning Networks. In this paper we propose a framework for empowering patients' digital health literacy adjusted to patients' currents needs by utilizing the contemporary way of learning through Personal Learning Networks, existing high quality learning resources and semantics technologies for interconnecting knowledge pieces. The framework based on the concept of knowledge maps for health as defined in this paper. Health Digital Literacy needs definitely further enhancement and the use of the proposed concept might lead to useful tools which enable use of understandable health trusted resources tailored to each person needs.

  6. Caring for clients with dual diagnosis in rural communities in Australia: the experience of mental health professionals.

    Science.gov (United States)

    Deans, C; Soar, R

    2005-06-01

    This paper identifies and describes the experiences of 13 rural mental health professionals who care for clients diagnosed with a mental illness and a coexisting alcohol and other drug disorder (dual diagnosis). Dual diagnosis is a common problem which is often poorly understood and managed by mental health professionals. The effect of excessive substance use on a person's mental well-being can present as a diagnostic challenge as each condition may mask symptoms of the other. The authors utilized a phenomenological approach to discover the experiences of a group of mental health professionals working in rural communities in Victoria, Australia. Caring for clients diagnosed with dual diagnosis was found to be a complex and stressful role that involved high levels of skill and knowledge. Despite the fact that health professionals in rural areas are expected to deliver the most appropriate care to individuals with a dual diagnosis, a number of these rural health professionals have limited preparation and experience in dealing with arising clinical diagnosis issues. Clinicians experience frustration, resentment and powerlessness in their attempt to understand their clients' drug misuse whilst simultaneously endeavouring to provide a quality mental health service.

  7. Use of artificial neural networks (computer analysis) in the diagnosis of microcalcifications on mammography

    International Nuclear Information System (INIS)

    Markopoulos, Christos; Kouskos, Efstratios; Koufopoulos, Konstantinos; Kyriakou, Vasiliki; Gogas, John

    2001-01-01

    Introduction/objective: the purpose of this study was to evaluate a computer based method for differentiating malignant from benign clustered microcalcifications, comparing it with the performance of three physicians. Methods and material: materials for the study are 240 suspicious microcalcifications on mammograms from 220 female patients who underwent breast biopsy, following hook wire localization under mammographic guidance. The histologic findings were malignant in 108 cases (45%) and benign in 132 cases (55%). Those clusters were analyzed by a computer program and eight features of the calcifications (density, number, area, brightness, diameter average, distance average, proximity average, perimeter compacity average) were quantitatively estimated by a specific artificial neural network. Human input was limited to initial identification of the calcifications. Three physicians-observers were also evaluated for the malignant or benign nature of the clustered microcalcifications. Results: the performance of the artificial network was evaluated by receiver operating characteristics (ROC) curves. ROC curves were also generated for the performance of each observer and for the three observers as a group. The ROC curves for the computer and for the physicians were compared and the results are:area under the curve (AUC) value for computer is 0.937, for physician-1 is 0.746, for physician-2 is 0.785, for physician-3 is 0.835 and for physicians as a group is 0.810. The results of the Student's t-test for paired data showed statistically significant difference between the artificial neural network and the physicians' performance, independently and as a group. Discussion and conclusion: our study showed that computer analysis achieves statistically significantly better performance than that of physicians in the classification of malignant and benign calcifications. This method, after further evaluation and improvement, may help radiologists and breast surgeons in better

  8. Kalman filter based fault diagnosis of networked control system with white noise

    Institute of Scientific and Technical Information of China (English)

    Yanwei WANG; Ying ZHENG

    2005-01-01

    The networked control system NCS is regarded as a sampled control system with output time-variant delay.White noise is considered in the model construction of NCS.By using the Kalman filter theory to compute the filter parameters,a Kalman filter is constructed for this NCS.By comparing the output of the filter and the practical system,a residual is generated to diagnose the sensor faults and the actuator faults.Finally,an example is given to show the feasibility of the approach.

  9. Randomised primary health center based interventions to improve the diagnosis and treatment of undifferentiated fever and dengue in Vietnam

    NARCIS (Netherlands)

    Phuong, Hoang L.; Nga, Tran T. T.; Giao, Phan T.; Hung, Le Q.; Binh, Tran Q.; Nam, Nguyen V.; Nagelkerke, Nico; de Vries, Peter J.

    2010-01-01

    ABSTRACT: BACKGROUND: Fever is a common reason for attending primary health facilities in Vietnam. Response of health care providers to patients with fever commonly consists of making a presumptive diagnosis and proposing corresponding treatment. In Vietnam, where malaria was brought under control,

  10. Network analysis to support research management: evidence from the Fiocruz Observatory in Science, Technology and Innovation in Health

    Energy Technology Data Exchange (ETDEWEB)

    Fonseca, B.; Sampaio, R.B.; Silva, M.V.; Dos Santos, P.X.

    2016-07-01

    Brazil has been encouraging the establishment of research networks to address strategic health issues in response to social demands, creating an urgent need to develop indicators for their evaluation. The Oswaldo Cruz Foundation (Fiocruz), a national research, training and production institution, has initiated the development of an “Observatory in Science, Technology and Innovation in Health” to monitor and evaluate research and technological development for the formulation of institutional policies. In this context, we are proposing the use of social network analysis to map cooperation in strategic areas of research, identify prominent researchers and support internal research networks. In this preliminary study, coauthorship analysis was used to map the cooperative relations of Fiocruz in tuberculosis (TB) research, an important public health issue for which diagnosis and adequate treatment are still challenging. Our findings suggest that Brazilian research organizations acting in TB research are embedded in highly connected networks. The large number of international organizations present in the Brazilian network reflects the global increase in scientific collaboration and Brazil’s engagement in international collaborative research efforts. Fiocruz frequent cooperation with high-income countries demonstrates its concern in benefiting from the access to facilities, funding, equipment and networks that are often limited in its research setting. Collaboration with high burden countries has to be strengthened, as it could improve access to local knowledge and better understanding of the disease in different endemic contexts. Centrality analysis consolidated information on the importance of Fiocruz in connecting TB research institutions in Brazil. Fiocruz Observatory intends to advance this analysis by looking into the mechanisms of collaboration, identifying priority themes and assessing comparative advantages of the network members, an important contribution

  11. An Approach to Management of Health Care and Medical Diagnosis Using of a Hybrid Disease Diagnosis System

    Directory of Open Access Journals (Sweden)

    Hodjat Hamidi

    2017-02-01

    Full Text Available Introduction: In order to simplify the information exchange within the medical diagnosis process, a collaborative software agent’s framework is presented. The purpose of the framework is to allow the automated information exchange between different medicine specialists. Methods: This study presented architecture of a hybrid disease diagnosis system. The architecture employed a learning algorithm and used soft computing to build a medical knowledge base. These machine intelligences are combined in a complementary approach to overcome the weakness of each other. To evaluate the hybrid learning algorithm and compare it with other methods, 699 samples were used in each experiment, where 60% was for training, 20% was for cross validation, and 20% for testing. Results: The results were obtained from the experiments on the breast cancer dataset. Different methods of soft computing system were merged to create diagnostic software functionality. As it is shown in the structure, the system has the ability to learn and collect knowledge that can be used in the detection of new images. Currently, the system is at the design stage. The system is to evaluate the performance of hybrid learning algorithm. The preliminary results showed a better performance of this system than other methods. However, the results can be tested with hybrid system on larger data sets to improve hybrid learning algorithm. Conclusion: The purpose of this paper was to simplify the diagnosis process of a patient by splitting the medical domain concepts (e.g., causes, effects, symptoms, tests in human body systems (e.g., respiratory, cardiovascular, though maintaining the holistic perspective through the links between common concepts.

  12. Diagnosis of Alzheimer’s Disease Using Dual-Tree Complex Wavelet Transform, PCA, and Feed-Forward Neural Network

    Directory of Open Access Journals (Sweden)

    Debesh Jha

    2017-01-01

    Full Text Available Background. Error-free diagnosis of Alzheimer’s disease (AD from healthy control (HC patients at an early stage of the disease is a major concern, because information about the condition’s severity and developmental risks present allows AD sufferer to take precautionary measures before irreversible brain damage occurs. Recently, there has been great interest in computer-aided diagnosis in magnetic resonance image (MRI classification. However, distinguishing between Alzheimer’s brain data and healthy brain data in older adults (age > 60 is challenging because of their highly similar brain patterns and image intensities. Recently, cutting-edge feature extraction technologies have found extensive application in numerous fields, including medical image analysis. Here, we propose a dual-tree complex wavelet transform (DTCWT for extracting features from an image. The dimensionality of feature vector is reduced by using principal component analysis (PCA. The reduced feature vector is sent to feed-forward neural network (FNN to distinguish AD and HC from the input MR images. These proposed and implemented pipelines, which demonstrate improvements in classification output when compared to that of recent studies, resulted in high and reproducible accuracy rates of 90.06 ± 0.01% with a sensitivity of 92.00 ± 0.04%, a specificity of 87.78 ± 0.04%, and a precision of 89.6 ± 0.03% with 10-fold cross-validation.

  13. On the Use of Local Search in the Evolution of Neural Networks for the Diagnosis of Breast Cancer

    Directory of Open Access Journals (Sweden)

    Agam Gupta

    2015-07-01

    Full Text Available With the advancement in the field of Artificial Intelligence, there have been considerable efforts to develop technologies for pattern recognition related to medical diagnosis. Artificial Neural Networks (ANNs, a significant piece of Artificial Intelligence forms the base for most of the marvels in the former field. However, ANNs face the problem of premature convergence at a local minimum and inability to set hyper-parameters (like the number of neurons, learning rate, etc. while using Back Propagation Algorithm (BPA. In this paper, we have used the Genetic Algorithm (GA for the evolution of the ANN, which overcomes the limitations of the BPA. Since GA alone cannot fit for a high-dimensional, complex and multi-modal optimization landscape of the ANN, BPA is used as a local search algorithm to aid the evolution. The contributions of GA and BPA in the resultant approach are adjudged to determine the magnitude of local search necessary for optimization, striking a clear balance between exploration and exploitation in the evolution. The algorithm was applied to deal with the problem of Breast Cancer diagnosis. Results showed that under optimal settings, hybrid algorithm performs better than BPA or GA alone.

  14. Fault Diagnosis for Distribution Networks Using Enhanced Support Vector Machine Classifier with Classical Multidimensional Scaling

    Directory of Open Access Journals (Sweden)

    Ming-Yuan Cho

    2017-09-01

    Full Text Available In this paper, a new fault diagnosis techniques based on time domain reflectometry (TDR method with pseudo-random binary sequence (PRBS stimulus and support vector machine (SVM classifier has been investigated to recognize the different types of fault in the radial distribution feeders. This novel technique has considered the amplitude of reflected signals and the peaks of cross-correlation (CCR between the reflected and incident wave for generating fault current dataset for SVM. Furthermore, this multi-layer enhanced SVM classifier is combined with classical multidimensional scaling (CMDS feature extraction algorithm and kernel parameter optimization to increase training speed and improve overall classification accuracy. The proposed technique has been tested on a radial distribution feeder to identify ten different types of fault considering 12 input features generated by using Simulink software and MATLAB Toolbox. The success rate of SVM classifier is over 95% which demonstrates the effectiveness and the high accuracy of proposed method.

  15. Milling tool wear diagnosis by feed motor current signal using an artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Khajavi, Mehrdad Nouri; Nasernia, Ebrahim; Rostaghi, Mostafa [Dept. of Mechanical Engineering, Shahid Rajaee Teacher Training University, Tehran (Iran, Islamic Republic of)

    2016-11-15

    In this paper, a Multi-layer perceptron (MLP) neural network was used to predict tool wear in face milling. For this purpose, a series of experiments was conducted using a milling machine on a CK45 work piece. Tool wear was measured by an optical microscope. To improve the accuracy and reliability of the monitoring system, tool wear state was classified into five groups, namely, no wear, slight wear, normal wear, severe wear and broken tool. Experiments were conducted with the aforementioned tool wear states, and different machining conditions and data were extracted. An increase in current amplitude was observed as the tool wear increased. Furthermore, effects of parameters such as tool wear, feed, and cut depth on motor current consumption were analyzed. Considering the complexity of the wear state classification, a multi-layer neural network was used. The root mean square of motor current, feed, cut depth, and tool rpm were chosen as the input and amount of flank wear as the output of MLP. Results showed good performance of the designed tool wear monitoring system.

  16. New York hospital group striving for brand recognition. HealthStar Network.

    Science.gov (United States)

    Herreria, J

    1998-01-01

    HealthStar Network established a new concept in its eastern market--a group of hospitals forming one association. Marketers of HealthStar are conducting a branding campaign to distinguish individual expertise under one umbrella company.

  17. Health professional networks as a vector for improving healthcare quality and safety: a systematic review.

    Science.gov (United States)

    Cunningham, Frances C; Ranmuthugala, Geetha; Plumb, Jennifer; Georgiou, Andrew; Westbrook, Johanna I; Braithwaite, Jeffrey

    2012-03-01

    While there is a considerable corpus of theoretical and empirical literature on networks within and outside of the health sector, multiple research questions are yet to be answered. To conduct a systematic review of studies of professionals' network structures, identifying factors associated with network effectiveness and sustainability, particularly in relation to quality of care and patient safety. The authors searched MEDLINE, CINAHL, EMBASE, Web of Science and Business Source Premier from January 1995 to December 2009. A majority of the 26 unique studies identified used social network analysis to examine structural relationships in networks: structural relationships within and between networks, health professionals and their social context, health collaboratives and partnerships, and knowledge sharing networks. Key aspects of networks explored were administrative and clinical exchanges, network performance, integration, stability and influences on the quality of healthcare. More recent studies show that cohesive and collaborative health professional networks can facilitate the coordination of care and contribute to improving quality and safety of care. Structural network vulnerabilities include cliques, professional and gender homophily, and over-reliance on central agencies or individuals. Effective professional networks employ natural structural network features (eg, bridges, brokers, density, centrality, degrees of separation, social capital, trust) in producing collaboratively oriented healthcare. This requires efficient transmission of information and social and professional interaction within and across networks. For those using networks to improve care, recurring success factors are understanding your network's characteristics, attending to its functioning and investing time in facilitating its improvement. Despite this, there is no guarantee that time spent on networks will necessarily improve patient care.

  18. Validation of diabetes mellitus and hypertension diagnosis in computerized medical records in primary health care

    Directory of Open Access Journals (Sweden)

    Abánades-Herranz Juan C

    2011-10-01

    Full Text Available Abstract Background Computerized Clinical Records, which are incorporated in primary health care practice, have great potential for research. In order to use this information, data quality and reliability must be assessed to prevent compromising the validity of the results. The aim of this study is to validate the diagnosis of hypertension and diabetes mellitus in the computerized clinical records of primary health care, taking the diagnosis criteria established in the most prominently used clinical guidelines as the gold standard against which what measure the sensitivity, specificity, and determine the predictive values. The gold standard for diabetes mellitus was the diagnostic criteria established in 2003 American Diabetes Association Consensus Statement for diabetic subjects. The gold standard for hypertension was the diagnostic criteria established in the Joint National Committee published in 2003. Methods A cross-sectional multicentre validation study of diabetes mellitus and hypertension diagnoses in computerized clinical records of primary health care was carried out. Diagnostic criteria from the most prominently clinical practice guidelines were considered for standard reference. Sensitivity, specificity, positive and negative predictive values, and global agreement (with kappa index, were calculated. Results were shown overall and stratified by sex and age groups. Results The agreement for diabetes mellitus with the reference standard as determined by the guideline was almost perfect (κ = 0.990, with a sensitivity of 99.53%, a specificity of 99.49%, a positive predictive value of 91.23% and a negative predictive value of 99.98%. Hypertension diagnosis showed substantial agreement with the reference standard as determined by the guideline (κ = 0.778, the sensitivity was 85.22%, the specificity 96.95%, the positive predictive value 85.24%, and the negative predictive value was 96.95%. Sensitivity results were worse in patients who

  19. Distributed Prognostics and Health Management with a Wireless Network Architecture

    Science.gov (United States)

    Goebel, Kai; Saha, Sankalita; Sha, Bhaskar

    2013-01-01

    A heterogeneous set of system components monitored by a varied suite of sensors and a particle-filtering (PF) framework, with the power and the flexibility to adapt to the different diagnostic and prognostic needs, has been developed. Both the diagnostic and prognostic tasks are formulated as a particle-filtering problem in order to explicitly represent and manage uncertainties in state estimation and remaining life estimation. Current state-of-the-art prognostic health management (PHM) systems are mostly centralized in nature, where all the processing is reliant on a single processor. This can lead to a loss in functionality in case of a crash of the central processor or monitor. Furthermore, with increases in the volume of sensor data as well as the complexity of algorithms, traditional centralized systems become for a number of reasons somewhat ungainly for successful deployment, and efficient distributed architectures can be more beneficial. The distributed health management architecture is comprised of a network of smart sensor devices. These devices monitor the health of various subsystems or modules. They perform diagnostics operations and trigger prognostics operations based on user-defined thresholds and rules. The sensor devices, called computing elements (CEs), consist of a sensor, or set of sensors, and a communication device (i.e., a wireless transceiver beside an embedded processing element). The CE runs in either a diagnostic or prognostic operating mode. The diagnostic mode is the default mode where a CE monitors a given subsystem or component through a low-weight diagnostic algorithm. If a CE detects a critical condition during monitoring, it raises a flag. Depending on availability of resources, a networked local cluster of CEs is formed that then carries out prognostics and fault mitigation by efficient distribution of the tasks. It should be noted that the CEs are expected not to suspend their previous tasks in the prognostic mode. When the

  20. German cooperation-network 'equity in health'-health promotion in settings.

    Science.gov (United States)

    Mielck, Andreas; Kilian, Holger; Lehmann, Frank; Richter-Kornweitz, Antje; Kaba-Schönstein, Lotte

    2018-04-01

    In 2003, the German Federal Centre for Health Education (BZgA) initiated the national Cooperation-Network (CN) 'Equity in Health'. The CN is constantly increasing in size and scope, supporting setting approaches aimed at reducing health inequalities. A detailed description of the CN has not yet been available in English. The CN comprises a total of 66 institutional cooperation partners. Information concerning the structure and activities can be found on a special website. Coordination Centres (CC) have been established in the 16 federal states, for the coordination of all state-specific activities. Funding for the CN and CC is provided by the BZgA, the German statutory sickness funds and by the state-specific ministries of health. These partners also support the continuous quality improvement, which is based on the good-practice criteria developed by the Advisory Committee of the CN. In 2011, the 'Municipal Partner Process (MPP)' has been launched, specifically supporting local partners and integrated life-course approaches focussing on children. In 2015, the focus has been widened to include all age-groups. In July 2015, a new national health law concerning health promotion and prevention has been ratified by the federal Parliament, with a focus on reducing health inequalities. Currently, the details of its implementation are discussed on a nationwide basis. The CN has long advocated for such a law, and today the CN is a well-accepted partner providing concepts, methods and a strong and long-standing network. The article closes with future challenges faced by the CN.

  1. [Usefulness of serological studies for the early diagnosis of Lyme disease in Primary Health Care Centres].

    Science.gov (United States)

    Vázquez-López, María Esther; Fernández, Gonzalo; Díaz, Pablo; Díez-Morrondo, Carolina; Pego-Reigosa, Robustiano; Coira-Nieto, Amparo

    2018-01-01

    The main aim of this study was to determine the usefulness of an early diagnosis of Lyme disease (LD) in Primary Health Care Centres (PHCC) using the ELISA test as serological screening technique. A retrospective study (2006-2013) was performed in order to determine the anti-Borrelia seropositivity in 2,842 people at risk of having LD. The possible relationship between the environment and the area of residence with anti-Borrelia seropositivity was also studied according to the origin of the specimens (PHCC/Hospital). Overall, 15.2% of samples were positive to Borrelia spp. Seropositivity was significantly higher in samples sent by PHCC doctors than those sent by Hospital doctors. Seropositivity was significantly higher in rural than in urban populations and in those who live in mountainous or flat areas. The percentage of seropositivity has increased over the years. The role of the PHCC doctor is essential for achieving an early diagnosis of Lyme disease, as a higher percentage of seropositives was detected in samples submitted from PHCC. Furthermore, most early localised LD patients were diagnosed in PHCC, avoiding the appearance of sequelae. Therefore, detection of Borrelia specific antibodies using an ELISA assay is a useful screening test for patients at risk of LD. Copyright © 2017 Elsevier España, S.L.U. All rights reserved.

  2. Failure Diagnosis and Prognosis of Rolling - Element Bearings using Artificial Neural Networks: A Critical Overview

    Science.gov (United States)

    Rao, B. K. N.; Srinivasa Pai, P.; Nagabhushana, T. N.

    2012-05-01

    Rolling - Element Bearings are extensively used in almost all global industries. Any critical failures in these vitally important components would not only affect the overall systems performance but also its reliability, safety, availability and cost-effectiveness. Proactive strategies do exist to minimise impending failures in real time and at a minimum cost. Continuous innovative developments are taking place in the field of Artificial Neural Networks (ANNs) technology. Significant research and development are taking place in many universities, private and public organizations and a wealth of published literature is available highlighting the potential benefits of employing ANNs in intelligently monitoring, diagnosing, prognosing and managing rolling-element bearing failures. This paper attempts to critically review the recent trends in this topical area of interest.

  3. Failure Diagnosis and Prognosis of Rolling - Element Bearings using Artificial Neural Networks: A Critical Overview

    International Nuclear Information System (INIS)

    Rao, B K N; Pai, P Srinivasa; Nagabhushana, T N

    2012-01-01

    Rolling - Element Bearings are extensively used in almost all global industries. Any critical failures in these vitally important components would not only affect the overall systems performance but also its reliability, safety, availability and cost-effectiveness. Proactive strategies do exist to minimise impending failures in real time and at a minimum cost. Continuous innovative developments are taking place in the field of Artificial Neural Networks (ANNs) technology. Significant research and development are taking place in many universities, private and public organizations and a wealth of published literature is available highlighting the potential benefits of employing ANNs in intelligently monitoring, diagnosing, prognosing and managing rolling-element bearing failures. This paper attempts to critically review the recent trends in this topical area of interest.

  4. Efficacy of an artificial neural network-based approach to endoscopic ultrasound elastography in diagnosis of focal pancreatic masses.

    Science.gov (United States)

    Săftoiu, Adrian; Vilmann, Peter; Gorunescu, Florin; Janssen, Jan; Hocke, Michael; Larsen, Michael; Iglesias-Garcia, Julio; Arcidiacono, Paolo; Will, Uwe; Giovannini, Marc; Dietrich, Cristoph F; Havre, Roald; Gheorghe, Cristian; McKay, Colin; Gheonea, Dan Ionuţ; Ciurea, Tudorel

    2012-01-01

    By using strain assessment, real-time endoscopic ultrasound (EUS) elastography provides additional information about a lesion's characteristics in the pancreas. We assessed the accuracy of real-time EUS elastography in focal pancreatic lesions using computer-aided diagnosis by artificial neural network analysis. We performed a prospective, blinded, multicentric study at of 258 patients (774 recordings from EUS elastography) who were diagnosed with chronic pancreatitis (n = 47) or pancreatic adenocarcinoma (n = 211) from 13 tertiary academic medical centers in Europe (the European EUS Elastography Multicentric Study Group). We used postprocessing software analysis to compute individual frames of elastography movies recorded by retrieving hue histogram data from a dynamic sequence of EUS elastography into a numeric matrix. The data then were analyzed in an extended neural network analysis, to automatically differentiate benign from malignant patterns. The neural computing approach had 91.14% training accuracy (95% confidence interval [CI], 89.87%-92.42%) and 84.27% testing accuracy (95% CI, 83.09%-85.44%). These results were obtained using the 10-fold cross-validation technique. The statistical analysis of the classification process showed a sensitivity of 87.59%, a specificity of 82.94%, a positive predictive value of 96.25%, and a negative predictive value of 57.22%. Moreover, the corresponding area under the receiver operating characteristic curve was 0.94 (95% CI, 0.91%-0.97%), which was significantly higher than the values obtained by simple mean hue histogram analysis, for which the area under the receiver operating characteristic was 0.85. Use of the artificial intelligence methodology via artificial neural networks supports the medical decision process, providing fast and accurate diagnoses. Copyright © 2012 AGA Institute. Published by Elsevier Inc. All rights reserved.

  5. Social Networks in Later Life: Weighing Positive and Negative Effects on Health and Well-Being.

    Science.gov (United States)

    Rook, Karen S

    2015-02-01

    Social networks provide a mix of positive and negative experiences. Network members can provide help in times of need and day-to-day companionship, but they can also behave in ways that are inconsiderate, hurtful, or intrusive. Researchers must grapple with these dualities in order to develop a comprehensive understanding of how social network ties affect health and well-being. This article provides an overview of research that has examined the health-related effects of positive and negative aspects of social network involvement. If focuses on later life, a time when risks for declining health and for the loss or disruption of social relationships increase.

  6. Effective Interpersonal Health Communication for Linkage to Care After HIV Diagnosis in South Africa.

    Science.gov (United States)

    Mabuto, Tonderai; Charalambous, Salome; Hoffmann, Christopher J

    2017-01-01

    Early in the global response to HIV, health communication was focused toward HIV prevention. More recently, the role of health communication along the entire HIV care continuum has been highlighted. We sought to describe how a strategy of interpersonal communication allows for precision health communication to influence behavior regarding care engagement. We analyzed 1 to 5 transcripts from clients participating in longitudinal counseling sessions from a communication strategy arm of a randomized trial to accelerate entry into care in South Africa. The counseling arm was selected because it increased verified entry into care by 40% compared with the standard of care. We used thematic analysis to identify key aspects of communication directed specifically toward a client's goals or concerns. Of the participants, 18 of 28 were female and 21 entered HIV care within 90 days of diagnosis. Initiating a communication around client-perceived consequences of HIV was at times effective. However, counselors also probed around general topics of life disruption-such as potential for child bearing-as a technique to direct the conversation toward the participant's needs. Once individual concerns and needs were identified, counselors tried to introduce clinical care seeking and collaboratively discuss potential barriers and approaches to overcome to accessing that care. Through the use of interpersonal communication messages were focused on immediate needs and concerns of the client. When effectively delivered, it may be an important communication approach to improve care engagement.

  7. A Laboratory Test Expert System for Clinical Diagnosis Support in Primary Health Care

    Directory of Open Access Journals (Sweden)

    Rodrigo Fernandez-Millan

    2015-08-01

    Full Text Available Clinical Decision Support Systems have the potential to reduce lack of communication and errors in diagnostic steps in primary health care. Literature reports have showed great advances in clinical decision support systems in the recent years, which have proven its usefulness in improving the quality of care. However, most of these systems are focused on specific areas of diseases. In this way, we propose a rule-based expert system, which supports clinicians in primary health care, providing a list of possible diseases regarding patient’s laboratory tests results in order to assist previous diagnosis. Our system also allows storing and retrieving patient’s data and the history of patient’s analyses, establishing a basis for coordination between the various health care levels. A validation step and speed performance tests were made to check the quality of the system. We conclude that our system could improve clinician accuracy and speed, resulting in more efficiency and better quality of service. Finally, we propose some recommendations for further research.

  8. Diagnosis of Lithium-Ion Batteries State-of-Health based on Electrochemical Impedance Spectroscopy Technique

    DEFF Research Database (Denmark)

    Stroe, Daniel Ioan; Swierczynski, Maciej Jozef; Stan, Ana-Irina

    2014-01-01

    Lithium-ion batteries have developed into a popular energy storage choice for a wide range of applications because of their superior characteristics in comparison to other energy storage technologies. Besides modelling the performance behavior of Lithium-ion batteries, it has become of huge...... interest to accurately diagnose their state-of-health (SOH). At present, Lithium-ion batteries are diagnosed by performing capacity or resistance (current pulse) measurements; however, in the majority of the cases, these measurements are time consuming and result in changing the state of the battery...... as well. This paper investigates the use of the electrochemical impedance spectroscopy (EIS) technique for SOH diagnosis of Lithium-ion battery cells, instead of using the aforementioned techniques, since this new method allows for online and direct measurement of the battery cell response in any working...

  9. A power supply design of body sensor networks for health monitoring of neonates

    NARCIS (Netherlands)

    Chen, W.; Sonntag, C.L.W.; Boesten, F.; Bambang Oetomo, S.; Feijs, L.M.G.

    2008-01-01

    Critically ill new born babies are extremely tiny and vulnerable to external disturbance. Non-invasive health monitoring with body sensor networks is crucial for the survival of these neonates and the quality of their life later on. A key question for health monitoring with body sensor networks is

  10. The Role of Social Support and Social Networks in Health Information Seeking Behavior among Korean Americans

    Science.gov (United States)

    Kim, Wonsun

    2013-01-01

    Access to health information appears to be a crucial piece of the racial and ethnic health disparities puzzle among immigrants. There are a growing number of scholars who are investigating the role of social networks that have shown that the number and even types of social networks among minorities and lower income groups differ (Chatman, 1991;…

  11. Implementing multiple intervention strategies in Dutch public health-related policy networks

    NARCIS (Netherlands)

    Harting, Janneke; Peters, Dorothee; Grêaux, Kimberly; van Assema, Patricia; Verweij, Stefan; Stronks, Karien; Klijn, Erik-Hans

    2017-01-01

    Improving public health requires multiple intervention strategies. Implementing such an intervention mix is supposed to require a multisectoral policy network. As evidence to support this assumption is scarce, we examined under which conditions public health-related policy networks were able to

  12. Fault diagnosis of sensor networked structures with multiple faults using a virtual beam based approach

    Science.gov (United States)

    Wang, H.; Jing, X. J.

    2017-07-01

    This paper presents a virtual beam based approach suitable for conducting diagnosis of multiple faults in complex structures with limited prior knowledge of the faults involved. The "virtual beam", a recently-proposed concept for fault detection in complex structures, is applied, which consists of a chain of sensors representing a vibration energy transmission path embedded in the complex structure. Statistical tests and adaptive threshold are particularly adopted for fault detection due to limited prior knowledge of normal operational conditions and fault conditions. To isolate the multiple faults within a specific structure or substructure of a more complex one, a 'biased running' strategy is developed and embedded within the bacterial-based optimization method to construct effective virtual beams and thus to improve the accuracy of localization. The proposed method is easy and efficient to implement for multiple fault localization with limited prior knowledge of normal conditions and faults. With extensive experimental results, it is validated that the proposed method can localize both single fault and multiple faults more effectively than the classical trust index subtract on negative add on positive (TI-SNAP) method.

  13. "Diagnosis of sleep apnea in network" respiratory polygraphy as a decentralization strategy.

    Science.gov (United States)

    Borsini, Eduardo; Blanco, Magali; Bosio, Martin; Fernando, Di Tullio; Ernst, Glenda; Salvado, Alejandro

    2016-01-01

    Obstructive sleep apnea syndrome (OSA) is diagnosed through polysomnography (PSG) or respiratory polygraphy (RP). Self-administered home-based RP using devices with data transmission could facilitate diagnosis in distant populations. The purpose of this work was to describe a telemedicine initiative using RP in four satellite outpatient care clinics (OCC) of Buenos Aires Hospital Británico Central (HBC). OCC technicians were trained both in the use of RP. Raw signals were sent to HBC via intranet software for scoring and final report. During a 24-month 499 RP were performed in 499 patients: 303 men (60.7%) with the following characteristics (mean and standard deviation): valid time for manual analysis: 392.8 min (±100.1), AHI: 17.05 (±16.49 and percentile 25-75 [Pt]: 5-23) ev/hour, ODI (criterion 3%): 18.05 (±16.48 and Pt 25-75: 6-25) ev/hour, and time below 90% (Ttransmission, and low rate of missing data.

  14. Experience of a Neural Network Imitator Applied to Diagnosis of Pre-pathological Conditions in Humans

    International Nuclear Information System (INIS)

    Belyashov, D.N.; Emelyanova, I.V.; Tichshenko, A.V.; Makarenko, N.G.; Sultanova, B.G.

    1998-01-01

    The Governmental Resolution of the RK 'Program of Medical Rehabilitation for People Influenced by Nuclear Tests at STS in 1949-1990' was published in March 1997. Implementation of the program requires first of all to create the effective methods of operative diagnostics of arid zones' population. To our mind, for this aims systems analysis with elements of neural network classification is more effective. We demonstrate such an approach using the example of the modem diagnostics system creating to detect the pre-pathological states among population by express analysis and personal particulars. The following considerations were used in the base of the training set: 1) any formalism must be based oneself upon wealth of phenomenology (experience, intuition, the presence of symptoms); 2) typical attributes of disease can be divided on 2 groups - subjective and objective. The common state of patient is characterised by the first group and it can have no intercommunication with disease. The second one is obtained by laboratory inspection and it is not connected with patient sensations. Each of the objective at-tributes can be the attribute of several illnesses at once. In this case both the subjective and objective features must be used together; 3) acceptability of any scheme can be substantiated only statistically. The question about justifiability and sufficiency of training set always demands separate discussion. Personal particulars are more available for creating training set. The set must be professionally oriented in order to reduce of selection effects. For our experiment the fully-connected neural network ( computer software, imitating the work of neural computer) 'Multi Neuron' was chosen. Feature space using for the net work was created from the 206 personal particulars. The research aimed to determine pre-pathological states of the urinary system organs among industrial, office and professional workers in the mining industry connected with phosphorus

  15. The Relationship of Policymaking and Networking Characteristics among Leaders of Large Urban Health Departments.

    Science.gov (United States)

    Leider, Jonathon P; Castrucci, Brian C; Harris, Jenine K; Hearne, Shelley

    2015-08-06

    The relationship between policy networks and policy development among local health departments (LHDs) is a growing area of interest to public health practitioners and researchers alike. In this study, we examine policy activity and ties between public health leadership across large urban health departments. This study uses data from a national profile of local health departments as well as responses from a survey sent to three staff members (local health official, chief of policy, chief science officer) in each of 16 urban health departments in the United States. Network questions related to frequency of contact with health department personnel in other cities. Using exponential random graph models, network density and centrality were examined, as were patterns of communication among those working on several policy areas using exponential random graph models. All 16 LHDs were active in communicating about chronic disease as well as about use of alcohol, tobacco, and other drugs (ATOD). Connectedness was highest among local health officials (density = .55), and slightly lower for chief science officers (d = .33) and chiefs of policy (d = .29). After accounting for organizational characteristics, policy homophily (i.e., when two network members match on a single characteristic) and tenure were the most significant predictors of formation of network ties. Networking across health departments has the potential for accelerating the adoption of public health policies. This study suggests similar policy interests and formation of connections among senior leadership can potentially drive greater connectedness among other staff.

  16. The Relationship of Policymaking and Networking Characteristics among Leaders of Large Urban Health Departments

    Directory of Open Access Journals (Sweden)

    Jonathon P. Leider

    2015-08-01

    Full Text Available Background: The relationship between policy networks and policy development among local health departments (LHDs is a growing area of interest to public health practitioners and researchers alike. In this study, we examine policy activity and ties between public health leadership across large urban health departments. Methods: This study uses data from a national profile of local health departments as well as responses from a survey sent to three staff members (local health official, chief of policy, chief science officer in each of 16 urban health departments in the United States. Network questions related to frequency of contact with health department personnel in other cities. Using exponential random graph models, network density and centrality were examined, as were patterns of communication among those working on several policy areas using exponential random graph models. Results: All 16 LHDs were active in communicating about chronic disease as well as about use of alcohol, tobacco, and other drugs (ATOD. Connectedness was highest among local health officials (density = .55, and slightly lower for chief science officers (d = .33 and chiefs of policy (d = .29. After accounting for organizational characteristics, policy homophily (i.e., when two network members match on a single characteristic and tenure were the most significant predictors of formation of network ties. Conclusion: Networking across health departments has the potential for accelerating the adoption of public health policies. This study suggests similar policy interests and formation of connections among senior leadership can potentially drive greater connectedness among other staff.

  17. Identifying socio-ecological networks in rural-urban gradients: Diagnosis of a changing cultural landscape.

    Science.gov (United States)

    Arnaiz-Schmitz, C; Schmitz, M F; Herrero-Jáuregui, C; Gutiérrez-Angonese, J; Pineda, F D; Montes, C

    2018-01-15

    Socio-ecological systems maintain reciprocal interactions between biophysical and socioeconomic structures. As a result of these interactions key essential services for society emerge. Urban expansion is a direct driver of land change and cause serious shifts in socio-ecological relationships and the associated lifestyles. The framework of rural-urban gradients has proved to be a powerful tool for ecological research about urban influences on ecosystems and on sociological issues related to social welfare. However, to date there has not been an attempt to achieve a classification of municipalities in rural-urban gradients based on socio-ecological interactions. In this paper, we developed a methodological approach that allows identifying and classifying a set of socio-ecological network configurations in the Region of Madrid, a highly dynamic cultural landscape considered one of the European hotspots in urban development. According to their socio-ecological links, the integrated model detects four groups of municipalities, ordered along a rural-urban gradient, characterized by their degree of biophysical and socioeconomic coupling and different indicators of landscape structure and social welfare. We propose the developed model as a useful tool to improve environmental management schemes and land planning from a socio-ecological perspective, especially in territories subject to intense urban transformations and loss of rurality. Copyright © 2017 Elsevier B.V. All rights reserved.

  18. The training for health equity network evaluation framework: a pilot study at five health professional schools.

    Science.gov (United States)

    Ross, Simone J; Preston, Robyn; Lindemann, Iris C; Matte, Marie C; Samson, Rex; Tandinco, Filedito D; Larkins, Sarah L; Palsdottir, Bjorg; Neusy, Andre-Jacques

    2014-01-01

    The Training for Health Equity Network (THEnet), a group of diverse health professional schools aspiring toward social accountability, developed and pilot tested a comprehensive evaluation framework to assess progress toward socially accountable health professions education. The evaluation framework provides criteria for schools to assess their level of social accountability within their organization and planning; education, research and service delivery; and the direct and indirect impacts of the school and its graduates, on the community and health system. This paper describes the pilot implementation of testing the evaluation framework across five THEnet schools, and examines whether the evaluation framework was practical and feasible across contexts for the purposes of critical reflection and continuous improvement in terms of progress towards social accountability. In this pilot study, schools utilized the evaluation framework using a mixed method approach of data collection comprising of workshops, qualitative interviews and focus group discussions, document review and collation and analysis of existing quantitative data. The evaluation framework allowed each school to contextually gather evidence on how it was meeting the aspirational goals of social accountability across a range of school activities, and to identify strengths and areas for improvement and development. The evaluation framework pilot study demonstrated how social accountability can be assessed through a critically reflective and comprehensive process. As social accountability focuses on the relationship between health professions schools and health system and health population outcomes, each school was able to demonstrate to students, health professionals, governments, accrediting bodies, communities and other stakeholders how current and future health care needs of populations are addressed in terms of education, research, and service learning.

  19. Social network types among older Korean adults: Associations with subjective health.

    Science.gov (United States)

    Sohn, Sung Yun; Joo, Won-Tak; Kim, Woo Jung; Kim, Se Joo; Youm, Yoosik; Kim, Hyeon Chang; Park, Yeong-Ran; Lee, Eun

    2017-01-01

    With population aging now a global phenomenon, the health of older adults is becoming an increasingly important issue. Because the Korean population is aging at an unprecedented rate, preparing for public health problems associated with old age is particularly salient in this country. As the physical and mental health of older adults is related to their social relationships, investigating the social networks of older adults and their relationship to health status is important for establishing public health policies. The aims of this study were to identify social network types among older adults in South Korea and to examine the relationship of these social network types with self-rated health and depression. Data from the Korean Social Life, Health, and Aging Project were analyzed. Model-based clustering using finite normal mixture modeling was conducted to identify the social network types based on ten criterion variables of social relationships and activities: marital status, number of children, number of close relatives, number of friends, frequency of attendance at religious services, attendance at organized group meetings, in-degree centrality, out-degree centrality, closeness centrality, and betweenness centrality. Multivariate regression analysis was conducted to examine associations between the identified social network types and self-rated health and depression. The model-based clustering analysis revealed that social networks clustered into five types: diverse, family, congregant, congregant-restricted, and restricted. Diverse or family social network types were significantly associated with more favorable subjective mental health, whereas the restricted network type was significantly associated with poorer ratings of mental and physical health. In addition, our analysis identified unique social network types related to religious activities. In summary, we developed a comprehensive social network typology for older Korean adults. Copyright © 2016

  20. Structural integration and performance of inter-sectoral public health-related policy networks: An analysis across policy phases

    NARCIS (Netherlands)

    Peters, D. T. J. M.; Raab, J.; Grêaux, K. M.; Stronks, K.; Harting, J.

    2017-01-01

    Background: Inter-sectoral policy networks may be effective in addressing environmental determinants of health with interventions. However, contradictory results are reported on relations between structural network characteristics (i.e., composition and integration) and network performance, such as

  1. Structural integration and performance of inter-sectoral public health-related policy networks : An analysis across policy phases

    NARCIS (Netherlands)

    Peters, Dorothee; Raab, J.; Grêaux, Kimberley M.; Stronks, Karien; Harting, Janneke

    2017-01-01

    Background: Inter-sectoral policy networks may be effective in addressing environmental determinants of health with interventions. However, contradictory results are reported on relations between structure and network characteristics (i.e., composition and integration) and network performance, such

  2. Social Networking Addiction among Health Sciences Students in Oman

    Directory of Open Access Journals (Sweden)

    Ken Masters

    2015-08-01

    Full Text Available Objectives: Addiction to social networking sites (SNSs is an international issue with numerous methods of measurement. The impact of such addictions among health science students is of particular concern. This study aimed to measure SNS addiction rates among health sciences students at Sultan Qaboos University (SQU in Muscat, Oman. Methods: In April 2014, an anonymous English-language six-item electronic self-reporting survey based on the Bergen Facebook Addiction Scale was administered to a non-random cohort of 141 medical and laboratory science students at SQU. The survey was used to measure usage of three SNSs: Facebook (Facebook Inc., Menlo Park, California, USA, YouTube (YouTube, San Bruno, California, USA and Twitter (Twitter Inc., San Francisco, California, USA. Two sets of criteria were used to calculate addiction rates (a score of 3 on at least four survey items or a score of 3 on all six items. Work-related SNS usage was also measured. Results: A total of 81 students completed the survey (response rate: 57.4%. Of the three SNSs, YouTube was most commonly used (100%, followed by Facebook (91.4% and Twitter (70.4%. Usage and addiction rates varied significantly across the three SNSs. Addiction rates to Facebook, YouTube and Twitter, respectively, varied according to the criteria used (14.2%, 47.2% and 33.3% versus 6.3%, 13.8% and 12.8%. However, addiction rates decreased when workrelated activity was taken into account. Conclusion: Rates of SNS addiction among this cohort indicate a need for intervention. Additionally, the results suggest that addiction to individual SNSs should be measured and that workrelated activities should be taken into account during measurement.

  3. Social Networking Addiction among Health Sciences Students in Oman.

    Science.gov (United States)

    Masters, Ken

    2015-08-01

    Addiction to social networking sites (SNSs) is an international issue with numerous methods of measurement. The impact of such addictions among health science students is of particular concern. This study aimed to measure SNS addiction rates among health sciences students at Sultan Qaboos University (SQU) in Muscat, Oman. In April 2014, an anonymous English-language six-item electronic self-reporting survey based on the Bergen Facebook Addiction Scale was administered to a non-random cohort of 141 medical and laboratory science students at SQU. The survey was used to measure usage of three SNSs: Facebook (Facebook Inc., Menlo Park, California, USA), YouTube (YouTube, San Bruno, California, USA) and Twitter (Twitter Inc., San Francisco, California, USA). Two sets of criteria were used to calculate addiction rates (a score of 3 on at least four survey items or a score of 3 on all six items). Work-related SNS usage was also measured. A total of 81 students completed the survey (response rate: 57.4%). Of the three SNSs, YouTube was most commonly used (100%), followed by Facebook (91.4%) and Twitter (70.4%). Usage and addiction rates varied significantly across the three SNSs. Addiction rates to Facebook, YouTube and Twitter, respectively, varied according to the criteria used (14.2%, 47.2% and 33.3% versus 6.3%, 13.8% and 12.8%). However, addiction rates decreased when work-related activity was taken into account. Rates of SNS addiction among this cohort indicate a need for intervention. Additionally, the results suggest that addiction to individual SNSs should be measured and that work-related activities should be taken into account during measurement.

  4. Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network.

    Directory of Open Access Journals (Sweden)

    Seung Seog Han

    Full Text Available Although there have been reports of the successful diagnosis of skin disorders using deep learning, unrealistically large clinical image datasets are required for artificial intelligence (AI training. We created datasets of standardized nail images using a region-based convolutional neural network (R-CNN trained to distinguish the nail from the background. We used R-CNN to generate training datasets of 49,567 images, which we then used to fine-tune the ResNet-152 and VGG-19 models. The validation datasets comprised 100 and 194 images from Inje University (B1 and B2 datasets, respectively, 125 images from Hallym University (C dataset, and 939 images from Seoul National University (D dataset. The AI (ensemble model; ResNet-152 + VGG-19 + feedforward neural networks results showed test sensitivity/specificity/ area under the curve values of (96.0 / 94.7 / 0.98, (82.7 / 96.7 / 0.95, (92.3 / 79.3 / 0.93, (87.7 / 69.3 / 0.82 for the B1, B2, C, and D datasets. With a combination of the B1 and C datasets, the AI Youden index was significantly (p = 0.01 higher than that of 42 dermatologists doing the same assessment manually. For B1+C and B2+ D dataset combinations, almost none of the dermatologists performed as well as the AI. By training with a dataset comprising 49,567 images, we achieved a diagnostic accuracy for onychomycosis using deep learning that was superior to that of most of the dermatologists who participated in this study.

  5. Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network.

    Science.gov (United States)

    Han, Seung Seog; Park, Gyeong Hun; Lim, Woohyung; Kim, Myoung Shin; Na, Jung Im; Park, Ilwoo; Chang, Sung Eun

    2018-01-01

    Although there have been reports of the successful diagnosis of skin disorders using deep learning, unrealistically large clinical image datasets are required for artificial intelligence (AI) training. We created datasets of standardized nail images using a region-based convolutional neural network (R-CNN) trained to distinguish the nail from the background. We used R-CNN to generate training datasets of 49,567 images, which we then used to fine-tune the ResNet-152 and VGG-19 models. The validation datasets comprised 100 and 194 images from Inje University (B1 and B2 datasets, respectively), 125 images from Hallym University (C dataset), and 939 images from Seoul National University (D dataset). The AI (ensemble model; ResNet-152 + VGG-19 + feedforward neural networks) results showed test sensitivity/specificity/ area under the curve values of (96.0 / 94.7 / 0.98), (82.7 / 96.7 / 0.95), (92.3 / 79.3 / 0.93), (87.7 / 69.3 / 0.82) for the B1, B2, C, and D datasets. With a combination of the B1 and C datasets, the AI Youden index was significantly (p = 0.01) higher than that of 42 dermatologists doing the same assessment manually. For B1+C and B2+ D dataset combinations, almost none of the dermatologists performed as well as the AI. By training with a dataset comprising 49,567 images, we achieved a diagnostic accuracy for onychomycosis using deep learning that was superior to that of most of the dermatologists who participated in this study.

  6. Social networks, health promoting-behavior, and health-related quality of life in older Korean adults.

    Science.gov (United States)

    Hong, Minjoo; De Gagne, Jennie C; Shin, Hyewon

    2018-03-01

    In this cross-sectional, descriptive study, we compared the sociodemographic characteristics, social networks, health-promoting behavior, and the health-related quality of life of older Korean adults living in South Korea to those of older Korean adult immigrants living in the USA. A total of 354 older adults, aged 65 years or older, participated. Data were collected through self-directed questionnaires, and analyzed using a two way analysis of variance, t-tests, χ 2 -tests, and Pearson's correlation coefficient. The association between four sociodemographic characteristics and health-related quality of life was significantly different between the two groups. For the older Korean adults living in South Korea, positive correlations existed between a measure of their social networks and both health-promoting behavior and health-related quality of life. For the older Korean immigrants, the findings revealed a positive correlation only between social networks and health-promoting behavior. The study findings support the important association social networks can have with health-related quality of life, and their possible relationship to health-promoting behaviors of older Korean adults. We suggest that health policy-makers and healthcare providers develop comprehensive programs that are designed to improve older adults' social networks. © 2017 John Wiley & Sons Australia, Ltd.

  7. PRODIAG: Combined expert system/neural network for process fault diagnosis. Volume 1, Theory

    Energy Technology Data Exchange (ETDEWEB)

    Reifman, J.; Wei, T.Y.C.; Vitela, J.E.

    1995-09-01

    The function of the PRODIAG code is to diagnose on-line the root cause of a thermal-hydraulic (T-H) system transient with trace back to the identification of the malfunctioning component using the T-H instrumentation signals exclusively. The code methodology is based on the Al techniques of automated reasoning/expert systems (ES) and artificial neural networks (ANN). The research and development objective is to develop a generic code methodology which would be plant- and T-H-system-independent. For the ES part the only plant or T-H system specific code requirements would be implemented through input only and at that only through a Piping and Instrumentation Diagram (PID) database. For the ANN part the only plant or T-H system specific code requirements would be through the ANN training data for normal component characteristics and the same PID database information. PRODIAG would, therefore, be generic and portable from T-H system to T-H system and from plant to plant without requiring any code-related modifications except for the PID database and the ANN training with the normal component characteristics. This would give PRODIAG the generic feature which numerical simulation plant codes such as TRAC or RELAP5 have. As the code is applied to different plants and different T-H systems, only the connectivity information, the operating conditions and the normal component characteristics are changed, and the changes are made entirely through input. Verification and validation of PRODIAG would, be T-H system independent and would be performed only ``once``.

  8. Shorter time since inflammatory bowel disease diagnosis in children is associated with lower mental health in parents.

    Science.gov (United States)

    Werner, H; Braegger, Cp; Buehr, P; Koller, R; Nydegger, A; Spalinger, J; Heyland, K; Schibli, S; Landolt, Ma

    2015-01-01

    This study assessed the mental health of parents of children with inflammatory bowel disease (IBD), compared their mental health with age-matched and gender-matched references and examined parental and child predictors for mental health problems. A total of 125 mothers and 106 fathers of 125 children with active and inactive IBD from the Swiss IBD multicentre cohort study were included. Parental mental health was assessed by the Symptom Checklist 27 and child behaviour problems by the Strengths and Difficulties Questionnaire. Child medical data were extracted from hospital records. While the mothers reported lower mental health, the fathers' mental health was similar, or even better, than in age-matched and gender-matched community controls. In both parents, shorter time since the child's diagnosis was associated with poorer mental health. In addition, the presence of their own IBD diagnosis and child behaviour problems predicted maternal mental health problems. Parents of children with IBD may need professional support when their child is diagnosed, to mitigate distress. This, in turn, may help the child to adjust better to IBD. Particular attention should be paid to mothers who have their own IBD diagnosis and whose children display behaviour problems. ©2014 Foundation Acta Paediatrica. Published by John Wiley & Sons Ltd.

  9. Cooperative wireless network control based health and activity monitoring system.

    Science.gov (United States)

    Prakash, R; Ganesh, A Balaji; Girish, Siva V

    2016-10-01

    A real-time cooperative communication based wireless network is presented for monitoring health and activity of an end-user in their environment. The cooperative communication offers better energy consumption and also an opportunity to aware the current location of a user non-intrusively. The link between mobile sensor node and relay node is dynamically established by using Received Signal Strength Indicator (RSSI) and Link Quality Indicator (LQI) based on adaptive relay selection scheme. The study proposes a Linear Acceleration based Transmission Power Decision Control (LA-TPDC) algorithm to further enhance the energy efficiency of cooperative communication. Further, the occurrences of false alarms are carefully prevented by introducing three stages of sequential warning system. The real-time experiments are carried-out by using the nodes, namely mobile sensor node, relay nodes and a destination node which are indigenously developed by using a CC430 microcontroller integrated with an in-built transceiver at 868 MHz. The wireless node performance characteristics, such as energy consumption, Signal-Noise ratio (SNR), Bit Error Rate (BER), Packet Delivery Ratio (PDR) and transmission offset are evaluated for all the participated nodes. The experimental results observed that the proposed linear acceleration based transmission power decision control algorithm almost doubles the battery life time than energy efficient conventional cooperative communication.

  10. Energy Harvesting Based Body Area Networks for Smart Health.

    Science.gov (United States)

    Hao, Yixue; Peng, Limei; Lu, Huimin; Hassan, Mohammad Mehedi; Alamri, Atif

    2017-07-10

    Body area networks (BANs) are configured with a great number of ultra-low power consumption wearable devices, which constantly monitor physiological signals of the human body and thus realize intelligent monitoring. However, the collection and transfer of human body signals consume energy, and considering the comfort demand of wearable devices, both the size and the capacity of a wearable device's battery are limited. Thus, minimizing the energy consumption of wearable devices and optimizing the BAN energy efficiency is still a challenging problem. Therefore, in this paper, we propose an energy harvesting-based BAN for smart health and discuss an optimal resource allocation scheme to improve BAN energy efficiency. Specifically, firstly, considering energy harvesting in a BAN and the time limits of human body signal transfer, we formulate the energy efficiency optimization problem of time division for wireless energy transfer and wireless information transfer. Secondly, we convert the optimization problem into a convex optimization problem under a linear constraint and propose a closed-form solution to the problem. Finally, simulation results proved that when the size of data acquired by the wearable devices is small, the proportion of energy consumed by the circuit and signal acquisition of the wearable devices is big, and when the size of data acquired by the wearable devices is big, the energy consumed by the signal transfer of the wearable device is decisive.

  11. Electromechanical impedance-based health diagnosis for tendon and anchorage zone in a nuclear containment structure

    Science.gov (United States)

    Min, Jiyoung; Shim, Hyojin; Yun, Chung-Bang

    2012-04-01

    For a nuclear containment structure, the structural health monitoring is essential because of its high potential risk and grave social impact. In particular, the tendon and anchorage zone are to be monitored because they are under high tensile or compressive stress. In this paper, a method to monitor the tendon force and the condition of the anchorage zone is presented by using the impedance-based health diagnosis system. First, numerical simulations were conducted for cases with various loose tensile forces on the tendon as well as damages on the bearing plate and concrete structure. Then, experimental studies were carried out on a scaled model of the anchorage system. The relationship between the loose tensile force and the impedance-based damage index was analyzed by a regression analysis. When a structure gets damaged, the damage index increases so that the status of damage can be identified. The results of the numerical and experimental studies indicate a big potential of the proposed impedance-based method for monitoring the tendon and anchorage system.

  12. Research of diagnosis sensors fault based on correlation analysis of the bridge structural health monitoring system

    Science.gov (United States)

    Hu, Shunren; Chen, Weimin; Liu, Lin; Gao, Xiaoxia

    2010-03-01

    Bridge structural health monitoring system is a typical multi-sensor measurement system due to the multi-parameters of bridge structure collected from the monitoring sites on the river-spanning bridges. Bridge structure monitored by multi-sensors is an entity, when subjected to external action; there will be different performances to different bridge structure parameters. Therefore, the data acquired by each sensor should exist countless correlation relation. However, complexity of the correlation relation is decided by complexity of bridge structure. Traditionally correlation analysis among monitoring sites is mainly considered from physical locations. unfortunately, this method is so simple that it cannot describe the correlation in detail. The paper analyzes the correlation among the bridge monitoring sites according to the bridge structural data, defines the correlation of bridge monitoring sites and describes its several forms, then integrating the correlative theory of data mining and signal system to establish the correlation model to describe the correlation among the bridge monitoring sites quantificationally. Finally, The Chongqing Mashangxi Yangtze river bridge health measurement system is regards as research object to diagnosis sensors fault, and simulation results verify the effectiveness of the designed method and theoretical discussions.

  13. Social Network resources and self-rated health in a deprived Danish neighborhood

    DEFF Research Database (Denmark)

    Tanggaard Andersen, Pernille; Holst Algren, Maria; Fromsejer Heiberg, Regina

    2017-01-01

    Research has demonstrated that living in a deprived neighborhood contributes to the occurrence and development of poor health. Furthermore evidence shows that social networks are fundamental resources in preventing poor mental health. Neighborhood relationships and networks are vital for sustaining...... and improving quality of life. However, to determine potentials for public health action, the health impact of various types of network resources need to be explored and the association between socioeconomic position and self-rated health needs to be analysed to determine whether it is partially explained...... by social network resources. This is the main aim of this article. Cross-sectional data from one deprived neighborhood located in Denmark were collected in 2008 and 2013 using a postal health survey. The target group was defined as adults older than 16 years. In 2008, 408 residents participated...

  14. Clinician practice and the National Healthcare Safety Network definition for the diagnosis of catheter-associated urinary tract infection.

    Science.gov (United States)

    Al-Qas Hanna, Fadi; Sambirska, Oksana; Iyer, Sugantha; Szpunar, Susanna; Fakih, Mohamad G

    2013-12-01

    The National Healthcare Safety Network (NHSN) definition for catheter-associated urinary tract infection (CAUTI) is used to evaluate improvements in CAUTI prevention efforts. We assessed whether clinician practice was reflective of the NHSN definition. We evaluated all adult inpatients hospitalized between July 2010 and June 2011, with a first positive urine culture > 48 hours of admission obtained while catheterized or within 48 hours of catheter discontinuation. Data comprised patients' signs, symptoms, and diagnostic tests; clinician's diagnosis; and the impression of the infectious diseases (ID) consultant. The clinician's practice was compared with the NHSN definition and the ID consultant's impression. Antibiotics were initiated by clinicians to treat CAUTI in 216 of 387 (55.8%) cases, with 119 of 387 (30.7%) fitting the NHSN CAUTI definition, and 63 of 211 (29.9%) considered by ID to have a CAUTI. The sensitivity, specificity, and positive and negative predictive values of a clinician diagnosis of CAUTI were 62.2%, 47%, 34.3%, and 73.7% when compared with NHSN CAUTI definition (n = 387) and 100%, 57.4%, 50%, and 100% when compared with the ID consultant evaluation (n = 211), respectively. The positive predictive value of the NHSN CAUTI definition was 35.1% when compared with the ID consultant's impression (n = 211). NHSN CAUTI definition did not reflect clinician or ID consultant practices. Our findings reflect the differences between surveillance definitions and clinical practice. Copyright © 2013 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Mosby, Inc. All rights reserved.

  15. Adolescent Health in Hawai'i: The Adolescent Health Network's Teen Health Advisor Report.

    Science.gov (United States)

    Hawaii State Dept. of Health, Honolulu. Maternal and Child Health Branch.

    This publication reports on a survey to develop a profile of adolescent health in Hawaii in order to develop effective prevention and intervention strategies. The survey covered: general health status; family, peer, and school problems; depression and suicide; use of licit and illicit substances; sexuality and sexually transmitted diseases; and…

  16. Methods for Inferring Health-Related Social Networks among Coworkers from Online Communication Patterns

    Science.gov (United States)

    Matthews, Luke J.; DeWan, Peter; Rula, Elizabeth Y.

    2013-01-01

    Studies of social networks, mapped using self-reported contacts, have demonstrated the strong influence of social connections on the propensity for individuals to adopt or maintain healthy behaviors and on their likelihood to adopt health risks such as obesity. Social network analysis may prove useful for businesses and organizations that wish to improve the health of their populations by identifying key network positions. Health traits have been shown to correlate across friendship ties, but evaluating network effects in large coworker populations presents the challenge of obtaining sufficiently comprehensive network data. The purpose of this study was to evaluate methods for using online communication data to generate comprehensive network maps that reproduce the health-associated properties of an offline social network. In this study, we examined three techniques for inferring social relationships from email traffic data in an employee population using thresholds based on: (1) the absolute number of emails exchanged, (2) logistic regression probability of an offline relationship, and (3) the highest ranked email exchange partners. As a model of the offline social network in the same population, a network map was created using social ties reported in a survey instrument. The email networks were evaluated based on the proportion of survey ties captured, comparisons of common network metrics, and autocorrelation of body mass index (BMI) across social ties. Results demonstrated that logistic regression predicted the greatest proportion of offline social ties, thresholding on number of emails exchanged produced the best match to offline network metrics, and ranked email partners demonstrated the strongest autocorrelation of BMI. Since each method had unique strengths, researchers should choose a method based on the aspects of offline behavior of interest. Ranked email partners may be particularly useful for purposes related to health traits in a social network. PMID

  17. Methods for inferring health-related social networks among coworkers from online communication patterns.

    Science.gov (United States)

    Matthews, Luke J; DeWan, Peter; Rula, Elizabeth Y

    2013-01-01

    Studies of social networks, mapped using self-reported contacts, have demonstrated the strong influence of social connections on the propensity for individuals to adopt or maintain healthy behaviors and on their likelihood to adopt health risks such as obesity. Social network analysis may prove useful for businesses and organizations that wish to improve the health of their populations by identifying key network positions. Health traits have been shown to correlate across friendship ties, but evaluating network effects in large coworker populations presents the challenge of obtaining sufficiently comprehensive network data. The purpose of this study was to evaluate methods for using online communication data to generate comprehensive network maps that reproduce the health-associated properties of an offline social network. In this study, we examined three techniques for inferring social relationships from email traffic data in an employee population using thresholds based on: (1) the absolute number of emails exchanged, (2) logistic regression probability of an offline relationship, and (3) the highest ranked email exchange partners. As a model of the offline social network in the same population, a network map was created using social ties reported in a survey instrument. The email networks were evaluated based on the proportion of survey ties captured, comparisons of common network metrics, and autocorrelation of body mass index (BMI) across social ties. Results demonstrated that logistic regression predicted the greatest proportion of offline social ties, thresholding on number of emails exchanged produced the best match to offline network metrics, and ranked email partners demonstrated the strongest autocorrelation of BMI. Since each method had unique strengths, researchers should choose a method based on the aspects of offline behavior of interest. Ranked email partners may be particularly useful for purposes related to health traits in a social network.

  18. Methods for inferring health-related social networks among coworkers from online communication patterns.

    Directory of Open Access Journals (Sweden)

    Luke J Matthews

    Full Text Available Studies of social networks, mapped using self-reported contacts, have demonstrated the strong influence of social connections on the propensity for individuals to adopt or maintain healthy behaviors and on their likelihood to adopt health risks such as obesity. Social network analysis may prove useful for businesses and organizations that wish to improve the health of their populations by identifying key network positions. Health traits have been shown to correlate across friendship ties, but evaluating network effects in large coworker populations presents the challenge of obtaining sufficiently comprehensive network data. The purpose of this study was to evaluate methods for using online communication data to generate comprehensive network maps that reproduce the health-associated properties of an offline social network. In this study, we examined three techniques for inferring social relationships from email traffic data in an employee population using thresholds based on: (1 the absolute number of emails exchanged, (2 logistic regression probability of an offline relationship, and (3 the highest ranked email exchange partners. As a model of the offline social network in the same population, a network map was created using social ties reported in a survey instrument. The email networks were evaluated based on the proportion of survey ties captured, comparisons of common network metrics, and autocorrelation of body mass index (BMI across social ties. Results demonstrated that logistic regression predicted the greatest proportion of offline social ties, thresholding on number of emails exchanged produced the best match to offline network metrics, and ranked email partners demonstrated the strongest autocorrelation of BMI. Since each method had unique strengths, researchers should choose a method based on the aspects of offline behavior of interest. Ranked email partners may be particularly useful for purposes related to health traits in a

  19. [Health system sustainability from a network perspective: a proposal to optimize healthy habits and social support].

    Science.gov (United States)

    Marqués Sánchez, Pilar; Fernández Peña, Rosario; Cabrera León, Andrés; Muñoz Doyague, María F; Llopis Cañameras, Jaime; Arias Ramos, Natalia

    2013-01-01

    The search of new health management formulas focused to give wide services is one of the priorities of our present health policies. Those formulas examine the optimization of the links between the main actors involved in public health, ie, users, professionals, local socio-political and corporate agents. This paper is aimed to introduce the Social Network Analysis as a method for analyzing, measuring and interpreting those connections. The knowledge of people's relationships (what is called social networks) in the field of public health is becoming increasingly important at an international level. In fact, countries such as UK, Netherlands, Italy, Australia and U.S. are looking formulas to apply this knowledge to their health departments. With this work we show the utility of the ARS on topics related to sustainability of the health system, particularly those related with health habits and social support, topics included in the 2020 health strategies that underline the importance of the collaborative aspects in networks.

  20. Diagnosis and management of Transposition of great arteries within a pediatric cardiology network with the aid of telemedicine: A case report from Brazil.

    Science.gov (United States)

    Galdino, Millena M; Hazin, Sheila Mv; de Araújo, Juliana Ss; Regis, Cláudio T; Rodrigues, Klecida N; Mourato, Felipe A; Mattos, Sandra da Silva

    2016-04-01

    We present a case of a newborn from a remote, underserved area in the inland of Paraíba, a state from Northeast Brazil. She presented with clinical cyanosis at birth. With the aid of telemedicine, a neonatologist under online cardiology supervision performed a screening echocardiogram. The session established the diagnosis of simple transposition of the great vessels in the baby's first few hours of life. During the same telemedicine session, the necessary arrangements for transferal to a larger maternity center took place. The baby was maintained stable on prostaglandins and was subsequently transferred to a tertiary cardiac center in the neighboring State, Pernambuco. She underwent anatomical correction at day 10, presented no surgical or postoperative complications, and was discharged home at the age of 21 days. She is now over three years old and continues her follow-up care mostly at her hometown, with local pediatricians under online supervision by a cardiologist in a virtual outpatient clinic. The establishment of a Pediatric Cardiology Network, with the aid of telemedicine, can produce a major impact on the access to specialized health care for poor regions of developing countries. © The Author(s) 2015.

  1. An Appraisal of Social Network Theory and Analysis as Applied to Public Health: Challenges and Opportunities.

    Science.gov (United States)

    Valente, Thomas W; Pitts, Stephanie R

    2017-03-20

    The use of social network theory and analysis methods as applied to public health has expanded greatly in the past decade, yielding a significant academic literature that spans almost every conceivable health issue. This review identifies several important theoretical challenges that confront the field but also provides opportunities for new research. These challenges include (a) measuring network influences, (b) identifying appropriate influence mechanisms, (c) the impact of social media and computerized communications, (d) the role of networks in evaluating public health interventions, and (e) ethics. Next steps for the field are outlined and the need for funding is emphasized. Recently developed network analysis techniques, technological innovations in communication, and changes in theoretical perspectives to include a focus on social and environmental behavioral influences have created opportunities for new theory and ever broader application of social networks to public health topics.

  2. Work-based social networks and health status among Japanese employees.

    Science.gov (United States)

    Suzuki, E; Takao, S; Subramanian, S V; Doi, H; Kawachi, I

    2009-09-01

    Despite the worldwide trend towards more time being spent at work by employed people, few studies have examined the independent influences of work-based versus home-based social networks on employees' health. We examined the association between work-based social networks and health status by controlling for home-based social networks in a cross-sectional study. By employing a two-stage stratified random sampling procedure, 1105 employees were identified from 46 companies in Okayama, Japan, in 2007. Work-based social networks were assessed by asking the number of co-workers whom they consult with ease on personal issues. The outcome was self-rated health; the adjusted OR for poor health compared employees with no network with those who have larger networks. Although a clear (and inverse) dose-response relationship was found between the size of work-based social networks and poor health (OR 1.53, 95% CI 1.03 to 2.27, comparing those with the lowest versus highest level of social network), the association was attenuated to statistical non-significance after we controlled for the size of home-based social networks. In further analyses stratified on age groups, in older workers (> or =50 years) work-based social networks were apparently associated with better health status, whereas home-based networks were not. The reverse was true among middle-aged workers (30-49 years). No associations were found among younger workers (social support on health according to age groups. We hypothesise that these patterns reflect generational differences in workers' commitment to their workplace.

  3. Effects of social networks on physical health among people with serious mental illness.

    Science.gov (United States)

    Lee, Sungkyu; Wong, Yin-Ling Irene; Rothbard, Aileen

    2014-12-01

    This study examined the effects of social network characteristics on physical health among people with serious mental illness using social transactions that are reciprocal, and the combination of objective and subjective health measures. The sample consisted of a probability sample of 231 adults with serious mental illness who resided in permanent supportive housing in Philadelphia, Pennsylvania. Path analyses were conducted to examine the relationships between social network characteristics and two aspects of medical comorbidity, objective health and subjective health. Bivariate statistics showed that individuals with medical comorbidity were more likely to have contact with their network members and had a higher level of reciprocal positive tangible support when compared to those who did not have medical comorbidity. The results of the path analyses revealed that none of the social network characteristics were associated with better physical health. The lack of a significant relationship between social networks and better physical health is contrary to prior research findings. However, this is the first study to include both types of social transactions simultaneously as predictors of better physical health for individuals with serious mental illness. A longitudinal study would provide more insight into the temporal relationship of social networks and physical health conditions of people with serious mental illness. Furthermore, the transactional nature of social relationships, particularly for those with mental health issues, requires greater exploration.

  4. A Simple Network to Remove Interference in Surface EMG Signal from Single Gene Affected Phenylketonuria Patients for Proper Diagnosis

    Science.gov (United States)

    Mohanty, Madhusmita; Basu, Mousumi; Pattanayak, Deba Narayan; Mohapatra, Sumant Kumar

    2018-04-01

    Recently Autosomal Recessive Single Gene (ARSG) diseases are highly effective to the children within the age of 5-10 years. One of the most ARSG disease is a Phenylketonuria (PKU). This single gene disease is associated with mutations in the gene that encodes the enzyme phenylalanine hydroxylase (PAH, Gene 612349). Through this mutation process, PAH of the gene affected patient can not properly manufacture PAH as a result the patients suffer from decreased muscle tone which shows abnormality in EMG signal. Here the extraction of the quality of the PKU affected EMG (PKU-EMG) signal is a keen interest, so it is highly necessary to remove the added ECG signal as well as the biological and instrumental noises. In the Present paper we proposed a method for detection and classification of the PKU affected EMG signal. Here Discrete Wavelet Transformation is implemented for extraction of the features of the PKU affected EMG signal. Adaptive Neuro-Fuzzy Inference System (ANFIS) network is used for the classification of the signal. Modified Particle Swarm Optimization (MPSO) and Modified Genetic Algorithm (MGA) are used to train the ANFIS network. Simulation result shows that the proposed method gives better performance as compared to existing approaches. Also it gives better accuracy of 98.02% for the detection of PKU-EMG signal. The advantages of the proposed model is to use MGA and MPSO to train the parameters of ANFIS network for classification of ECG and EMG signal of PKU affected patients. The proposed method obtained the high SNR (18.13 ± 0.36 dB), SNR (0.52 ± 1.62 dB), RE (0.02 ± 0.32), MSE (0.64 ± 2.01), CC (0.99 ± 0.02), RMSE (0.75 ± 0.35) and MFRE (0.01 ± 0.02), RMSE (0.75 ± 0.35) and MFRE (0.01 ± 0.02). From authors knowledge, this is the first time a composite method is used for diagnosis of PKU affected patients. The accuracy (98.02%), sensitivity (100%) and specificity (98.59%) helps for proper clinical treatment. It can help for readers

  5. Diabetes prevalence and diagnosis in US states: analysis of health surveys

    Directory of Open Access Journals (Sweden)

    Oza Shefali

    2009-09-01

    Full Text Available Abstract Background Current US surveillance data provide estimates of diabetes using laboratory tests at the national level as well as self-reported data at the state level. Self-reported diabetes prevalence may be biased because respondents may not be aware of their risk status. Our objective was to estimate the prevalence of diagnosed and undiagnosed diabetes by state. Methods We estimated undiagnosed diabetes prevalence as a function of a set of health system and sociodemographic variables using a logistic regression in the National Health and Nutrition Examination Survey (2003-2006. We applied this relationship to identical variables from the Behavioral Risk Factor Surveillance System (2003-2007 to estimate state-level prevalence of undiagnosed diabetes by age group and sex. We assumed that those who report being diagnosed with diabetes in both surveys are truly diabetic. Results The prevalence of diabetes in the U.S. was 13.7% among men and 11.7% among women ≥ 30 years. Age-standardized diabetes prevalence was highest in Mississippi, West Virginia, Louisiana, Texas, South Carolina, Alabama, and Georgia (15.8 to 16.6% for men and 12.4 to 14.8% for women. Vermont, Minnesota, Montana, and Colorado had the lowest prevalence (11.0 to 12.2% for men and 7.3 to 8.4% for women. Men in all states had higher diabetes prevalence than women. The absolute prevalence of undiagnosed diabetes, as a percent of total population, was highest in New Mexico, Texas, Florida, and California (3.5 to 3.7 percentage points and lowest in Montana, Oklahoma, Oregon, Alaska, Vermont, Utah, Washington, and Hawaii (2.1 to 3 percentage points. Among those with no established diabetes diagnosis, being obese, being Hispanic, not having insurance and being ≥ 60 years old were significantly associated with a higher risk of having undiagnosed diabetes. Conclusion Diabetes prevalence is highest in the Southern and Appalachian states and lowest in the Midwest and the Northeast

  6. Real-Time Monitoring and Fault Diagnosis of a Low Power Hub Motor Using Feedforward Neural Network

    Directory of Open Access Journals (Sweden)

    Mehmet Şimşir

    2016-01-01

    Full Text Available Low power hub motors are widely used in electromechanical systems such as electrical bicycles and solar vehicles due to their robustness and compact structure. Such systems driven by hub motors (in wheel motors encounter previously defined and undefined faults under operation. It may inevitably lead to the interruption of the electromechanical system operation; hence, economic losses take place at certain times. Therefore, in order to maintain system operation sustainability, the motor should be precisely monitored and the faults are diagnosed considering various significant motor parameters. In this study, the artificial feedforward backpropagation neural network approach is proposed to real-time monitor and diagnose the faults of the hub motor by measuring seven main system parameters. So as to construct a necessary model, we trained the model, using a data set consisting of 4160 samples where each has 7 parameters, by the MATLAB environment until the best model is obtained. The results are encouraging and meaningful for the specific motor and the developed model may be applicable to other types of hub motors. The prosperous model of the whole system was embedded into Arduino Due microcontroller card and the mobile real-time monitoring and fault diagnosis system prototype for hub motor was designed and manufactured.

  7. Detection and diagnosis of a natural gas dehydration plant by absorption with triethylene glycol, employing a artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2004-07-01

    The natural gas dehydration is a great importance operation in the gas and petroleum industry, It avoids operational problems associated with the water content, which appear frequently in the industrial facilities that use the natural gas as raw material or as work tool. Due to the presence of undesirable pollutants which may enter the plant with the wet natural gas current (lubricating, corrosion inhibitors, salts, and others), the equipment that constitutes the dehydration plants are capable to suffering operational faults as the heat exchangers fouling, foam formation in the absorber, glycol losses for dragging; trays, packings, valves and filters fouling; glycol degradation, inadequate temperatures of regeneration and others. The above mentioned faults often cannot be detected by the operators and engineers but up to the moment when a catastrophic damage occurs or when products are obtained out of specification, which causes big economic and time losses. By means of the application of artificial neural networks, there was achieved the detection and the effective diagnosis of faults, still in incipient state, in a gas dehydration plant. (author)

  8. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) Software Development: Applications, Infrastructure, and Middleware/Networks

    Energy Technology Data Exchange (ETDEWEB)

    Williams, Dean N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2011-06-30

    The status of and future plans for the Program for Climate Model Diagnosis and Intercomparison (PCMDI) hinge on software that PCMDI is either currently distributing or plans to distribute to the climate community in the near future. These software products include standard conventions, national and international federated infrastructures, and community analysis and visualization tools. This report also mentions other secondary software not necessarily led by or developed at PCMDI to provide a complete picture of the overarching applications, infrastructures, and middleware/networks. Much of the software described anticipates the use of future technologies envisioned over the span of next year to 10 years. These technologies, together with the software, will be the catalyst required to address extreme-scale data warehousing, scalability issues, and service-level requirements for a diverse set of well-known projects essential for predicting climate change. These tools, unlike the previous static analysis tools of the past, will support the co-existence of many users in a productive, shared virtual environment. This advanced technological world driven by extreme-scale computing and the data it generates will increase scientists’ productivity, exploit national and international relationships, and push research to new levels of understanding.

  9. The Utility of Expert Diagnosis in Surgical Neuropathology: Analysis of Consultations Reviewed at 5 National Comprehensive Cancer Network Institutions.

    Science.gov (United States)

    Bruner, Janet M; Louis, David N; McLendon, Roger; Rosenblum, Marc K; Archambault, W Tad; Most, Susan; Tihan, Tarik

    2017-03-01

    The aim of this study was to characterize the type and degree of discrepancies between non-expert and expert diagnoses of CNS tumors to identify the value of consultations in surgical neuropathology. Neuropathology experts from 5 National Comprehensive Cancer Network (NCCN) member institutions participated in the review of 1281 consultations selected based on inclusion criteria. The consultation cases were re-reviewed at the NCCN headquarters to determine concordance with the original diagnoses. Among all consultations, 249 (19.4%) were submitted for expert diagnoses without final diagnoses from the submitting institution. Within the remaining 1032 patients, the serious/major discrepancy rate was 4.8%, and less serious and minor discrepancies were seen in 19.4% of the cases. The discrepancy rate was higher among patients who were referred to NCCN institutions for consultation compared to those who were referred for treatment only. The discrepancy rates, patient demographics, type of consultations and submitting institutions varied among participating NCCN institutions. Expert consultations identified a subset of cases with significant diagnostic discrepancies, and constituted the initial diagnoses in some cases. These data indicate that expert consultations in glial tumors and all types of pediatric CNS tumors can improve accurate diagnosis and enable appropriate management. © 2017 American Association of Neuropathologists, Inc. All rights reserved.

  10. Transforming public health education in India through networking and collaborations: opportunities and challenges.

    Science.gov (United States)

    Sharma, Anjali; Zodpey, Sanjay P

    2013-01-01

    A competent and motivated health workforce is indispensable to achieve the best health outcomes possible through given available resources and circumstances. However, apart from the shortages and unequal distribution, the workforce has fallen short of responding to the public health challenges of 21 st century also because of primarily the traditional training of health professionals. Although, health professionals have made enormous contributions to health and development over the past century, the 20 th century educational strategies are unfit to tackle 21 st century challenges. One of the key recommendations of the Lancet Commission on Education of Health Professionals is to improve health through reforms of professional education by establishing networks and partnerships which takes advantage of information and communication linkages. The primary goal of this manuscript is to highlight the potential of networks and partnerships in advancing the agenda of educational reforms to revitalize public health education in India. It outlines the current status and expanding scope of public health education in India, existing networks of public health professionals and public health education institutions in the country, and opportunities, advantages and challenges for such networks. Although, we have networks of individuals and institutions in the country, there potential to bring about change has still not being utilized fully and effectively. Immediate collaborative efforts could be directed towards designing and adaptation of competency driven curriculum frameworks suitable of addressing public health challenges of 21 st century, shifting the current focus of curriculum to multidisciplinary public health outlook, developing accreditation mechanisms for both the programs and institutions, engaging in creating job opportunities and designing career pathways for public health professionals in public and private sector. These efforts could certainly be facilitated

  11. [Networks of experiences on community health as an information system in health promotion: lessons learned in Aragon (Spain)].

    Science.gov (United States)

    Gállego-Diéguez, Javier; Aliaga Traín, Pilar; Benedé Azagra, Carmen Belén; Bueno Franco, Manuel; Ferrer Gracia, Elisa; Ipiéns Sarrate, José Ramón; Muñoz Nadal, Pilar; Plumed Parrilla, Manuela; Vilches Urrutia, Begoña

    2016-11-01

    Networks of community health experiences promote interaction and knowledge management in health promotion among their participants. These networks integrate both professionals and social agents who work directly on the ground in small environments, with defined objectives and inclusion criteria and voluntary participation. In this article, networks in Aragon (Spain) are reviewed in order to analyse their role as an information system. The Health Promotion Projects Network of Aragon (Red Aragonesa de Proyectos de Promoción de la Salud, RAPPS) was launched in 1996 and currently includes 73 projects. The average duration of projects is 12.7 years. RAPPS interdisciplinary teams involve 701 people, of which 89.6% are professionals and 10.6% are social agents. The Aragon Health Promoting Schools Network (Red Aragonesa de Escuelas Promotoras de Salud, RAEPS) integrates 134 schools (24.9% of Aragon). The schools teams involve 829 teachers and members of the school community, students (35.2%), families (26.2%) and primary care health professionals (9.8%). Experiences Networks boost citizen participation, have an influence in changing social determinants and contribute to the formulation of plans and regional strategies. Networks can provide indicators for a health promotion information and monitoring system on: capacity building services in the territory, identifying assets and models of good practice, cross-sectoral and equity initiatives. Experiences Networks represent an opportunity to create a health promotion information system, systematising available information and establishing quality criteria for initiatives. Copyright © 2016 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

  12. Social networks, social participation, and health among youth living in extreme poverty in rural Malawi.

    Science.gov (United States)

    Rock, Amelia; Barrington, Clare; Abdoulayi, Sara; Tsoka, Maxton; Mvula, Peter; Handa, Sudhanshu

    2016-12-01

    Extensive research documents that social network characteristics affect health, but knowledge of peer networks of youth in Malawi and sub-Saharan Africa is limited. We examine the networks and social participation of youth living in extreme poverty in rural Malawi, using in-depth interviews with 32 youth and caregivers. We describe youth's peer networks and assess how gender and the context of extreme poverty influence their networks and participation, and how their networks influence health. In-school youth had larger, more interactive, and more supportive networks than out-of-school youth, and girls described less social participation and more isolation than boys. Youth exchanged social support and influence within their networks that helped cope with poverty-induced stress and sadness, and encouraged protective sexual health practices. However, poverty hampered their involvement in school, religious schools, and community organizations, directly by denying them required material means, and indirectly by reducing time and emotional resources and creating shame and stigma. Poverty alleviation policy holds promise for improving youth's social wellbeing and mental and physical health by increasing their opportunities to form networks, receive social support, and experience positive influence. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. Mental Health Service and Drug Treatment Utilization: Adolescents with Substance Use/Mental Disorders and Dual Diagnosis

    Science.gov (United States)

    Cheng, Tyrone C.; Lo, Celia C.

    2010-01-01

    This research is a secondary data analysis of the impact of adolescents' mental/substance-use disorders and dual diagnosis on their utilization of drug treatment and mental health services. By analyzing the same teenagers who participated in the NIMH Methods for the Epidemiology of Child and Adolescent Mental Disorders (MECA) study, logistic…

  14. Social networks and mental health in post-conflict Mitrovica, Kosova.

    Science.gov (United States)

    Nakayama, Risa; Koyanagi, Ai; Stickley, Andrew; Kondo, Tetsuo; Gilmour, Stuart; Arenliu, Aliriza; Shibuya, Kenji

    2014-11-17

    To investigate the relation between social networks and mental health in the post-conflict municipality of Mitrovica, Kosovo. Using a three-stage stratified sampling method, 1239 respondents aged 16 years or above were recruited in the Greater Mitrovica region. Social network depth was measured by the frequency of contacts with friends, relatives and strangers. Depression and anxiety were measured using the Hospital Anxiety and Depression Scale (HADS). Multivariate logistic regression was used to examine the association between social network depth and mental health. The analytical sample consisted of 993 respondents. The prevalence of depression (54.3%) and anxiety (64.4%) were extremely high. In multiple regression analysis, a lower depth of social network (contact with friends) was associated with higher levels of both depression and anxiety. This study has shown that only one variety of social network--contact with friends--was important in terms of mental health outcomes in a population living in an area heavily affected by conflict. This suggests that the relation between social networks and mental health may be complex in that the effects of different forms of social network on mental health are not uniform and may depend on the way social networks are operationalised and the particular context in which the relationship is examined.

  15. Implementing multiple intervention strategies in Dutch public health-related policy networks.

    Science.gov (United States)

    Harting, Janneke; Peters, Dorothee; Grêaux, Kimberly; van Assema, Patricia; Verweij, Stefan; Stronks, Karien; Klijn, Erik-Hans

    2017-10-13

    Improving public health requires multiple intervention strategies. Implementing such an intervention mix is supposed to require a multisectoral policy network. As evidence to support this assumption is scarce, we examined under which conditions public health-related policy networks were able to implement an intervention mix. Data were collected (2009-14) from 29 Dutch public health policy networks. Surveys were used to identify the number of policy sectors, participation of actors, level of trust, networking by the project leader, and intervention strategies implemented. Conditions sufficient for an intervention mix (≥3 of 4 non-educational strategies present) were determined in a fuzzy-set qualitative comparative analysis. A multisectoral policy network (≥7 of 14 sectors present) was neither a necessary nor a sufficient condition. In multisectoral networks, additionally required was either the active participation of network actors (≥50% actively involved) or active networking by the project leader (≥monthly contacts with network actors). In policy networks that included few sectors, a high level of trust (positive perceptions of each other's intentions) was needed-in the absence though of any of the other conditions. If the network actors were also actively involved, an extra requirement was active networking by the project leader. We conclude that the multisectoral composition of policy networks can contribute to the implementation of a variety of intervention strategies, but not without additional efforts. However, policy networks that include only few sectors are also able to implement an intervention mix. Here, trust seems to be the most important condition. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  16. The challenge of social networking in the field of environment and health.

    Science.gov (United States)

    van den Hazel, Peter; Keune, Hans; Randall, Scott; Yang, Aileen; Ludlow, David; Bartonova, Alena

    2012-06-28

    The fields of environment and health are both interdisciplinary and trans-disciplinary, and until recently had little engagement in social networking designed to cross disciplinary boundaries. The EU FP6 project HENVINET aimed to establish integrated social network and networking facilities for multiple stakeholders in environment and health. The underlying assumption is that increased social networking across disciplines and sectors will enhance the quality of both problem knowledge and problem solving, by facilitating interactions. Inter- and trans-disciplinary networks are considered useful for this purpose. This does not mean that such networks are easily organized, as openness to such cooperation and exchange is often difficult to ascertain. Different methods may enhance network building. Using a mixed method approach, a diversity of actions were used in order to investigate the main research question: which kind of social networking activities and structures can best support the objective of enhanced inter- and trans-disciplinary cooperation and exchange in the fields of environment and health. HENVINET applied interviews, a role playing session, a personal response system, a stakeholder workshop and a social networking portal as part of the process of building an interdisciplinary and trans-disciplinary network. The interviews provided support for the specification of requirements for an interdisciplinary and trans-disciplinary network. The role playing session, the personal response system and the stakeholder workshop were assessed as useful tools in forming such network, by increasing the awareness by different disciplines of other's positions. The social networking portal was particularly useful in delivering knowledge, but the role of the scientist in social networking is not yet clear. The main challenge in the field of environment and health is not so much a lack of scientific problem knowledge, but rather the ability to effectively communicate, share

  17. Loneliness, Social Networks, and Health: A Cross-Sectional Study in Three Countries.

    Science.gov (United States)

    Rico-Uribe, Laura Alejandra; Caballero, Francisco Félix; Olaya, Beatriz; Tobiasz-Adamczyk, Beata; Koskinen, Seppo; Leonardi, Matilde; Haro, Josep Maria; Chatterji, Somnath; Ayuso-Mateos, José Luis; Miret, Marta

    2016-01-01

    It is widely recognized that social networks and loneliness have effects on health. The present study assesses the differential association that the components of the social network and the subjective perception of loneliness have with health, and analyzes whether this association is different across different countries. A total of 10 800 adults were interviewed in Finland, Poland and Spain. Loneliness was assessed by means of the 3-item UCLA Loneliness Scale. Individuals' social networks were measured by asking about the number of members in the network, how often they had contacts with these members, and whether they had a close relationship. The differential association of loneliness and the components of the social network with health was assessed by means of hierarchical linear regression models, controlling for relevant covariates. In all three countries, loneliness was the variable most strongly correlated with health after controlling for depression, age, and other covariates. Loneliness contributed more strongly to health than any component of the social network. The relationship between loneliness and health was stronger in Finland (|β| = 0.25) than in Poland (|β| = 0.16) and Spain (|β| = 0.18). Frequency of contact was the only component of the social network that was moderately correlated with health. Loneliness has a stronger association with health than the components of the social network. This association is similar in three different European countries with different socio-economic and health characteristics and welfare systems. The importance of evaluating and screening feelings of loneliness in individuals with health problems should be taken into account. Further studies are needed in order to be able to confirm the associations found in the present study and infer causality.

  18. Loneliness, Social Networks, and Health: A Cross-Sectional Study in Three Countries.

    Directory of Open Access Journals (Sweden)

    Laura Alejandra Rico-Uribe

    Full Text Available It is widely recognized that social networks and loneliness have effects on health. The present study assesses the differential association that the components of the social network and the subjective perception of loneliness have with health, and analyzes whether this association is different across different countries.A total of 10 800 adults were interviewed in Finland, Poland and Spain. Loneliness was assessed by means of the 3-item UCLA Loneliness Scale. Individuals' social networks were measured by asking about the number of members in the network, how often they had contacts with these members, and whether they had a close relationship. The differential association of loneliness and the components of the social network with health was assessed by means of hierarchical linear regression models, controlling for relevant covariates.In all three countries, loneliness was the variable most strongly correlated with health after controlling for depression, age, and other covariates. Loneliness contributed more strongly to health than any component of the social network. The relationship between loneliness and health was stronger in Finland (|β| = 0.25 than in Poland (|β| = 0.16 and Spain (|β| = 0.18. Frequency of contact was the only component of the social network that was moderately correlated with health.Loneliness has a stronger association with health than the components of the social network. This association is similar in three different European countries with different socio-economic and health characteristics and welfare systems. The importance of evaluating and screening feelings of loneliness in individuals with health problems should be taken into account. Further studies are needed in order to be able to confirm the associations found in the present study and infer causality.

  19. Using Patient Pathway Analysis to Design Patient-centered Referral Networks for Diagnosis and Treatment of Tuberculosis: The Case of the Philippines.

    Science.gov (United States)

    Garfin, Celine; Mantala, Mariquita; Yadav, Rajendra; Hanson, Christy L; Osberg, Mike; Hymoff, Aaron; Makayova, Julia

    2017-11-06

    Tuberculosis (TB) is the 8th leading cause of death in the Philippines. A recent prevalence survey found that there were nearly 70% more cases of tuberculosis than previously estimated. Given these new data, the National TB Program (NTP), operating through a decentralized health system, identified about 58% of the estimated new drug-sensitive (DS) TB patients in 2016. However, the NTP only identified and commenced treatment for around 17% of estimated new drug-resistant patients. In order to reach the remaining 42% of drug-sensitive patients and 83% of drug-resistant patients, it is necessary to develop a better understanding of where patients seek care. National and regional patient pathway analyses (PPAs) were undertaken using existing national survey and NTP data. The PPA assessed the alignment between patient care seeking and the availability of TB diagnostic and treatment services. Systemic referral networks from the community-level Barangay Health Stations (BHSs) to diagnostic facilities have enabled more efficient detection of drug-sensitive tuberculosis in the public sector. Approximately 36% of patients initiated care in the private sector, where there is limited coverage of appropriate diagnostic technologies. Important differences in the alignment between care seeking patterns and diagnostic and treatment availability were found between regions. The PPA identified opportunities for strengthening access to care for all forms of tuberculosis and for accelerating the time to diagnosis by aligning services to where patients initiate care. Geographic variations in care seeking may guide prioritization of some regions for intensified engagement with the private sector. © The Author 2017. Published by Oxford University Press for the Infectious Diseases Society of America.

  20. Social networks and health among older adults in Lebanon: the mediating role of support and trust.

    Science.gov (United States)

    Webster, Noah J; Antonucci, Toni C; Ajrouch, Kristine J; Abdulrahim, Sawsan

    2015-01-01

    Despite a growing body of literature documenting the influence of social networks on health, less is known in other parts of the world. The current study investigates this link by clustering characteristics of network members nominated by older adults in Lebanon. We then identify the degree to which various types of people exist within the networks. This study further examines how network composition as measured by the proportion of each type (i.e., type proportions) is related to health; and the mediating role of positive support and trust in this process. Data are from the Family Ties and Aging Study (2009). Respondents aged ≥60 were selected (N = 195) for analysis. Three types of people within the networks were identified: Geographically Distant Male Youth, Geographically Close/Emotionally Distant Family, and Close Family. Having more Geographically Distant Male Youth in one's network was associated with health limitations, whereas more Close Family was associated with no health limitations. Positive support mediated the link between type proportions and health limitations, whereas trust mediated the link between type proportions and depressive symptoms. Results document links between the social networks and health of older adults in Lebanon within the context of ongoing demographic transitions. © The Author 2014. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  1. Social Networks and Health Among Older Adults in Lebanon: The Mediating Role of Support and Trust

    Science.gov (United States)

    Antonucci, Toni C.; Ajrouch, Kristine J.; Abdulrahim, Sawsan

    2015-01-01

    Objectives. Despite a growing body of literature documenting the influence of social networks on health, less is known in other parts of the world. The current study investigates this link by clustering characteristics of network members nominated by older adults in Lebanon. We then identify the degree to which various types of people exist within the networks. This study further examines how network composition as measured by the proportion of each type (i.e., type proportions) is related to health; and the mediating role of positive support and trust in this process. Method. Data are from the Family Ties and Aging Study (2009). Respondents aged ≥60 were selected (N = 195) for analysis. Results. Three types of people within the networks were identified: Geographically Distant Male Youth, Geographically Close/Emotionally Distant Family, and Close Family. Having more Geographically Distant Male Youth in one’s network was associated with health limitations, whereas more Close Family was associated with no health limitations. Positive support mediated the link between type proportions and health limitations, whereas trust mediated the link between type proportions and depressive symptoms. Discussion. Results document links between the social networks and health of older adults in Lebanon within the context of ongoing demographic transitions. PMID:25324295

  2. Depression and Chronic Health Conditions Among Latinos: The Role of Social Networks.

    Science.gov (United States)

    Soto, Sandra; Arredondo, Elva M; Villodas, Miguel T; Elder, John P; Quintanar, Elena; Madanat, Hala

    2016-12-01

    The purpose of this study was to examine the "buffering hypothesis" of social network characteristics in the association between chronic conditions and depression among Latinos. Cross-sectional self-report data from the San Diego Prevention Research Center's community survey of Latinos were used (n = 393). Separate multiple logistic regression models tested the role of chronic conditions and social network characteristics in the likelihood of moderate-to-severe depressive symptoms. Having a greater proportion of the network comprised of friends increased the likelihood of depression among those with high cholesterol. Having a greater proportion of women in the social network was directly related to the increased likelihood of depression, regardless of the presence of chronic health conditions. Findings suggest that network characteristics may play a role in the link between chronic conditions and depression among Latinos. Future research should explore strategies targeting the social networks of Latinos to improve health outcomes.

  3. Breast cancer mammographic diagnosis performance in a public health institution: a retrospective cohort study.

    Science.gov (United States)

    Mello, Juliana M R B; Bittelbrunn, Fernando P; Rockenbach, Marcio A B C; May, Guilherme G; Vedolin, Leonardo M; Kruger, Marilia S; Soldatelli, Matheus D; Zwetsch, Guilherme; de Miranda, Gabriel T F; Teixeira, Saone I P; Arruda, Bruna S

    2017-12-01

    To evaluate the quality assurance of mammography results at a reference institution for the diagnosis and treatment of breast cancer in southern Brazil, based on the BIRADS (Breast Imaging Reporting and Data System) 5th edition recommendations for auditing purposes. Retrospective cohort and cross-sectional study with 4502 patients (9668 mammographies)) who underwent at least one or both breast mammographies throughout 2013 at a regional public hospital, linked to a federal public university. The results were followed until 31 December 2014, including true positives (TPs), true negatives (TNs), false positives (FPs), false negatives (FNs), positive predictive values (PPVs), negative predictive value (NPV), sensitivity and specificity, with a confidence interval of 95%. The study showed high quality assurance, particularly regarding sensitivity (90.22%) and specificity (92.31%). The overall positive predictive value (PPV) was 65.35%, and the negative predictive value (NPV) was 98.32%. The abnormal interpretation rate (recall rate) was 12.26%. The results are appropriate when compared to the values proposed by the BIRADS 5th edition. Additionally, the study provided self-reflection considering our radiological practice, which is essential for improvements and collaboration regarding breast cancer detection. It may stimulate better radiological practice performance and continuing education, despite possible infrastructure and facility limitations. • Accurate quality performance rates are possible despite financial and governmental limitations. • Low-income institutions should develop standardised teamwork to improve radiological practice. • Regular mammography audits may help to increase the quality of public health systems.

  4. [Eugenics' extension in the Spanish health care system through the prenatal diagnosis].

    Science.gov (United States)

    Rodríguez Martín, Esteban

    2012-01-01

    The wide implantation of strategies of sifted or prenatal selection close to laws that protect the destruction of the human life before the childbirth in the whole world, they are giving place to an increasing number of eugenic abortions. In Spain, the law 2/2010 of the sexual and reproductive health and voluntary interruption of pregnancy there has supposed the liberalization of the eugenic abortion without term limit. In we make concrete, the sanitary national and international policies of prenatal selection of Down's Syndrome, which they chase to facilitate the total or partial destruction before the childbirth of this human group, submitting it to a few particular conditions of existence during his prenatal life in those who will be an object of a series of technologies of selection, they might be qualified of genocidal policies if we consider the definition of genocide given by United Nations. In consequence, the sanitary agent who takes part without objection in the above mentioned programs promoted by the principal agents, meets turned into a necessary cooperator of the abortion who justifies itself in the supposition of "foetal risk". We can conclude that we are present at an eugenic drift of the prenatal diagnosis that is opposite to the ethical beginning of the medical profession.

  5. How universal is coverage and access to diagnosis and treatment for Chagas disease in Colombia? A health systems analysis.

    Science.gov (United States)

    Cucunubá, Zulma M; Manne-Goehler, Jennifer M; Díaz, Diana; Nouvellet, Pierre; Bernal, Oscar; Marchiol, Andrea; Basáñez, María-Gloria; Conteh, Lesong

    2017-02-01

    Limited access to Chagas disease diagnosis and treatment is a major obstacle to reaching the 2020 World Health Organization milestones of delivering care to all infected and ill patients. Colombia has been identified as a health system in transition, reporting one of the highest levels of health insurance coverage in Latin America. We explore if and how this high level of coverage extends to those with Chagas disease, a traditionally marginalised population. Using a mixed methods approach, we calculate coverage for screening, diagnosis and treatment of Chagas. We then identify supply-side constraints both quantitatively and qualitatively. A review of official registries of tests and treatments for Chagas disease delivered between 2008 and 2014 is compared to estimates of infected people. Using the Flagship Framework, we explore barriers limiting access to care. Screening coverage is estimated at 1.2% of the population at risk. Aetiological treatment with either benznidazol or nifurtimox covered 0.3-0.4% of the infected population. Barriers to accessing screening, diagnosis and treatment are identified for each of the Flagship Framework's five dimensions of interest: financing, payment, regulation, organization and persuasion. The main challenges identified were: a lack of clarity in terms of financial responsibilities in a segmented health system, claims of limited resources for undertaking activities particularly in primary care, non-inclusion of confirmatory test(s) in the basic package of diagnosis and care, poor logistics in the distribution and supply chain of medicines, and lack of awareness of medical personnel. Very low screening coverage emerges as a key obstacle hindering access to care for Chagas disease. Findings suggest serious shortcomings in this health system for Chagas disease, despite the success of universal health insurance scale-up in Colombia. Whether these shortcomings exist in relation to other neglected tropical diseases needs investigating

  6. Health care for women in situations of violence: discoordination of network professionals

    Directory of Open Access Journals (Sweden)

    Jaqueline Arboit

    Full Text Available Abstract OBJECTIVE To learn the conceptions and actions of health professionals on the care network for women in situations of violence. METHOD A qualitative, descriptive, exploratory study was conducted between April and July 2015 with the participation of 21 health professionals from four primary health care teams in a city of the central region of the state of Rio Grande do Sul. Data were collected by means of individual semi-structured interviews. Content analysis was used for data systematization. RESULTS Health professionals recognized the importance of the health care network for coping with the problem of violence against women. However, their conceptions and actions were limited by the discoordination or absence of integration among professionals and services of the care network. CONCLUSION The conceptions and actions of health professionals contribute to the discoordination among the services. It is necessary to reflect on the daily practices of care for women in situations of violence.

  7. Utilizing social networking sites to promote adolescents' health: a pragmatic review of the literature.

    Science.gov (United States)

    Francomano, Jesse A; Harpin, Scott B

    2015-01-01

    Social networking site use has exploded among youth in the last few years and is being adapted as an important tool for healthcare interventions and serving as a platform for adolescents to gain access to health information. The aim of this study was to examine the strengths, weaknesses, and best practices of utilizing Facebook in adolescent health promotion and research via pragmatic literature review. We also examine how sites can facilitate ethically sound healthcare for adolescents, particularly at-risk youth. We conducted a literature review of health and social sciences literature from the past 5 years related to adolescent health and social network site use. Publications were grouped by shared content then categorized by themes. Five themes emerged: access to healthcare information, peer support and networking, risk and benefits of social network site use in care delivery, overcoming technological barriers, and social network site interventions. More research is needed to better understand how such Web sites can be better utilized to provide access to adolescents seeking healthcare. Given the broad reach of social network sites, all health information must be closely monitored for accurate, safe distribution. Finally, consent and privacy issues are omnipresent in social network sites, which calls for standards of ethical use.

  8. Health disparities in Europe's ageing population: the role of social network.

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    Olofsson, Jenny; Padyab, Mojgan; Malmberg, Gunnar

    2018-01-01

    Previous research suggests that the social network may play very different roles in relation to health in countries with differing welfare regimes. The study aimed to assess the interplay between social network, socioeconomic position, and self-rated health (SRH) in European countries. The study used cross-sectional data on individuals aged 50+ from the fourth wave of the Survey of Health, Ageing and Retirement in Europe (SHARE) and includes data from 16 countries. The outcome is poor SRH. All analyses are adjusted for age and stratified by gender. Low satisfaction with the social network was associated with poor SRH among women in all country groups, but predicted poor SRH among males in West/Central and Eastern Europe only. The results from the multivariable analysis showed an increased likelihood of poor SRH among those with relatively lower education, as well as among those with low satisfaction with the social network (women from all country groups and men from Western/Central and Eastern Europe). However, the results from interaction analysis show that poor SRH for those with lower relative position in educational level was greater among those with higher satisfaction with the social network among male and female participants from Northern Europe. The health of individuals who are highly satisfied with their social network is more associated with socioeconomic status in Northern Europe. This study highlights the significance of social network and socioeconomic gradients in health among the elderly in Europe.

  9. Gel network shampoo formulation and hair health benefits.

    Science.gov (United States)

    Marsh, J M; Brown, M A; Felts, T J; Hutton, H D; Vatter, M L; Whitaker, S; Wireko, F C; Styczynski, P B; Li, C; Henry, I D

    2017-10-01

    The objective of this work was to create a shampoo formula that contains a stable ordered gel network structure that delivers fatty alcohols inside hair. X-ray diffraction (SAXS and WAXS), SEM and DSC have been used to confirm formation of the ordered Lβ gel network with fatty alcohol (cetyl and stearyl alcohols) and an anionic surfactant (SLE1S). Micro-autoradiography and extraction methods using GC-MS were used to confirm penetration of fatty alcohols into hair, and cyclic fatigue testing was used to measure hair strength. In this work, evidence of a stable Lβ ordered gel network structure created from cetyl and stearyl alcohols and anionic surfactant (SLE1S) is presented, and this is confirmed via scanning electron microscopy images showing lamella layers and differential scanning calorimetry (DSC) showing new melting peaks vs the starting fatty alcohols. Hair washed for 16 repeat cycles with this shampoo showed penetration of fatty alcohols from the gel network into hair as confirmed by a differential extraction method with GC-MS and by radiolabelling of stearyl alcohol and showing its presence inside hair cross-sections. The gel network role in delivering fatty alcohol inside hair is demonstrated by comparing with a shampoo with added fatty alcohol not in an ordered gel network structure. The hair containing fatty alcohol was measured via the Dia-stron cyclic fatigue instrument and showed a significantly higher number of cycles to break vs control. The formation of a stable gel network was confirmed in the formulated shampoo, and it was demonstrated that this gel network is important to deliver cetyl and stearyl alcohols into hair. The presence of fatty alcohol inside hair was shown to deliver a hair strength benefit via cyclic fatigue testing. © 2017 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

  10. [Relation of psychological distress after diagnosis of gastric cancer at a cancer screening center with psychological support from public health nurses and family members].

    Science.gov (United States)

    Fukui, Sakiko; Ozawa, Harumi

    2003-07-01

    The objectives of this study were to examine the degree of psychological distress during the first 6 months after diagnosis of gastric cancer and investigate the relation to psychological support from public health nurses and family members. One hundred and five patients with stomach, colorectal, or esophagus cancer were mailed a questionnaire. They were asked questions concerning the level of shock on the day of diagnosis, at 1-week after the diagnosis, and at 6 months post diagnosis. In addition, their physical and psychological status was assessed at the 6-month time point. They were also asked about perceived psychological support from public health nurses and family members. The relation between psychological distress and such psychological support was then assessed using multiple regression analyses. The levels of shock on the day of diagnosis and after 1-week were both significantly related to the psychological support from public health nurses. Physical and psychological status at 6 months post diagnosis was significantly related to the level of psychological support from the patient's family members. The study revealed that psychological support from public health nurses improves the level of patient psychological distress during the first 1 week after the cancer diagnosis. Psychological support from family members facilitates the physical and psychological adjustment at 6 months post diagnosis. The results indicate that psychological support is important just after cancer diagnosis and for longer term adjustment, pointing to a major role of health care professionals alleviating problems associated with cancer diagnosis.

  11. Toward Predicting Social Support Needs in Online Health Social Networks.

    Science.gov (United States)

    Choi, Min-Je; Kim, Sung-Hee; Lee, Sukwon; Kwon, Bum Chul; Yi, Ji Soo; Choo, Jaegul; Huh, Jina

    2017-08-02

    While online health social networks (OHSNs) serve as an effective platform for patients to fulfill their various social support needs, predicting the needs of users and providing tailored information remains a challenge. The objective of this study was to discriminate important features for identifying users' social support needs based on knowledge gathered from survey data. This study also provides guidelines for a technical framework, which can be used to predict users' social support needs based on raw data collected from OHSNs. We initially conducted a Web-based survey with 184 OHSN users. From this survey data, we extracted 34 features based on 5 categories: (1) demographics, (2) reading behavior, (3) posting behavior, (4) perceived roles in OHSNs, and (5) values sought in OHSNs. Features from the first 4 categories were used as variables for binary classification. For the prediction outcomes, we used features from the last category: the needs for emotional support, experience-based information, unconventional information, and medical facts. We compared 5 binary classifier algorithms: gradient boosting tree, random forest, decision tree, support vector machines, and logistic regression. We then calculated the scores of the area under the receiver operating characteristic (ROC) curve (AUC) to understand the comparative effectiveness of the used features. The best performance was AUC scores of 0.89 for predicting users seeking emotional support, 0.86 for experience-based information, 0.80 for unconventional information, and 0.83 for medical facts. With the gradient boosting tree as our best performing model, we analyzed the strength of individual features in predicting one's social support need. Among other discoveries, we found that users seeking emotional support tend to post more in OHSNs compared with others. We developed an initial framework for automatically predicting social support needs in OHSNs using survey data. Future work should involve nonsurvey

  12. Wearable sensors