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Sample records for model predicted values

  1. Predictability of extreme values in geophysical models

    Directory of Open Access Journals (Sweden)

    A. E. Sterk

    2012-09-01

    Full Text Available Extreme value theory in deterministic systems is concerned with unlikely large (or small values of an observable evaluated along evolutions of the system. In this paper we study the finite-time predictability of extreme values, such as convection, energy, and wind speeds, in three geophysical models. We study whether finite-time Lyapunov exponents are larger or smaller for initial conditions leading to extremes. General statements on whether extreme values are better or less predictable are not possible: the predictability of extreme values depends on the observable, the attractor of the system, and the prediction lead time.

  2. Evaluating the Predictive Value of Growth Prediction Models

    Science.gov (United States)

    Murphy, Daniel L.; Gaertner, Matthew N.

    2014-01-01

    This study evaluates four growth prediction models--projection, student growth percentile, trajectory, and transition table--commonly used to forecast (and give schools credit for) middle school students' future proficiency. Analyses focused on vertically scaled summative mathematics assessments, and two performance standards conditions (high…

  3. Genomic value prediction for quantitative traits under the epistatic model

    Directory of Open Access Journals (Sweden)

    Xu Shizhong

    2011-01-01

    Full Text Available Abstract Background Most quantitative traits are controlled by multiple quantitative trait loci (QTL. The contribution of each locus may be negligible but the collective contribution of all loci is usually significant. Genome selection that uses markers of the entire genome to predict the genomic values of individual plants or animals can be more efficient than selection on phenotypic values and pedigree information alone for genetic improvement. When a quantitative trait is contributed by epistatic effects, using all markers (main effects and marker pairs (epistatic effects to predict the genomic values of plants can achieve the maximum efficiency for genetic improvement. Results In this study, we created 126 recombinant inbred lines of soybean and genotyped 80 makers across the genome. We applied the genome selection technique to predict the genomic value of somatic embryo number (a quantitative trait for each line. Cross validation analysis showed that the squared correlation coefficient between the observed and predicted embryo numbers was 0.33 when only main (additive effects were used for prediction. When the interaction (epistatic effects were also included in the model, the squared correlation coefficient reached 0.78. Conclusions This study provided an excellent example for the application of genome selection to plant breeding.

  4. Dealing with missing predictor values when applying clinical prediction models.

    NARCIS (Netherlands)

    Janssen, K.J.; Vergouwe, Y.; Donders, A.R.T.; Harrell Jr, F.E.; Chen, Q.; Grobbee, D.E.; Moons, K.G.

    2009-01-01

    BACKGROUND: Prediction models combine patient characteristics and test results to predict the presence of a disease or the occurrence of an event in the future. In the event that test results (predictor) are unavailable, a strategy is needed to help users applying a prediction model to deal with

  5. Real estate value prediction using multivariate regression models

    Science.gov (United States)

    Manjula, R.; Jain, Shubham; Srivastava, Sharad; Rajiv Kher, Pranav

    2017-11-01

    The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.

  6. Models for measuring and predicting shareholder value: A study of ...

    Indian Academy of Sciences (India)

    With the increasing focus on creation of shareholder value and core competencies, many companies are outsourcing their information technology (IT) related activities to third party software companies. Indian software companies have become leaders in providing these services. Companies from several other countries are ...

  7. Predictive Models of Alcohol Use Based on Attitudes and Individual Values

    Science.gov (United States)

    Del Castillo Rodríguez, José A. García; López-Sánchez, Carmen; Soler, M. Carmen Quiles; Del Castillo-López, Álvaro García; Pertusa, Mónica Gázquez; Campos, Juan Carlos Marzo; Inglés, Cándido J.

    2013-01-01

    Two predictive models are developed in this article: the first is designed to predict people' attitudes to alcoholic drinks, while the second sets out to predict the use of alcohol in relation to selected individual values. University students (N = 1,500) were recruited through stratified sampling based on sex and academic discipline. The…

  8. Computational Techniques for Model Predictive Control of Large-Scale Systems with Continuous-Valued and Discrete-Valued Inputs

    Directory of Open Access Journals (Sweden)

    Koichi Kobayashi

    2013-01-01

    Full Text Available We propose computational techniques for model predictive control of large-scale systems with both continuous-valued control inputs and discrete-valued control inputs, which are a class of hybrid systems. In the proposed method, we introduce the notion of virtual control inputs, which are obtained by relaxing discrete-valued control inputs to continuous variables. In online computation, first, we find continuous-valued control inputs and virtual control inputs minimizing a cost function. Next, using the obtained virtual control inputs, only discrete-valued control inputs at the current time are computed in each subsystem. In addition, we also discuss the effect of quantization errors. Finally, the effectiveness of the proposed method is shown by a numerical example. The proposed method enables us to reduce and decentralize the computation load.

  9. The Prediction Value

    NARCIS (Netherlands)

    Koster, M.; Kurz, S.; Lindner, I.; Napel, S.

    2013-01-01

    We introduce the prediction value (PV) as a measure of players’ informational importance in probabilistic TU games. The latter combine a standard TU game and a probability distribution over the set of coalitions. Player i’s prediction value equals the difference between the conditional expectations

  10. Developing risk prediction models for kidney injury and assessing incremental value for novel biomarkers.

    Science.gov (United States)

    Kerr, Kathleen F; Meisner, Allison; Thiessen-Philbrook, Heather; Coca, Steven G; Parikh, Chirag R

    2014-08-07

    The field of nephrology is actively involved in developing biomarkers and improving models for predicting patients' risks of AKI and CKD and their outcomes. However, some important aspects of evaluating biomarkers and risk models are not widely appreciated, and statistical methods are still evolving. This review describes some of the most important statistical concepts for this area of research and identifies common pitfalls. Particular attention is paid to metrics proposed within the last 5 years for quantifying the incremental predictive value of a new biomarker. Copyright © 2014 by the American Society of Nephrology.

  11. Estimating cross-validatory predictive p-values with integrated importance sampling for disease mapping models.

    Science.gov (United States)

    Li, Longhai; Feng, Cindy X; Qiu, Shi

    2017-06-30

    An important statistical task in disease mapping problems is to identify divergent regions with unusually high or low risk of disease. Leave-one-out cross-validatory (LOOCV) model assessment is the gold standard for estimating predictive p-values that can flag such divergent regions. However, actual LOOCV is time-consuming because one needs to rerun a Markov chain Monte Carlo analysis for each posterior distribution in which an observation is held out as a test case. This paper introduces a new method, called integrated importance sampling (iIS), for estimating LOOCV predictive p-values with only Markov chain samples drawn from the posterior based on a full data set. The key step in iIS is that we integrate away the latent variables associated the test observation with respect to their conditional distribution without reference to the actual observation. By following the general theory for importance sampling, the formula used by iIS can be proved to be equivalent to the LOOCV predictive p-value. We compare iIS and other three existing methods in the literature with two disease mapping datasets. Our empirical results show that the predictive p-values estimated with iIS are almost identical to the predictive p-values estimated with actual LOOCV and outperform those given by the existing three methods, namely, the posterior predictive checking, the ordinary importance sampling, and the ghosting method by Marshall and Spiegelhalter (2003). Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  12. Value-based benefit design: using a predictive modeling approach to improve compliance.

    Science.gov (United States)

    Mahoney, John J

    2008-07-01

    Increased medication compliance rates have been demonstrated to result in improved clinical outcomes and reduced overall medical expenditures. As such, managed care stakeholders should take the total value approach to benefit design and consider total medical costs beyond the cost of pharmacotherapy alone. To describe the value-based benefit design employed by Pitney Bowes (specifically, the predictive modeling approach), to improve medication compliance, and to report the results of this intervention. Despite significant skepticism surrounding value-based benefit design, there is growing evidence that these plans can be used in conjunction with careful pharmacy management. In fact, value-based design provides a different lever on pharmacy management and allows for the appropriate drug to be channeled to the appropriate person. Studies demonstrating the adverse impact of high coinsurance levels further augment the argument for value-based benefit design. Value-based benefit design was employed at Pitney Bowes, a $6.1-billion global provider of integrated mailstream solutions, with noticeable success. Patients were either placed in a disease management program or in a secondary program promoting preventive care. The company selectively cut copays to achieve that end, and this total value approach translated into significant savings. To develop a successful value-based benefit design, stakeholders cannot simply cut costs or cut copays. Action must be taken as part of a concerted program, coupled with disease management or similar interventions. "Value based" means that positive outcomes are the ultimate goal, and barriers to those positive outcomes must be addressed.

  13. A CBR-Based and MAHP-Based Customer Value Prediction Model for New Product Development

    Science.gov (United States)

    Zhao, Yu-Jie; Luo, Xin-xing; Deng, Li

    2014-01-01

    In the fierce market environment, the enterprise which wants to meet customer needs and boost its market profit and share must focus on the new product development. To overcome the limitations of previous research, Chan et al. proposed a dynamic decision support system to predict the customer lifetime value (CLV) for new product development. However, to better meet the customer needs, there are still some deficiencies in their model, so this study proposes a CBR-based and MAHP-based customer value prediction model for a new product (C&M-CVPM). CBR (case based reasoning) can reduce experts' workload and evaluation time, while MAHP (multiplicative analytic hierarchy process) can use actual but average influencing factor's effectiveness in stimulation, and at same time C&M-CVPM uses dynamic customers' transition probability which is more close to reality. This study not only introduces the realization of CBR and MAHP, but also elaborates C&M-CVPM's three main modules. The application of the proposed model is illustrated and confirmed to be sensible and convincing through a stimulation experiment. PMID:25162050

  14. A CBR-based and MAHP-based customer value prediction model for new product development.

    Science.gov (United States)

    Zhao, Yu-Jie; Luo, Xin-xing; Deng, Li

    2014-01-01

    In the fierce market environment, the enterprise which wants to meet customer needs and boost its market profit and share must focus on the new product development. To overcome the limitations of previous research, Chan et al. proposed a dynamic decision support system to predict the customer lifetime value (CLV) for new product development. However, to better meet the customer needs, there are still some deficiencies in their model, so this study proposes a CBR-based and MAHP-based customer value prediction model for a new product (C&M-CVPM). CBR (case based reasoning) can reduce experts' workload and evaluation time, while MAHP (multiplicative analytic hierarchy process) can use actual but average influencing factor's effectiveness in stimulation, and at same time C&M-CVPM uses dynamic customers' transition probability which is more close to reality. This study not only introduces the realization of CBR and MAHP, but also elaborates C&M-CVPM's three main modules. The application of the proposed model is illustrated and confirmed to be sensible and convincing through a stimulation experiment.

  15. Can Shell-Model Predict Cluster-Decay Q-Values?

    International Nuclear Information System (INIS)

    Delion, D.; Sandulescu, A.

    2000-01-01

    The general belief is that all the information concerning α and heavy-cluster decay is practically included in the barrier penetration because it depends exponentially on the Q-value and on the other hand the formation probability has a smooth behaviour versus the mass number. In all calculations the Q-values are taken as experimental numbers. To our knowledge up to now no attempts were done to connect the Q-value with the microscopic cluster formation probability. In this lecture we show that this connection can be performed using the fact that any cluster-decaying state is a resonance. It is shown that, by including the continuum part of the single particle spectrum, the shell model is able to reproduce cluster-decay widths. The diagonalization of the mean field is performed using a relative small single particle basis with two harmonic oscillator parameters. The first part of the basis is connected with the bound states while the second one with the states in continuum. The microscopic cluster formation amplitude and outgoing Coulomb wave function should have the same logarithmic derivative beyond the nuclear surface, for a given energy. Therefore this condition predicts in principle cluster-decay Q-value. This is equivalent to the 'plateau condition': the independence of the cluster decay width versus the channel radius. The dependence of the asymptotic tail of single particle states on mass number along a neutron chain, necessary to fulfil the plateau condition, can be induced by the second expansion ho parameter. This is based on the observation that Q-value decreases with increasing neutron number. Therefore the second term of the expansion, connected with the states in continuum, which mainly influences the cluster formation, should be more bound. This procedure is performed without changing the standard Woods-Saxon potential and the first ho expansion parameter, connected with the spectroscopic properties. The dependence of the second ho parameter versus

  16. Our calibrated model has poor predictive value: An example from the petroleum industry

    International Nuclear Information System (INIS)

    Carter, J.N.; Ballester, P.J.; Tavassoli, Z.; King, P.R.

    2006-01-01

    It is often assumed that once a model has been calibrated to measurements then it will have some level of predictive capability, although this may be limited. If the model does not have predictive capability then the assumption is that the model needs to be improved in some way. Using an example from the petroleum industry, we show that cases can exit where calibrated models have limited predictive capability. This occurs even when there is no modelling error present. It is also shown that the introduction of a small modelling error can make it impossible to obtain any models with useful predictive capability. We have been unable to find ways of identifying which calibrated models will have some predictive capacity and those which will not

  17. Dormancy Prediction Model in a Prepaid Predominant Mobile Market : A Customer Value Management Approach

    OpenAIRE

    Adeolu O. Dairo; Temitope Akinwumi

    2014-01-01

    Previous studies have predicted customer churn in the mobile indutry especially the postpaid customer segment of the market. However, only few studies have been published on the prepaid segment that could be used and operationalised within the marketing team that are responsible for the management of incident of prepaid churn. This is the first identifiable literature where customer dormancy is predicted along the customer value segmentation. In th...

  18. Predicting Calcium Values for Gastrointestinal Bleeding Patients in Intensive Care Unit Using Clinical Variables and Fuzzy Modeling

    Directory of Open Access Journals (Sweden)

    G Khalili-Zadeh-Mahani

    2016-07-01

    Full Text Available Introduction: Reducing unnecessary laboratory tests is an essential issue in the Intensive Care Unit. One solution for this issue is to predict the value of a laboratory test to specify the necessity of ordering the tests. The aim of this paper was to propose a clinical decision support system for predicting laboratory tests values. Calcium laboratory tests of three categories of patients, including upper and lower gastrointestinal bleeding, and unspecified hemorrhage of gastrointestinal tract, have been selected as the case studies for this research. Method: In this research, the data have been collected from MIMIC-II database. For predicting calcium laboratory values, a Fuzzy Takagi-Sugeno model is used and the input variables of the model are heart rate and previous value of calcium laboratory test. Results: The results showed that the values of calcium laboratory test for the understudy patients were predictable with an acceptable accuracy. In average, the mean absolute errors of the system for the three categories of the patients are 0.27, 0.29, and 0.28, respectively. Conclusion: In this research, using fuzzy modeling and two variables of heart rate and previous calcium laboratory values, a clinical decision support system was proposed for predicting laboratory values of three categories of patients with gastrointestinal bleeding. Using these two clinical values as input variables, the obtained results were acceptable and showed the capability of the proposed system in predicting calcium laboratory values. For achieving better results, the impact of more input variables should be studied. Since, the proposed system predicts the laboratory values instead of just predicting the necessity of the laboratory tests; it was more generalized than previous studies. So, the proposed method let the specialists make the decision depending on the condition of each patient.

  19. Predictive error dependencies when using pilot points and singular value decomposition in groundwater model calibration

    DEFF Research Database (Denmark)

    Christensen, Steen; Doherty, John

    2008-01-01

    over the model area. Singular value decomposition (SVD) of the (possibly weighted) sensitivity matrix of the pilot point based model produces eigenvectors of which we pick a small number corresponding to significant eigenvalues. Super parameters are defined as factors through which parameter...... nonlinear functions. Recommendations concerning the use of pilot points and singular value decomposition in real-world groundwater model calibration are finally given. (c) 2008 Elsevier Ltd. All rights reserved....

  20. Grey-Markov prediction model based on background value optimization and central-point triangular whitenization weight function

    Science.gov (United States)

    Ye, Jing; Dang, Yaoguo; Li, Bingjun

    2018-01-01

    Grey-Markov forecasting model is a combination of grey prediction model and Markov chain which show obvious optimization effects for data sequences with characteristics of non-stationary and volatility. However, the state division process in traditional Grey-Markov forecasting model is mostly based on subjective real numbers that immediately affects the accuracy of forecasting values. To seek the solution, this paper introduces the central-point triangular whitenization weight function in state division to calculate possibilities of research values in each state which reflect preference degrees in different states in an objective way. On the other hand, background value optimization is applied in the traditional grey model to generate better fitting data. By this means, the improved Grey-Markov forecasting model is built. Finally, taking the grain production in Henan Province as an example, it verifies this model's validity by comparing with GM(1,1) based on background value optimization and the traditional Grey-Markov forecasting model.

  1. Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines)

    Science.gov (United States)

    Carranza, Emmanuel John M.; Laborte, Alice G.

    2015-01-01

    Machine learning methods that have been used in data-driven predictive modeling of mineral prospectivity (e.g., artificial neural networks) invariably require large number of training prospect/locations and are unable to handle missing values in certain evidential data. The Random Forests (RF) algorithm, which is a machine learning method, has recently been applied to data-driven predictive mapping of mineral prospectivity, and so it is instructive to further study its efficacy in this particular field. This case study, carried out using data from Abra (Philippines), examines (a) if RF modeling can be used for data-driven modeling of mineral prospectivity in areas with a few (i.e., individual layers of evidential data. Furthermore, RF modeling can handle missing values in evidential data through an RF-based imputation technique whereas in WofE modeling values are simply represented by zero weights. Therefore, the RF algorithm is potentially more useful than existing methods that are currently used for data-driven predictive mapping of mineral prospectivity. In particular, it is not a purely black-box method like artificial neural networks in the context of data-driven predictive modeling of mineral prospectivity. However, further testing of the method in other areas with a few mineral occurrences is needed to fully investigate its usefulness in data-driven predictive modeling of mineral prospectivity.

  2. A Robust Statistical Model to Predict the Future Value of the Milk Production of Dairy Cows Using Herd Recording Data

    DEFF Research Database (Denmark)

    Græsbøll, Kaare; Kirkeby, Carsten Thure; Nielsen, Søren Saxmose

    2017-01-01

    The future value of an individual dairy cow depends greatly on its projected milk yield. In developed countries with developed dairy industry infrastructures, facilities exist to record individual cow production and reproduction outcomes consistently and accurately. Accurate prediction...... of the future value of a dairy cow requires further detailed knowledge of the costs associated with feed, management practices, production systems, and disease. Here, we present a method to predict the future value of the milk production of a dairy cow based on herd recording data only. The method consists...... of several steps to evaluate lifetime milk production and individual cow somatic cell counts and to finally predict the average production for each day that the cow is alive. Herd recording data from 610 Danish Holstein herds were used to train and test a model predicting milk production (including factors...

  3. [Establishment of risk evaluation model of peritoneal metastasis in gastric cancer and its predictive value].

    Science.gov (United States)

    Zhao, Junjie; Zhou, Rongjian; Zhang, Qi; Shu, Ping; Li, Haojie; Wang, Xuefei; Shen, Zhenbin; Liu, Fenglin; Chen, Weidong; Qin, Jing; Sun, Yihong

    2017-01-25

    To establish an evaluation model of peritoneal metastasis in gastric cancer, and to assess its clinical significance. Clinical and pathologic data of the consecutive cases of gastric cancer admitted between April 2015 and December 2015 in Department of General Surgery, Zhongshan Hospital of Fudan University were analyzed retrospectively. A total of 710 patients were enrolled in the study after 18 patients with other distant metastasis were excluded. The correlations between peritoneal metastasis and different factors were studied through univariate (Pearson's test or Fisher's exact test) and multivariate analyses (Binary Logistic regression). Independent predictable factors for peritoneal metastasis were combined to establish a risk evaluation model (nomogram). The nomogram was created with R software using the 'rms' package. In the nomogram, each factor had different scores, and every patient could have a total score by adding all the scores of each factor. A higher total score represented higher risk of peritoneal metastasis. Receiver operating characteristic (ROC) curve analysis was used to compare the sensitivity and specificity of the established nomogram. Delong. Delong. Clarke-Pearson test was used to compare the difference of the area under the curve (AUC). The cut-off value was determined by the AUC, when the ROC curve had the biggest AUC, the model had the best sensitivity and specificity. Among 710 patients, 47 patients had peritoneal metastasis (6.6%), including 30 male (30/506, 5.9%) and 17 female (17/204, 8.3%); 31 were ≥ 60 years old (31/429, 7.2%); 38 had tumor ≥ 3 cm(38/461, 8.2%). Lauren classification indicated that 2 patients were intestinal type(2/245, 0.8%), 8 patients were mixed type(8/208, 3.8%), 11 patients were diffuse type(11/142, 7.7%), and others had no associated data. CA19-9 of 13 patients was ≥ 37 kU/L(13/61, 21.3%); CA125 of 11 patients was ≥ 35 kU/L(11/36, 30.6%); CA72-4 of 11 patients was ≥ 10 kU/L(11/39, 28

  4. Predictive error dependencies when using pilot points and singular value decomposition in groundwater model calibration

    DEFF Research Database (Denmark)

    Christensen, Steen; Doherty, John

    2008-01-01

    A significant practical problem with the pilot point method is to choose the location of the pilot points. We present a method that is intended to relieve the modeler from much of this responsibility. The basic idea is that a very large number of pilot points are distributed more or less uniformly...... over the model area. Singular value decomposition (SVD) of the (possibly weighted) sensitivity matrix of the pilot point based model produces eigenvectors of which we pick a small number corresponding to significant eigenvalues. Super parameters are defined as factors through which parameter...... combinations corresponding to the chosen eigenvectors are multiplied to obtain the pilot point values. The model can thus be transformed from having many-pilot-point parameters to having a few super parameters that can be estimated by nonlinear regression on the basis of the available observations. (This...

  5. Improved prediction of higher heating value of biomass using an artificial neural network model based on proximate analysis.

    Science.gov (United States)

    Uzun, Harun; Yıldız, Zeynep; Goldfarb, Jillian L; Ceylan, Selim

    2017-06-01

    As biomass becomes more integrated into our energy feedstocks, the ability to predict its combustion enthalpies from routine data such as carbon, ash, and moisture content enables rapid decisions about utilization. The present work constructs a novel artificial neural network model with a 3-3-1 tangent sigmoid architecture to predict biomasses' higher heating values from only their proximate analyses, requiring minimal specificity as compared to models based on elemental composition. The model presented has a considerably higher correlation coefficient (0.963) and lower root mean square (0.375), mean absolute (0.328), and mean bias errors (0.010) than other models presented in the literature which, at least when applied to the present data set, tend to under-predict the combustion enthalpy. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. What Is the Predictive Value of Animal Models for Vaccine Efficacy in Humans? Consideration of Strategies to Improve the Value of Animal Models.

    Science.gov (United States)

    Herati, Ramin Sedaghat; Wherry, E John

    2018-04-02

    Animal models are an essential feature of the vaccine design toolkit. Although animal models have been invaluable in delineating the mechanisms of immune function, their precision in predicting how well specific vaccines work in humans is often suboptimal. There are, of course, many obvious species differences that may limit animal models from predicting all details of how a vaccine works in humans. However, careful consideration of which animal models may have limitations should also allow more accurate interpretations of animal model data and more accurate predictions of what is to be expected in clinical trials. In this article, we examine some of the considerations that might be relevant to cross-species extrapolation of vaccine-related immune responses for the prediction of how vaccines will perform in humans. Copyright © 2018 Cold Spring Harbor Laboratory Press; all rights reserved.

  7. Hemorrhage Prediction Models in Surgical Intensive Care: Bedside Monitoring Data Adds Information to Lab Values.

    Science.gov (United States)

    De Pasquale, Marco; Moss, Travis J; Cerutti, Sergio; Calland, James Forrest; Lake, Douglas E; Moorman, J Randall; Ferrario, Manuela

    2017-11-01

    Hemorrhage is a frequent complication in surgery patients; its identification and management have received increasing attention as a target for quality improvement in patient care in the Intensive Care Unit (ICU). The purposes of this work were 1) to find an early detection model for hemorrhage by exploring the range of data mining methods that are currently available, and 2) to compare prediction models utilizing continuously measured physiological data from bedside monitors to those using commonly obtained laboratory tests. We studied 3766 patients admitted to the University of Virginia Health System Surgical Trauma Burn ICU. Hemorrhage was defined as three or more units of red blood cells transfused within 24 h without red blood cell transfusion in the preceding 24 h. 222 patients (5.9%) experienced a hemorrhage, and multivariate models based on vital signs and their trends showed good results (AUC = 76.1%). The hematocrit, not surprisingly, had excellent performance (AUC = 87.7%). Models that included both continuous monitoring and laboratory tests had the best performance (AUC = 92.2%). The results point to a combined strategy of continuous monitoring and intermittent lab tests as a reasonable clinical approach to the early detection of hemorrhage in the surgical ICU.

  8. A Robust Statistical Model to Predict the Future Value of the Milk Production of Dairy Cows Using Herd Recording Data

    DEFF Research Database (Denmark)

    Græsbøll, Kaare; Kirkeby, Carsten Thure; Nielsen, Søren Saxmose

    2017-01-01

    of the future value of a dairy cow requires further detailed knowledge of the costs associated with feed, management practices, production systems, and disease. Here, we present a method to predict the future value of the milk production of a dairy cow based on herd recording data only. The method consists......The future value of an individual dairy cow depends greatly on its projected milk yield. In developed countries with developed dairy industry infrastructures, facilities exist to record individual cow production and reproduction outcomes consistently and accurately. Accurate prediction...... of somatic cell count. We conclude that estimates of future average production can be used on a day-to-day basis to rank cows for culling, or can be implemented in simulation models of within-herd disease spread to make operational decisions, such as culling versus treatment. An advantage of the approach...

  9. A novel complex model of hemodialysis adequacy: Predictive value and relationship with malnutrition inflammation score

    Directory of Open Access Journals (Sweden)

    Vlatković Vlastimir

    2017-01-01

    Full Text Available Target dialysis dose to ensure the best patient outcome is still a matter of debate. Traditional models have a number of limitations and do not comprehensively reflect all factors involved. In this study we present a new complex model of dialysis adequacy, the hemodialysis adequacy score (HAS, and evaluate its prognostic value, as well as its relationship with the malnutrition-inflammation score (MIS. The components of HAS included paradigms of the 6 major factors known to influence the outcome of hemodialysis (HD patients: the modified Karnofsky index (KI, the Charlson comorbidity index (CCI, Kt/V and URR measures of dialysis dose, body mass index (BMI and serum albumin level, serum levels of hemoglobin and ferritin, intact parathyroid hormone (iPTH and calciumphosphorus solubility product. The score was evaluated in a 24-month prospective study on 147 HD patients. Odds ratio analysis showed that hospitalized patients had twice the chance to have HAS >13 compared to those who were not hospitalized during the study period (OR=2.152, CI 95% (1.0024- 4.619. Mortality rate was significantly higher in patients with a HAS >13 at the 12-month follow-up (χ2=16.416, p 13 had significantly higher probability of death (log-rank Cox- Mantel=17.920, df=1, p <0.00023. The HAS directly and significantly correlated with the MIS at all measurements (p <0.0001. Results confirmed that the HAS is a useful tool to assess dialysis adequacy with a good prognostic value. The cutoff level for the HAS at 13 points was associated with an unfavorable outcome.

  10. Linear and regressive stochastic models for prediction of daily maximum ozone values at Mexico City atmosphere

    Energy Technology Data Exchange (ETDEWEB)

    Bravo, J. L [Instituto de Geofisica, UNAM, Mexico, D.F. (Mexico); Nava, M. M [Instituto Mexicano del Petroleo, Mexico, D.F. (Mexico); Gay, C [Centro de Ciencias de la Atmosfera, UNAM, Mexico, D.F. (Mexico)

    2001-07-01

    We developed a procedure to forecast, with 2 or 3 hours, the daily maximum of surface ozone concentrations. It involves the adjustment of Autoregressive Integrated and Moving Average (ARIMA) models to daily ozone maximum concentrations at 10 monitoring atmospheric stations in Mexico City during one-year period. A one-day forecast is made and it is adjusted with the meteorological and solar radiation information acquired during the first 3 hours before the occurrence of the maximum value. The relative importance for forecasting of the history of the process and of meteorological conditions is evaluated. Finally an estimate of the daily probability of exceeding a given ozone level is made. [Spanish] Se aplica un procedimiento basado en la metodologia conocida como ARIMA, para predecir, con 2 o 3 horas de anticipacion, el valor maximo de la concentracion diaria de ozono. Esta basado en el calculo de autorregresiones y promedios moviles aplicados a los valores maximos de ozono superficial provenientes de 10 estaciones de monitoreo atmosferico en la Ciudad de Mexico y obtenidos durante un ano de muestreo. El pronostico para un dia se ajusta con la informacion meteorologica y de radiacion solar correspondiente a un periodo que antecede con al menos tres horas la ocurrencia esperada del valor maximo. Se compara la importancia relativa de la historia del proceso y de las condiciones meteorologicas previas para el pronostico. Finalmente se estima la probabilidad diaria de que un nivel normativo o preestablecido para contingencias de ozono sea rebasado.

  11. Applying Regression Models with Mixed Frequency Data in Modeling and Prediction of Iran's Wheat Import Value (Generalized OLS-based ARDL Approach

    Directory of Open Access Journals (Sweden)

    mitra jalerajabi

    2014-10-01

    Full Text Available Due to the importance of the import management, this study applies generalized ARDL approach to estimate MIDAS regression for wheat import value and to compare the accuracy of forecasts with those competed by the regression with adjusted data model. Mixed frequency sampling models aim to extract information with high frequency indicators so that independent variables with lower frequencies are modeled and foorcasted. Due to a more precise identification of the relationships among the variables, more accurate prediction is expected. Based on the results of both estimated regression with adjusted frequency models and MIDAS for the years 1978-2003 as a training period, wheat import value with internal products and exchange rate was positively related, while the relative price variable had an adverse relation with the Iran's wheat import value. Based on the results from the conventional statistics such as RMSE, MAD, MAPE and the statistical significance, MIDAS models using data sets of annual wheat import value, internal products, relative price and seasonal exchange rate significantly improves prediction of annual wheat import value for the years2004-2008 as a testing period. Hence, it is recommended that applying prediction approaches with mixed data improves modeling and prediction of agricultural import value, especially for strategic import products.

  12. Stochastic Model Predictive Fault Tolerant Control Based on Conditional Value at Risk for Wind Energy Conversion System

    Directory of Open Access Journals (Sweden)

    Yun-Tao Shi

    2018-01-01

    Full Text Available Wind energy has been drawing considerable attention in recent years. However, due to the random nature of wind and high failure rate of wind energy conversion systems (WECSs, how to implement fault-tolerant WECS control is becoming a significant issue. This paper addresses the fault-tolerant control problem of a WECS with a probable actuator fault. A new stochastic model predictive control (SMPC fault-tolerant controller with the Conditional Value at Risk (CVaR objective function is proposed in this paper. First, the Markov jump linear model is used to describe the WECS dynamics, which are affected by many stochastic factors, like the wind. The Markov jump linear model can precisely model the random WECS properties. Second, the scenario-based SMPC is used as the controller to address the control problem of the WECS. With this controller, all the possible realizations of the disturbance in prediction horizon are enumerated by scenario trees so that an uncertain SMPC problem can be transformed into a deterministic model predictive control (MPC problem. Finally, the CVaR object function is adopted to improve the fault-tolerant control performance of the SMPC controller. CVaR can provide a balance between the performance and random failure risks of the system. The Min-Max performance index is introduced to compare the fault-tolerant control performance with the proposed controller. The comparison results show that the proposed method has better fault-tolerant control performance.

  13. Oxygen and hydrogen isotope ratios in tree rings: how well do models predict observed values?

    CSIR Research Space (South Africa)

    Waterhouse, JS

    2002-07-30

    Full Text Available Annual oxygen and hydrogen isotope ratios in the alpha-cellulose of the latewood of oak (Quercus robur L.) growing on well-drained ground in Norfolk, UK have been measured. The authors have compared the observed values of isotope ratios with those...

  14. The value and cost of complexity in predictive modelling: role of tissue anisotropic conductivity and fibre tracts in neuromodulation

    Science.gov (United States)

    Salman Shahid, Syed; Bikson, Marom; Salman, Humaira; Wen, Peng; Ahfock, Tony

    2014-06-01

    Objectives. Computational methods are increasingly used to optimize transcranial direct current stimulation (tDCS) dose strategies and yet complexities of existing approaches limit their clinical access. Since predictive modelling indicates the relevance of subject/pathology based data and hence the need for subject specific modelling, the incremental clinical value of increasingly complex modelling methods must be balanced against the computational and clinical time and costs. For example, the incorporation of multiple tissue layers and measured diffusion tensor (DTI) based conductivity estimates increase model precision but at the cost of clinical and computational resources. Costs related to such complexities aggregate when considering individual optimization and the myriad of potential montages. Here, rather than considering if additional details change current-flow prediction, we consider when added complexities influence clinical decisions. Approach. Towards developing quantitative and qualitative metrics of value/cost associated with computational model complexity, we considered field distributions generated by two 4 × 1 high-definition montages (m1 = 4 × 1 HD montage with anode at C3 and m2 = 4 × 1 HD montage with anode at C1) and a single conventional (m3 = C3-Fp2) tDCS electrode montage. We evaluated statistical methods, including residual error (RE) and relative difference measure (RDM), to consider the clinical impact and utility of increased complexities, namely the influence of skull, muscle and brain anisotropic conductivities in a volume conductor model. Main results. Anisotropy modulated current-flow in a montage and region dependent manner. However, significant statistical changes, produced within montage by anisotropy, did not change qualitative peak and topographic comparisons across montages. Thus for the examples analysed, clinical decision on which dose to select would not be altered by the omission of anisotropic brain conductivity

  15. Predictive value and modeling analysis of MSCT signs in gastrointestinal stromal tumors (GISTs) to pathological risk degree.

    Science.gov (United States)

    Wang, J-K

    2017-03-01

    By analyzing MSCT (multi-slice computed tomography) signs with different risks in gastrointestinal stromal tumors, this paper aimed to discuss the predictive value and modeling analysis of MSCT signs in GISTs (gastrointestinal stromal tumor) to pathological risk degree. 100 cases of primary GISTs with abdominal and pelvic MSCT scan were involved in this study. All MSCT scan findings and enhanced findings were analyzed and compared among cases with different risk degree of pathology. Then GISTs diagnostic model was established by using support vector machine (SVM) algorithm, and its diagnostic value was evaluated as well. All lesions were solitary, among which there were 46 low-risk cases, 24 medium-risk cases and 30 high-risk cases. For all high-risk, medium-risk and low-risk GISTs, there were statistical differences in tumor growth pattern, size, shape, fat space, with or without calcification, ulcer, enhancement method and peritumoral and intratumoral vessels (pvalue at each period (plain scan, arterial phase, venous phase) (p>0.05). The apparent difference lied in plain scan, arterial phase and venous phase for each risk degree. The diagnostic accuracy of SVM diagnostic model established with 10 imaging features as indexes was 70.0%, and it was especially reliable when diagnosing GISTs of high or low risk. Preoperative analysis of MSCT features is clinically significant for its diagnosis of risk degree and prognosis; GISTs diagnostic model established on the basis of SVM possesses high diagnostic value.

  16. PREDICTIVE MODEL FOR THE ADDED VALUE OF SULTANA SEEDLESS GRAPE PRODUCTION

    Directory of Open Access Journals (Sweden)

    Umut Burak Geyikci

    2015-07-01

    Full Text Available Turkey after USA is the second important raisin grape producer by cv “Sultana”in the world (Kara,2014. The Manisa district alone accounts for 31% of totalgrape production and 80% of the whole sultana seedless raisin grape production inTurkey. 95% of total grape output generated in Manisa is made up of Sultanaseedless grape(TUIK, 2012. In thisstudy, the added value of grape production inManisa has been calculated and according to the findings, the per capita addedvalue of grape production has been computed. In order to calculate the addedvalue of grape production in Manisa, costs of labor, fuel, fertilizer, disinfection,hormone, repair and maintenance of the businesses around have been investigated.After calculated costs had been deducted from total business income, the totaladded value of grape production in Manisa and the per capita added valueofgrape production in Manisa were attained The efficiency per hectare in the sampleproduction units, which was investigated during the field research, measures up to26.470 kg. The percentile distributions of cost items at this efficiency level are;27,8% fuel costs, 23,2% fertilizing costs, 19,9% irrigation costs, 11,4%disinfection costs, 7,5% hormone usage costs, 4,6% harvesting and transportingcosts and the rest consists of maintenance costs. When the costs are deducted from income per hectare (11.646 USD, the added value per hectare turns out to be8843 USD

  17. Protein secondary structure prediction using a small training set (compact model) combined with a Complex-valued neural network approach.

    Science.gov (United States)

    Rashid, Shamima; Saraswathi, Saras; Kloczkowski, Andrzej; Sundaram, Suresh; Kolinski, Andrzej

    2016-09-13

    Protein secondary structure prediction (SSP) has been an area of intense research interest. Despite advances in recent methods conducted on large datasets, the estimated upper limit accuracy is yet to be reached. Since the predictions of SSP methods are applied as input to higher-level structure prediction pipelines, even small errors may have large perturbations in final models. Previous works relied on cross validation as an estimate of classifier accuracy. However, training on large numbers of protein chains compromises the classifier ability to generalize to new sequences. This prompts a novel approach to training and an investigation into the possible structural factors that lead to poor predictions. Here, a small group of 55 proteins termed the compact model is selected from the CB513 dataset using a heuristics-based approach. In a prior work, all sequences were represented as probability matrices of residues adopting each of Helix, Sheet and Coil states, based on energy calculations using the C-Alpha, C-Beta, Side-chain (CABS) algorithm. The functional relationship between the conformational energies computed with CABS force-field and residue states is approximated using a classifier termed the Fully Complex-valued Relaxation Network (FCRN). The FCRN is trained with the compact model proteins. The performance of the compact model is compared with traditional cross-validated accuracies and blind-tested on a dataset of G Switch proteins, obtaining accuracies of ∼81 %. The model demonstrates better results when compared to several techniques in the literature. A comparative case study of the worst performing chain identifies hydrogen bond contacts that lead to Coil ⇔ Sheet misclassifications. Overall, mispredicted Coil residues have a higher propensity to participate in backbone hydrogen bonding than correctly predicted Coils. The implications of these findings are: (i) the choice of training proteins is important in preserving the generalization of a

  18. Benzene patterns in different urban environments and a prediction model for benzene rates based on NOx values

    Science.gov (United States)

    Paz, Shlomit; Goldstein, Pavel; Kordova-Biezuner, Levana; Adler, Lea

    2017-04-01

    Exposure to benzene has been associated with multiple severe impacts on health. This notwithstanding, at most monitoring stations, benzene is not monitored on a regular basis. The aims of the study were to compare benzene rates in different urban environments (region with heavy traffic and industrial region), to analyse the relationship between benzene and meteorological parameters in a Mediterranean climate type, to estimate the linkages between benzene and NOx and to suggest a prediction model for benzene rates based on NOx levels in order contribute to a better estimation of benzene. Data were used from two different monitoring stations, located on the eastern Mediterranean coast: 1) a traffic monitoring station in Tel Aviv, Israel (TLV) located in an urban region with heavy traffic; 2) a general air quality monitoring station in Haifa Bay (HIB), located in Israel's main industrial region. At each station, hourly, daily, monthly, seasonal, and annual data of benzene, NOx, mean temperature, relative humidity, inversion level, and temperature gradient were analysed over three years: 2008, 2009, and 2010. A prediction model for benzene rates based on NOx levels (which are monitored regularly) was developed to contribute to a better estimation of benzene. The severity of benzene pollution was found to be considerably higher at the traffic monitoring station (TLV) than at the general air quality station (HIB), despite the location of the latter in an industrial area. Hourly, daily, monthly, seasonal, and annual patterns have been shown to coincide with anthropogenic activities (traffic), the day of the week, and atmospheric conditions. A strong correlation between NOx and benzene allowed the development of a prediction model for benzene rates, based on NOx, the day of the week, and the month. The model succeeded in predicting the benzene values throughout the year (except for September). The severity of benzene pollution was found to be considerably higher at the

  19. Modelo de previsão de value at risk utilizando volatilidade de longo prazo = Value at Risk prediction model using long term volatility

    Directory of Open Access Journals (Sweden)

    Vinicius Mothé Maia

    2016-07-01

    Full Text Available Tendo em vista a importância do Value at Risk (VaR como medida de risco para instituições financeiras e agências de risco, o presente estudo avaliou se o modelo ARLS é mais preciso no cálculo do VaR de longo prazo que os modelos tradicionais, dada sua maior adequação para a previsão da volatilidade. Considerando a utilização do VaR pelos agentes de mercado como medida de risco para o gerenciamento de portfólios é importante sua adequada mensuração. A partir de dados diários dos mercados de ações e cambial dos BRICS (Brasil, Rússia, Índia, China e África do Sul foram calculadas as volatilidades futuras para 15 dias, 1 mês e 3 meses. Em seguida, calculou-se as medidas tradicionais de avaliação da precisão do VaR. Os resultados sugerem a superioridade do modelo ARLS para a previsão da volatilidade cambial, capaz de prever corretamente o número de violações em 33% dos casos, enquanto os modelos tradicionais não obtiveram um bom desempenho. Com relação ao mercado acionário, os modelos GARCH e ARLS apresentaram desempenho similar. O modelo GARCH é superior considerando a perda média quadrática. Esses resultados apontam para a escolha do modelo ARLS no cálculo do VaR de portfólios cambiais devido a maior precisão alcançada. Ajuda assim os agentes de mercado a melhor gerirem o risco de suas carteiras. Em relação ao mercado acionário, em função do desempenho similar dos modelos GARCH e ARLS, o modelo GARCH é o mais indicado devido a sua maior simplicidade e fácil implementação computacional. Having in mind the importance of Value at Risk (VaR as a risk measure for financial institutions and rating agencies, this study evaluated whether the ARLS model is more accurate in the calculation of the long term VaR than the traditional models, considering it is more appropriate for predicting the long-term volatility. Due to the fact that VaR s being used for market players as a measure of risk for the portfolio

  20. [Predicting value of 2014 European guidelines risk prediction model for sudden cardiac death (HCM Risk-SCD) in Chinese patients with hypertrophic cardiomyopathy].

    Science.gov (United States)

    Li, W X; Liu, L W; Wang, J; Zuo, L; Yang, F; Kang, N; Lei, C H

    2017-12-24

    Objective: To evaluate the predicting value of the 2014 European Society of Cardiology (ESC) guidelines risk prediction model for sudden cardiac death (HCM Risk-SCD) in Chinese patients with hypertrophic cardiomyopathy (HCM), and to explore the predictors of adverse cardiovascular events in Chinese HCM patients. Methods: The study population consisted of a consecutive 207 HCM patients admitted in our center from October 2014 to October 2016. All patients were followed up to March 2017. The 5-year SCD probability of each patient was estimated using HCM Risk-SCD model based on electrocardiogram, echocardiography and cardiac magnetic resonance (CMR) examination results. The primary, second, and composite endpoints were recorded. The primary endpoint included SCD and appropriate ICD therapy, identical to the HCM Risk-SCD endpoint. The second endpoint included acute myocardial infarction, hospitalization for heart failure, thrombus embolism and end-stage HCM. The composite endpoint was either the primary or the second endpoint. Patients were divided into the 3 categories according to 5-year SCD probability assessed by HCM Risk-SCD model: low risk grouprisk group ≥4% torisk group≥6%. Results: (1) Prevalence of endpoints: All 207 HCM patients completed the follow-up (350 (230, 547) days). During follow-up, 8 (3.86%) patients reached the primary endpoints (3 cases of SCD, 3 cases of survival after defibrillation, and 2 cases of appropriate ICD discharge); 21 (10.14%) patients reached the second endpoints (1 case of acute myocardial infarction, 16 cases of heart failure hospitalization, 2 cases of thromboembolism, and 2 cases of end-stage HCM). (2) Predicting value of HCM Risk-SCD model: Patients with primary endpoints had higher prevalence of syncope and intermediate-high risk of 5-year SCD, as compared to those without primary endpoints (both Pvalue of HCM Risk-SCD model: The low risk group included 122 patients (59%), the intermediate risk group 42 (20%), and the

  1. A non-parametric mixture model for genome-enabled prediction of genetic value for a quantitative trait.

    Science.gov (United States)

    Gianola, Daniel; Wu, Xiao-Lin; Manfredi, Eduardo; Simianer, Henner

    2010-10-01

    A Bayesian nonparametric form of regression based on Dirichlet process priors is adapted to the analysis of quantitative traits possibly affected by cryptic forms of gene action, and to the context of SNP-assisted genomic selection, where the main objective is to predict a genomic signal on phenotype. The procedure clusters unknown genotypes into groups with distinct genetic values, but in a setting in which the number of clusters is unknown a priori, so that standard methods for finite mixture analysis do not work. The central assumption is that genetic effects follow an unknown distribution with some "baseline" family, which is a normal process in the cases considered here. A Bayesian analysis based on the Gibbs sampler produces estimates of the number of clusters, posterior means of genetic effects, a measure of credibility in the baseline distribution, as well as estimates of parameters of the latter. The procedure is illustrated with a simulation representing two populations. In the first one, there are 3 unknown QTL, with additive, dominance and epistatic effects; in the second, there are 10 QTL with additive, dominance and additive × additive epistatic effects. In the two populations, baseline parameters are inferred correctly. The Dirichlet process model infers the number of unique genetic values correctly in the first population, but it produces an understatement in the second one; here, the true number of clusters is over 900, and the model gives a posterior mean estimate of about 140, probably because more replication of genotypes is needed for correct inference. The impact on inferences of the prior distribution of a key parameter (M), and of the extent of replication, was examined via an analysis of mean body weight in 192 paternal half-sib families of broiler chickens, where each sire was genotyped for nearly 7,000 SNPs. In this small sample, it was found that inference about the number of clusters was affected by the prior distribution of M. For a

  2. Predicting sickness absence-are extended health check-ups of any value? Comparisons of three individual risk models.

    Science.gov (United States)

    Falkenberg, Anna; Nyfjäll, Mats; Bildt, Carina; Vingård, Eva

    2009-01-01

    To predict sickness absence by three health check-up models. A study group of 821 participants from the public sector in Sweden where three health check-up models were compared 1) the limited variable model including smoking, body mass index, blood pressure, and cholesterol, 2) the several variable model including smoking, waist-hip ratio, blood pressure, relation between low density lipoproteins and high density lipoproteins, serum triglycerides, and fitness test, and 3) Self-rated health measured by one single question. Sickness absence data during 1 year was delivered from the employer. The three models served their purpose to predict sickness absence. The self-rated health-model with one single question has as good quality in predestination as more complicated models. This may have an implication for cost-effective procedures in occupational health services.

  3. Preoperative estimation of disc herniation recurrence after microdiscectomy: predictive value of a multivariate model based on radiographic parameters.

    Science.gov (United States)

    Belykh, Evgenii; Krutko, Alexander V; Baykov, Evgenii S; Giers, Morgan B; Preul, Mark C; Byvaltsev, Vadim A

    2017-03-01

    Recurrence of lumbar disc herniation (rLDH) is one of the unfavorable outcomes after microdiscectomy. Prediction of the patient population with increased risk of rLDH is important because patients may benefit from preventive measures or other surgical options. The study assessed preoperative factors associated with rLDH after microdiscectomy and created a mathematical model for estimation of chances for rLDH. This is a retrospective case-control study. The study includes patients who underwent microdiscectomy for LDH. Lumbar disc herniation recurrence was determined using magnetic resonance imaging. The study included 350 patients with LDH and a minimum of 3 years of follow-up. Patients underwent microdiscectomy for LDH at the L4-L5 and L5-S1 levels from 2008 to 2012. Patients were divided into two groups to identify predictors of recurrence: those who developed rLDH (n=50) within 3 years and those who did not develop rLDH (n=300) within the same follow-up period. Multivariate analysis was performed using patient baseline clinical and radiography data. Non-linear, multivariate, logistic regression analysis was used to build a predictive model. Recurrence of LDH occurred within 1 to 48 months after microdiscectomy. Preoperatively, patients who developed rLDH were smokers (70% vs. 27%, pnon-linear modeling allowed for more accurate prediction of rLDH (90% correct prediction of rLDH; 99% correct prediction of no rLDH) than other univariate logit models. Preoperative radiographic parameters in patients with LDH can be used to assess the risk of recurrence after microdiscectomy. The multifactorial non-linear model provided more accurate rLDH probability estimation than the univariate analyses. The software developed from this model may be implemented during patient counseling or decision making when choosing the type of primary surgery for LDH. Copyright © 2016 Elsevier Inc. All rights reserved.

  4. Adjust cut-off values of immunohistochemistry models to predict risk of distant recurrence in invasive breast carcinoma patients

    Directory of Open Access Journals (Sweden)

    Yen-Ying Chen

    2016-12-01

    Conclusion: It is necessary to adjust the cut-off values of IHC-based prognostic models to fit the purpose. If the estimated risk is clearly high or low, it may be reasonable to omit multigene assays when cost is a consideration.

  5. Liver stiffness value-based risk estimation of late recurrence after curative resection of hepatocellular carcinoma: development and validation of a predictive model.

    Directory of Open Access Journals (Sweden)

    Kyu Sik Jung

    Full Text Available Preoperative liver stiffness (LS measurement using transient elastography (TE is useful for predicting late recurrence after curative resection of hepatocellular carcinoma (HCC. We developed and validated a novel LS value-based predictive model for late recurrence of HCC.Patients who were due to undergo curative resection of HCC between August 2006 and January 2010 were prospectively enrolled and TE was performed prior to operations by study protocol. The predictive model of late recurrence was constructed based on a multiple logistic regression model. Discrimination and calibration were used to validate the model.Among a total of 139 patients who were finally analyzed, late recurrence occurred in 44 patients, with a median follow-up of 24.5 months (range, 12.4-68.1. We developed a predictive model for late recurrence of HCC using LS value, activity grade II-III, presence of multiple tumors, and indocyanine green retention rate at 15 min (ICG R15, which showed fairly good discrimination capability with an area under the receiver operating characteristic curve (AUROC of 0.724 (95% confidence intervals [CIs], 0.632-0.816. In the validation, using a bootstrap method to assess discrimination, the AUROC remained largely unchanged between iterations, with an average AUROC of 0.722 (95% CIs, 0.718-0.724. When we plotted a calibration chart for predicted and observed risk of late recurrence, the predicted risk of late recurrence correlated well with observed risk, with a correlation coefficient of 0.873 (P<0.001.A simple LS value-based predictive model could estimate the risk of late recurrence in patients who underwent curative resection of HCC.

  6. QSPR Models for Predicting Log Pliver Values for Volatile Organic Compounds Combining Statistical Methods and Domain Knowledge

    Directory of Open Access Journals (Sweden)

    Mónica F. Díaz

    2012-12-01

    Full Text Available Volatile organic compounds (VOCs are contained in a variety of chemicals that can be found in household products and may have undesirable effects on health. Thereby, it is important to model blood-to-liver partition coefficients (log Pliver for VOCs in a fast and inexpensive way. In this paper, we present two new quantitative structure-property relationship (QSPR models for the prediction of log Pliver, where we also propose a hybrid approach for the selection of the descriptors. This hybrid methodology combines a machine learning method with a manual selection based on expert knowledge. This allows obtaining a set of descriptors that is interpretable in physicochemical terms. Our regression models were trained using decision trees and neural networks and validated using an external test set. Results show high prediction accuracy compared to previous log Pliver models, and the descriptor selection approach provides a means to get a small set of descriptors that is in agreement with theoretical understanding of the target property.

  7. Prognostic value of a systemic inflammatory response index in metastatic renal cell carcinoma and construction of a predictive model

    Science.gov (United States)

    Li, Hongzhao; Chen, Luyao; Li, Xintao; Zhang, Yu; Xie, Yongpeng; Zhang, Xu

    2017-01-01

    Inflammation act as a crucial role in carcinogenesis and tumor progression. In this study, we aim to investigate the prognostic significance of systemic inflammatory biomarkers in metastatic renal cell carcinoma (mRCC) and develop a survival predictive model. One hundred and sixty-one mRCC patients who had undergone cytoreductive nephrectomy were enrolled from January 2006 to December 2013. We created a systemic inflammation response index (SIRI) basing on pretreatment hemoglobin and lymphocyte to monocyte ratio (LMR), and evaluated its associations with overall survival (OS) and clinicopathological features. Pretreatment hemoglobin and LMR both remained as independent factors adjusted for other markers of systemic inflammation responses and conventional clinicopathological parameters. A high SIRI seems to be an independent prognosis predictor of worse OS and was significantly correlated with aggressive tumor behaviors. Inclusion of the SIRI into a prognostic model including Fuhrman grade, histology, tumor necrosis and targeted therapy established a nomogram, which accurately predicted 1-year survival for mRCC patients. The SIRI seems to be a prognostic biomarker in mRCC patients. The proposed nomogram can be applied to predict OS of patients with mRCC after nephrectomy. PMID:28881716

  8. Limited predictive value of the IDF definition of metabolic syndrome for the diagnosis of insulin resistance measured with the oral minimal model.

    Science.gov (United States)

    Ghanassia, E; Raynaud de Mauverger, E; Brun, J-F; Fedou, C; Mercier, J

    2009-01-01

    To assess the agreement of the NCEP ATP-III and the IDF definitions of metabolic syndrome and to determine their predictive values for the diagnosis of insulin resistance. For this purpose, we recruited 150 subjects (94 women and 56 men) and determined the presence of metabolic syndrome using the NCEP-ATP III and IDF definitions. We evaluated their insulin sensitivity S(I) using Caumo's oral minimal model after a standardized hyperglucidic breakfast test. Subjects whose S(I) was in the lowest quartile were considered as insulin resistant. We then calculated sensitivity, specificity, positive and negative predictive values of both definitions for the diagnosis of insulin resistance. The prevalence of metabolic syndrome was 37.4% (NCEP-ATP III) and 40% (IDF). Agreement between the two definitions was 96%. Using NCEP-ATP III and IDF criteria for the identification of insulin resistant subjects, sensitivity was 55.3% and 63%, specificity was 68.8% and 67.8%, positive predictive value was 37.5% and 40%, negative predictive value was 81.9% and 84.5%, respectively. Positive predictive value increased with the number of criteria for both definitions. Whatever the definition, the scoring of metabolic syndrome is not a reliable tool for the individual diagnosis of insulin resistance, and is more useful for excluding this diagnosis.

  9. Predictive Values of Electroencephalography (EEG) in Epilepsy ...

    African Journals Online (AJOL)

    Predictive Values of Electroencephalography (EEG) in Epilepsy Patients with Abnormal Behavioural Symptoms. OR Obiako, SO Adeyemi, TL Sheikh, LF Owolabi, MA Majebi, MO Gomina, F Adebayo, EU Iwuozo ...

  10. Probabilistic maximum-value wind prediction for offshore environments

    DEFF Research Database (Denmark)

    Staid, Andrea; Pinson, Pierre; Guikema, Seth D.

    2015-01-01

    statistical models to predict the full distribution of the maximum-value wind speeds in a 3 h interval. We take a detailed look at the performance of linear models, generalized additive models and multivariate adaptive regression splines models using meteorological covariates such as gust speed, wind speed......, convective available potential energy, Charnock, mean sea-level pressure and temperature, as given by the European Center for Medium-Range Weather Forecasts forecasts. The models are trained to predict the mean value of maximum wind speed, and the residuals from training the models are used to develop......, and probabilistic forecasts result in greater value to the end-user. The models outperform traditional baseline forecast methods and achieve low predictive errors on the order of 1–2 m s−1. We show the results of their predictive accuracy for different lead times and different training methodologies....

  11. Updated activated sludge model number 1 parameter values for improved prediction of nitrogen removal in activated sludge processes: validation at 13 full-scale plants.

    Science.gov (United States)

    Choubert, Jean-Marc; Stricker, Anne-Emmanuelle; Marquot, Aurélien; Racault, Yvan; Gillot, Sylvie; Héduit, Alain

    2009-01-01

    The Activated Sludge Model number 1 (ASM1) is the main model used in simulation projects focusing on nitrogen removal. Recent laboratory-scale studies have found that the default values given 20 years ago for the decay rate of nitrifiers and for the heterotrophic biomass yield in anoxic conditions were inadequate. To verify the relevance of the revised parameter values at full scale, a series of simulations were carried out with ASM1 using the original and updated set of parameters at 20 degrees C and 10 degrees C. The simulation results were compared with data collected at 13 full-scale nitrifying-denitrifying municipal treatment plants. This work shows that simulations using the original ASM1 default parameters tend to overpredict the nitrification rate and underpredict the denitrification rate. The updated set of parameters allows more realistic predictions over a wide range of operating conditions.

  12. Predicting Customer Lifetime Value in Multi-Service Industries

    NARCIS (Netherlands)

    A.C.D. Donkers (Bas); P.C. Verhoef (Peter); M.G. de Jong (Martijn)

    2003-01-01

    textabstractCustomer lifetime value (CLV) is a key-metric within CRM. Although, a large number of marketing scientists and practitioners argue in favor of this metric, there are only a few studies that consider the predictive modeling of CLV. In this study we focus on the prediction of CLV in

  13. The use of predicted values for item parameters in item response theory models: An application in intelligence tests

    NARCIS (Netherlands)

    Matteucci, M.; S. Mignani, Prof.; Veldkamp, Bernard P.

    2012-01-01

    In testing, item response theory models are widely used in order to estimate item parameters and individual abilities. However, even unidimensional models require a considerable sample size so that all parameters can be estimated precisely. The introduction of empirical prior information about

  14. Estimating and Predicting Metal Concentration Using Online Turbidity Values and Water Quality Models in Two Rivers of the Taihu Basin, Eastern China

    Science.gov (United States)

    Yao, Hong; Zhuang, Wei; Qian, Yu; Xia, Bisheng; Yang, Yang; Qian, Xin

    2016-01-01

    Turbidity (T) has been widely used to detect the occurrence of pollutants in surface water. Using data collected from January 2013 to June 2014 at eleven sites along two rivers feeding the Taihu Basin, China, the relationship between the concentration of five metals (aluminum (Al), titanium (Ti), nickel (Ni), vanadium (V), lead (Pb)) and turbidity was investigated. Metal concentration was determined using inductively coupled plasma mass spectrometry (ICP-MS). The linear regression of metal concentration and turbidity provided a good fit, with R2 = 0.86–0.93 for 72 data sets collected in the industrial river and R2 = 0.60–0.85 for 60 data sets collected in the cleaner river. All the regression presented good linear relationship, leading to the conclusion that the occurrence of the five metals are directly related to suspended solids, and these metal concentration could be approximated using these regression equations. Thus, the linear regression equations were applied to estimate the metal concentration using online turbidity data from January 1 to June 30 in 2014. In the prediction, the WASP 7.5.2 (Water Quality Analysis Simulation Program) model was introduced to interpret the transport and fates of total suspended solids; in addition, metal concentration downstream of the two rivers was predicted. All the relative errors between the estimated and measured metal concentration were within 30%, and those between the predicted and measured values were within 40%. The estimation and prediction process of metals’ concentration indicated that exploring the relationship between metals and turbidity values might be one effective technique for efficient estimation and prediction of metal concentration to facilitate better long-term monitoring with high temporal and spatial density. PMID:27028017

  15. Estimating and Predicting Metal Concentration Using Online Turbidity Values and Water Quality Models in Two Rivers of the Taihu Basin, Eastern China.

    Directory of Open Access Journals (Sweden)

    Hong Yao

    Full Text Available Turbidity (T has been widely used to detect the occurrence of pollutants in surface water. Using data collected from January 2013 to June 2014 at eleven sites along two rivers feeding the Taihu Basin, China, the relationship between the concentration of five metals (aluminum (Al, titanium (Ti, nickel (Ni, vanadium (V, lead (Pb and turbidity was investigated. Metal concentration was determined using inductively coupled plasma mass spectrometry (ICP-MS. The linear regression of metal concentration and turbidity provided a good fit, with R(2 = 0.86-0.93 for 72 data sets collected in the industrial river and R(2 = 0.60-0.85 for 60 data sets collected in the cleaner river. All the regression presented good linear relationship, leading to the conclusion that the occurrence of the five metals are directly related to suspended solids, and these metal concentration could be approximated using these regression equations. Thus, the linear regression equations were applied to estimate the metal concentration using online turbidity data from January 1 to June 30 in 2014. In the prediction, the WASP 7.5.2 (Water Quality Analysis Simulation Program model was introduced to interpret the transport and fates of total suspended solids; in addition, metal concentration downstream of the two rivers was predicted. All the relative errors between the estimated and measured metal concentration were within 30%, and those between the predicted and measured values were within 40%. The estimation and prediction process of metals' concentration indicated that exploring the relationship between metals and turbidity values might be one effective technique for efficient estimation and prediction of metal concentration to facilitate better long-term monitoring with high temporal and spatial density.

  16. Predicting media appeal from instinctive moral values

    NARCIS (Netherlands)

    Tamborini, R.; Eden, A.L.; Bowman, N.D.; Grizzard, M.; Weber, R.; Lewis, R.

    2013-01-01

    Zillmann's moral sanction theory defines morality subcultures for entertainment as groups of media viewers who evaluate character actions with shared value systems. However, the theory provides no a priori means to identify these shared value systems. The model of intuitive morality and exemplars

  17. Predicting Breeding Values in Animals by Kalman Filter

    DEFF Research Database (Denmark)

    Karacaoren, B; Janss, L L G; Kadarmideen, H N

    2012-01-01

    May 2004-March 2005 for 7 times approximately at monthly intervals from dairy cows (n=80) stationed at the Chamau research farm of Eidgenössische Technische Hochschule (ETH), Switzerland. Benefits of KF were demonstrated using random walk models via simulations. Breeding values were predicted over...... be useful in animal breeding industry for obtaining online estimation of breeding values over days in milk....

  18. Sound propagation in forests: A comparison of experimental results and values predicted by the Nord 2000 model

    DEFF Research Database (Denmark)

    Tarrero, A.I.; Martín, M.A.; González, J.

    2008-01-01

    The purpose of the work described in this paper is twofold: (i) to present the results of an experimental investigation of the sound attenuation in different types of forest, and (ii) to validate a part of the Nord 2000 model. A number of measurements have been carried out in regular and irregular...

  19. Drug delivery to solid tumors: the predictive value of the multicellular tumor spheroid model for nanomedicine screening

    Directory of Open Access Journals (Sweden)

    Millard M

    2017-10-01

    Full Text Available Marie Millard,1,2 Ilya Yakavets,1–3 Vladimir Zorin,3,4 Aigul Kulmukhamedova,1,2,5 Sophie Marchal,1,2 Lina Bezdetnaya1,2 1Centre de Recherche en Automatique de Nancy, Centre National de la Recherche Scientifique UMR 7039, Université de Lorraine, 2Research Department, Institut de Cancérologie de Lorraine, Vandœuvre-lès-Nancy, France; 3Laboratory of Biophysics and Biotechnology, 4International Sakharov Environmental Institute, Belarusian State University, Minsk, Belarus; 5Department of Radiology, Medical Company Sunkar, Almaty, Kazakhstan Abstract: The increasing number of publications on the subject shows that nanomedicine is an attractive field for investigations aiming to considerably improve anticancer chemotherapy. Based on selective tumor targeting while sparing healthy tissue, carrier-mediated drug delivery has been expected to provide significant benefits to patients. However, despite reduced systemic toxicity, most nanodrugs approved for clinical use have been less effective than previously anticipated. The gap between experimental results and clinical outcomes demonstrates the necessity to perform comprehensive drug screening by using powerful preclinical models. In this context, in vitro three-dimensional models can provide key information on drug behavior inside the tumor tissue. The multicellular tumor spheroid (MCTS model closely mimics a small avascular tumor with the presence of proliferative cells surrounding quiescent cells and a necrotic core. Oxygen, pH and nutrient gradients are similar to those of solid tumor. Furthermore, extracellular matrix (ECM components and stromal cells can be embedded in the most sophisticated spheroid design. All these elements together with the physicochemical properties of nanoparticles (NPs play a key role in drug transport, and therefore, the MCTS model is appropriate to assess the ability of NP to penetrate the tumor tissue. This review presents recent developments in MCTS models for a

  20. Simulation model estimates of test accuracy and predictive values for the Danish Salmonella surveillance program in dairy herds

    DEFF Research Database (Denmark)

    Warnick, L.D.; Nielsen, L.R.; Nielsen, Jens

    2006-01-01

    The Danish government and cattle industry instituted a Salmonella surveillance program in October 2002 to help reduce Salmonella enterica subsp. enterica serotype Dublin (S. Dublin) infections. All dairy herds are tested by measuring antibodies in bulk tank milk at 3-month intervals. The program...... is based on a well-established ELISA, but the overall test program accuracy and misclassification was not previously investigated. We developed a model to simulate repeated bulk tank milk antibody measurements for dairy herds conditional on true infection status. The distributions of bulk tank milk...

  1. Cultural Resource Predictive Modeling

    Science.gov (United States)

    2017-10-01

    refining formal, inductive predictive models is the quality of the archaeological and environmental data. To build models efficiently, relevant...geomorphology, and historic information . Lessons Learned: The original model was focused on the identification of prehistoric resources. This...system but uses predictive modeling informally . For example, there is no probability for buried archaeological deposits on the Burton Mesa, but there is

  2. Does Disagreement Amongst Forecasters have Predictive Value?

    NARCIS (Netherlands)

    R. Legerstee (Rianne); Ph.H.B.F. Franses (Philip Hans)

    2010-01-01

    textabstractForecasts from various experts are often used in macroeconomic forecasting models. Usually the focus is on the mean or median of the survey data. In the present study we adopt a different perspective on the survey data as we examine the predictive power of disagreement amongst

  3. Predictive modeling of complications.

    Science.gov (United States)

    Osorio, Joseph A; Scheer, Justin K; Ames, Christopher P

    2016-09-01

    Predictive analytic algorithms are designed to identify patterns in the data that allow for accurate predictions without the need for a hypothesis. Therefore, predictive modeling can provide detailed and patient-specific information that can be readily applied when discussing the risks of surgery with a patient. There are few studies using predictive modeling techniques in the adult spine surgery literature. These types of studies represent the beginning of the use of predictive analytics in spine surgery outcomes. We will discuss the advancements in the field of spine surgery with respect to predictive analytics, the controversies surrounding the technique, and the future directions.

  4. Incremental value of anemia in cardiac surgical risk prediction with the European System for Cardiac Operative Risk Evaluation (EuroSCORE) II model.

    Science.gov (United States)

    Scrascia, Giuseppe; Guida, Pietro; Caparrotti, Sergio Maria; Capone, Giuseppe; Contini, Marco; Cassese, Mauro; Fanelli, Vitantonio; Martinelli, Gianluca; Mazzei, Valerio; Zaccaria, Salvatore; Paparella, Domenico

    2014-09-01

    Anemia is a risk factor for adverse events after cardiac operations. We evaluated the incremental value of preoperative anemia over the European System for Cardiac Operative Risk Evaluation (EuroSCORE) II to predict hospital death after cardiac operations. Data for 4,594 consecutive adults (1,548 women [33.7%]), aged 67 ± 11 years, who underwent cardiac operations from January 2011 to July 2013 were extracted from the Regional Cardiac Surgery Registry of Puglia. The last preoperative hemoglobin value was used, according to World Health Organization criteria, to classify anemia as mild (hemoglobin 11.0 to 12.9 g/dL in men and 11.0 to 11.9 g/dL in women) in 1,021 patients (22.2%) and as moderate to severe (hemoglobin anemia, with model discrimination quantified by C statistic and risk classification by the use of net reclassification improvement (NRI). Overall expected and observed mortality rates were 4.4% and 5.9%. Anemia was significantly associated with a mortality rate of 3.4% in patients without anemia, 7.7% in mild anemia, and 15.7% in moderate to severe anemia (p anemia was analyzed with EuroSCORE II, the model improved in discrimination (C statistic = 0.852 vs 0.860; p = 0.007) and reclassification (category free-NRI, 0.592; p anemia has strong association with operative death in cardiac surgical patients. Anemia provides significant incremental value over the EuroSCORE II and should be considered for assessment of cardiac surgical risk. Copyright © 2014 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

  5. Archaeological predictive model set.

    Science.gov (United States)

    2015-03-01

    This report is the documentation for Task 7 of the Statewide Archaeological Predictive Model Set. The goal of this project is to : develop a set of statewide predictive models to assist the planning of transportation projects. PennDOT is developing t...

  6. The Economic Value of Predicting Bond Risk Premia

    DEFF Research Database (Denmark)

    Sarno, Lucio; Schneider, Paul; Wagner, Christian

    2016-01-01

    This paper studies whether the evident statistical predictability of bond risk premia translates into economic gains for investors. We propose a novel estimation strategy for affine term structure models that jointly fits yields and bond excess returns, thereby capturing predictive information...... otherwise hidden to standard estimations. The model predicts excess returns with high regression R2s and high forecast accuracy but cannot outperform the expectations hypothesis out-of-sample in terms of economic value, showing a general contrast between statistical and economic metrics of forecast...... evaluation. More specifically, the model mostly generates positive (negative) economic value during times of high (low) macroeconomic uncertainty. Overall, the expectations hypothesis remains a useful benchmark for investment decisions in bond markets, especially in low uncertainty states....

  7. Value Preferences Predicting Narcissistic Personality Traits in Young Adults

    Science.gov (United States)

    Gungor, Ibrahim Halil; Eksi, Halil; Aricak, Osman Tolga

    2012-01-01

    This study aimed at showing how the value preferences of young adults could predict the narcissistic characteristics of young adults according to structural equation modeling. 133 female (59.6%) and 90 male (40.4%), total 223 young adults participated the study (average age: 25.66, ranging from 20 to 38). Ratio group sampling method was used while…

  8. Wind power prediction models

    Science.gov (United States)

    Levy, R.; Mcginness, H.

    1976-01-01

    Investigations were performed to predict the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction was derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave desert. In addition to a model for power prediction over relatively long periods of time, an interim simulation model that produces sample wind speeds is described. The interim model furnishes uncorrelated sample speeds at hourly intervals that reproduce the statistical wind distribution at Goldstone. A stochastic simulation model to provide speed samples representative of both the statistical speed distributions and correlations is also discussed.

  9. Zephyr - the prediction models

    DEFF Research Database (Denmark)

    Nielsen, Torben Skov; Madsen, Henrik; Nielsen, Henrik Aalborg

    2001-01-01

    utilities as partners and users. The new models are evaluated for five wind farms in Denmark as well as one wind farm in Spain. It is shown that the predictions based on conditional parametric models are superior to the predictions obatined by state-of-the-art parametric models.......This paper briefly describes new models and methods for predicationg the wind power output from wind farms. The system is being developed in a project which has the research organization Risø and the department of Informatics and Mathematical Modelling (IMM) as the modelling team and all the Danish...

  10. A comparison of the predictive performance of three pharmacokinetic models for propofol using measured values obtained during target-controlled infusion

    NARCIS (Netherlands)

    Glen, J. B.; White, M.

    2014-01-01

    We compared the predictive performance of the existing Diprifusor and Schnider models, used for target-controlled infusion of propofol, with a new modification of the Diprifusor model (White) incorporating age and sex covariates. The bias and inaccuracy (precision) of each model were determined

  11. The Economic Value of Predicting Bond Risk Premia

    DEFF Research Database (Denmark)

    Sarno, Lucio; Schneider, Paul; Wagner, Christian

    the expectations hypothesis (EH) out-ofsample: the forecasts do not add economic value compared to using the average historical excess return as an EH-consistent estimate of constant risk premia. We show that in general statistical signicance does not necessarily translate into economic signicance because EH...... deviations mainly matter at short horizons and standard predictability metrics are not compatible with common measures of economic value. Overall, the EH remains the benchmark for investment decisions and should be considered an economic prior in models of bond risk premia.......This paper studies whether the evident statistical predictability of bond risk premia translates into economic gains for bond investors. We show that ane term structure models (ATSMs) estimated by jointly tting yields and bond excess returns capture this predictive information otherwise hidden...

  12. Inverse and Predictive Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Syracuse, Ellen Marie [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-09-27

    The LANL Seismo-Acoustic team has a strong capability in developing data-driven models that accurately predict a variety of observations. These models range from the simple – one-dimensional models that are constrained by a single dataset and can be used for quick and efficient predictions – to the complex – multidimensional models that are constrained by several types of data and result in more accurate predictions. Team members typically build models of geophysical characteristics of Earth and source distributions at scales of 1 to 1000s of km, the techniques used are applicable for other types of physical characteristics at an even greater range of scales. The following cases provide a snapshot of some of the modeling work done by the Seismo- Acoustic team at LANL.

  13. New Trends, News Values, and New Models.

    Science.gov (United States)

    Higgins, Mary Anne

    1996-01-01

    Explores implications of the prediction that in the next millennium the public will experience a scarcity of knowledge and a surplus of information. Reviews research suggesting that journalists focus on these news values: emphasizing how/why, devaluing immediacy, specializing/analyzing, representing a constituency. Examines two new models of…

  14. Genomic Prediction of Genetic Values for Resistance to Wheat Rusts

    Directory of Open Access Journals (Sweden)

    Leonardo Ornella

    2012-11-01

    Full Text Available Durable resistance to the rust diseases of wheat ( L. can be achieved by developing lines that have race-nonspecific adult plant resistance conferred by multiple minor slow-rusting genes. Genomic selection (GS is a promising tool for accumulating favorable alleles of slow-rusting genes. In this study, five CIMMYT wheat populations evaluated for resistance were used to predict resistance to stem rust ( and yellow rust ( using Bayesian least absolute shrinkage and selection operator (LASSO (BL, ridge regression (RR, and support vector regression with linear or radial basis function kernel models. All parents and populations were genotyped using 1400 Diversity Arrays Technology markers and different prediction problems were assessed. Results show that prediction ability for yellow rust was lower than for stem rust, probably due to differences in the conditions of infection of both diseases. For within population and environment, the correlation between predicted and observed values (Pearson’s correlation [ρ] was greater than 0.50 in 90% of the evaluations whereas for yellow rust, ρ ranged from 0.0637 to 0.6253. The BL and RR models have similar prediction ability, with a slight superiority of the BL confirming reports about the additive nature of rust resistance. When making predictions between environments and/or between populations, including information from another environment or environments or another population or populations improved prediction.

  15. Predicting community sensitivity to ozone, using Ellenberg Indicator values

    Energy Technology Data Exchange (ETDEWEB)

    Jones, M. Laurence M. [Centre for Ecology and Hydrology Bangor, Orton Building, Deiniol Road, Bangor, Gwynedd LL57 2UP (United Kingdom)]. E-mail: lj@ceh.ac.uk; Hayes, Felicity [Centre for Ecology and Hydrology Bangor, Orton Building, Deiniol Road, Bangor, Gwynedd LL57 2UP (United Kingdom)]. E-mail: fhay@ceh.ac.uk; Mills, Gina [Centre for Ecology and Hydrology Bangor, Orton Building, Deiniol Road, Bangor, Gwynedd LL57 2UP (United Kingdom)]. E-mail: gmi@ceh.ac.uk; Sparks, Tim H. [Centre for Ecology and Hydrology Monks Wood, Abbots Ripton, Huntingdon, Cambridgeshire PE28 2LS (United Kingdom)]. E-mail: ths@ceh.ac.uk; Fuhrer, Juerg [Swiss Federal Research Station for Agroecology and Agriculture (FAL), Air Pollution/Climate Group, Reckenholzstrasse 191, CH-8046 Zurich (Switzerland)]. E-mail: juerg.fuhrer@fal.admin.ch

    2007-04-15

    This paper develops a regression-based model for predicting changes in biomass of individual species exposed to ozone (RS{sub p}), based on their Ellenberg Indicator values. The equation (RS{sub p}=1.805-0.118Light-0.135Salinity) underpredicts observed sensitivity but has the advantage of widespread applicability to almost 3000 European species. The model was applied to grassland communities to develop two further predictive tools. The first tool, percentage change in biomass (ORI%) was tested on data from a field-based ozone exposure experiment and predicted a 27% decrease in biomass over 5 years compared with an observed decrease of 23%. The second tool, an index of community sensitivity to ozone (CORI), was applied to 48 grassland communities and suggests that community sensitivity to ozone is primarily species-driven. A repeat-sampling routine showed that nine species were the minimum requirement to estimate CORI within 5%.

  16. Examining predictive relationships among consumer values: factors ...

    African Journals Online (AJOL)

    The outcome of analysis indicates that values of stimulation and hedonistic, domain specific, evaluative belief, and restrictive conformity values showed collectively to have positive relationship with retail behavioural intentions. Individually, however, only stimulation and hedonistic values had positive relationship with ...

  17. The relative value of operon predictions

    NARCIS (Netherlands)

    Brouwer, Rutger W. W.; Kuipers, Oscar P.; van Hijum, Sacha A. F. T.

    For most organisms, computational operon predictions are the only source of genome-wide operon information. Operon prediction methods described in literature are based on (a combination of) the following five criteria: (i) intergenic distance, (ii) conserved gene clusters, (iii) functional relation,

  18. The value of multivariate model sophistication

    DEFF Research Database (Denmark)

    Rombouts, Jeroen; Stentoft, Lars; Violante, Francesco

    2014-01-01

    We assess the predictive accuracies of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set of 444 multivariate models that differ in their spec......We assess the predictive accuracies of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set of 444 multivariate models that differ...... in their specification of the conditional variance, conditional correlation, innovation distribution, and estimation approach. All of the models belong to the dynamic conditional correlation class, which is particularly suitable because it allows consistent estimations of the risk neutral dynamics with a manageable....... In addition to investigating the value of model sophistication in terms of dollar losses directly, we also use the model confidence set approach to statistically infer the set of models that delivers the best pricing performances....

  19. Reliability and Minimal Detectable Change Values for Predictions of Knee Forces during Gait and Stair Ascent Derived from the FreeBody Musculoskeletal Model of the Lower Limb

    Directory of Open Access Journals (Sweden)

    Phil D. B. Price

    2017-12-01

    Full Text Available FreeBody is a musculoskeletal model of the lower limb used to calculate predictions of muscle and joint contact forces. The validation of FreeBody has been described in a number of publications; however, its reliability has yet to be established. The purpose of this study was, therefore, to establish the test–retest reliability of FreeBody in a population of healthy adults in order to add support to previous and future research using FreeBody that demonstrates differences between cohorts after an intervention. We hypothesized that test–retest estimations of knee contact forces from FreeBody would demonstrate a high intra-class correlation. Kinematic and kinetic data from nine older participants (4 men: mean age = 63 ± 11 years; 5 women: mean age = 49 ± 4 years performing level walking and stair ascent was collected on consecutive days and then analyzed using FreeBody. There was a good level of intra-session agreement between the waveforms for the individual trials of each activity during testing session 1 (R = 0.79–0.97. Similarly, overall there was a good inter-session agreement within subjects (R = 0.69–0.97 although some subjects showed better agreement than others. There was a high level of agreement between the group mean waveforms of the two sessions for all variables (R = 0.882–0.997. The intra-class correlation coefficients (ICC were very high for peak tibiofemoral joint contact forces (TFJ and hamstring forces during gait, for peak patellofemoral joint contact forces and quadriceps forces during stair ascent and for peak lateral TFJ and the proportion of TFJ accounted for by the medial compartment during both tasks (ICC = 0.86–0.96. Minimal detectable change (MDC of the peak knee forces during gait ranged between 0.43 and 1.53 × body weight (18–170% of the mean peak values. The smallest MDCs were found for medial TFJ share (4.1 and 5.8% for walking and stair ascent, respectively

  20. Mathematical modeling of CA125 kinetics in recurrent ovarian cancer (ROC) patients treated with chemotherapy and predictive value of early modeled kinetic parameters in CALYPSO trial: A GCIG study

    DEFF Research Database (Denmark)

    Dahl Steffensen, Karina

    2011-01-01

    Background: Although CA125 kinetic profiles may be related with relapse risk in ovarian cancer patients treated with chemotherapy, no reliable kinetic parameters have been reported. Mathematical modeling may help describe CA125 decline dynamically and determine parameters predictive of relapse....... Methods: Data from CALYPSO phase III trial data comparing 2 carboplatin-based regimens in ROC patients were analyzed. Based on population kinetic approach (Monolix software), a semi-mechanistic model was used to fit serum log (CA125) concentration-time profiles with following parameters: tumor growth rate...... constant (BETA); CA 125 tumor production (KIN); tumor decay rate constant (KOUT) and treatment indirect effect (Emax relationships with A and A50) “d[CA125]/dt=(KIN* exp [BETA*t]) * (1 - [A/{A+A50}]) – KOUT * (CA125)” where t is time. The predictive values of KIN; KOUT; BETA and A50 estimated during...

  1. Neural networks for predicting breeding values and genetic gains

    Directory of Open Access Journals (Sweden)

    Gabi Nunes Silva

    2014-12-01

    Full Text Available Analysis using Artificial Neural Networks has been described as an approach in the decision-making process that, although incipient, has been reported as presenting high potential for use in animal and plant breeding. In this study, we introduce the procedure of using the expanded data set for training the network. Wealso proposed using statistical parameters to estimate the breeding value of genotypes in simulated scenarios, in addition to the mean phenotypic value in a feed-forward back propagation multilayer perceptron network. After evaluating artificial neural network configurations, our results showed its superiority to estimates based on linear models, as well as its applicability in the genetic value prediction process. The results further indicated the good generalization performance of the neural network model in several additional validation experiments.

  2. Multifractal Value at Risk model

    Science.gov (United States)

    Lee, Hojin; Song, Jae Wook; Chang, Woojin

    2016-06-01

    In this paper new Value at Risk (VaR) model is proposed and investigated. We consider the multifractal property of financial time series and develop a multifractal Value at Risk (MFVaR). MFVaR introduced in this paper is analytically tractable and not based on simulation. Empirical study showed that MFVaR can provide the more stable and accurate forecasting performance in volatile financial markets where large loss can be incurred. This implies that our multifractal VaR works well for the risk measurement of extreme credit events.

  3. Markovian prediction of future values for food grains in the economic survey

    Science.gov (United States)

    Sathish, S.; Khadar Babu, S. K.

    2017-11-01

    Now-a-days prediction and forecasting are plays a vital role in research. For prediction, regression is useful to predict the future value and current value on production process. In this paper, we assume food grain production exhibit Markov chain dependency and time homogeneity. The economic generative performance evaluation the balance time artificial fertilization different level in Estrusdetection using a daily Markov chain model. Finally, Markov process prediction gives better performance compare with Regression model.

  4. One-Step Dynamic Classifier Ensemble Model for Customer Value Segmentation with Missing Values

    OpenAIRE

    Jin Xiao; Bing Zhu; Geer Teng; Changzheng He; Dunhu Liu

    2014-01-01

    Scientific customer value segmentation (CVS) is the base of efficient customer relationship management, and customer credit scoring, fraud detection, and churn prediction all belong to CVS. In real CVS, the customer data usually include lots of missing values, which may affect the performance of CVS model greatly. This study proposes a one-step dynamic classifier ensemble model for missing values (ODCEM) model. On the one hand, ODCEM integrates the preprocess of missing values and the classif...

  5. Predictive Surface Complexation Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Sverjensky, Dimitri A. [Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Earth and Planetary Sciences

    2016-11-29

    Surface complexation plays an important role in the equilibria and kinetics of processes controlling the compositions of soilwaters and groundwaters, the fate of contaminants in groundwaters, and the subsurface storage of CO2 and nuclear waste. Over the last several decades, many dozens of individual experimental studies have addressed aspects of surface complexation that have contributed to an increased understanding of its role in natural systems. However, there has been no previous attempt to develop a model of surface complexation that can be used to link all the experimental studies in order to place them on a predictive basis. Overall, my research has successfully integrated the results of the work of many experimentalists published over several decades. For the first time in studies of the geochemistry of the mineral-water interface, a practical predictive capability for modeling has become available. The predictive correlations developed in my research now enable extrapolations of experimental studies to provide estimates of surface chemistry for systems not yet studied experimentally and for natural and anthropogenically perturbed systems.

  6. Suboptimal Choice in Pigeons: Stimulus Value Predicts Choice over Frequencies.

    Directory of Open Access Journals (Sweden)

    Aaron P Smith

    Full Text Available Pigeons have shown suboptimal gambling-like behavior when preferring a stimulus that infrequently signals reliable reinforcement over alternatives that provide greater reinforcement overall. As a mechanism for this behavior, recent research proposed that the stimulus value of alternatives with more reliable signals for reinforcement will be preferred relatively independently of their frequencies. The present study tested this hypothesis using a simplified design of a Discriminative alternative that, 50% of the time, led to either a signal for 100% reinforcement or a blackout period indicative of 0% reinforcement against a Nondiscriminative alternative that always led to a signal that predicted 50% reinforcement. Pigeons showed a strong preference for the Discriminative alternative that remained despite reducing the frequency of the signal for reinforcement in subsequent phases to 25% and then 12.5%. In Experiment 2, using the original design of Experiment 1, the stimulus following choice of the Nondiscriminative alternative was increased to 75% and then to 100%. Results showed that preference for the Discriminative alternative decreased only when the signals for reinforcement for the two alternatives predicted the same probability of reinforcement. The ability of several models to predict this behavior are discussed, but the terminal link stimulus value offers the most parsimonious account of this suboptimal behavior.

  7. Macrophage inflammatory protein-1α shows predictive value as a risk marker for subjects and sites vulnerable to bone loss in a longitudinal model of aggressive periodontitis.

    Directory of Open Access Journals (Sweden)

    Daniel H Fine

    Full Text Available Improved diagnostics remains a fundamental goal of biomedical research. This study was designed to assess cytokine biomarkers that could predict bone loss (BL in localized aggressive periodontitis. 2,058 adolescents were screened. Two groups of 50 periodontally healthy adolescents were enrolled in the longitudinal study. One group had Aggregatibacter actinomycetemcomitans (Aa, the putative pathogen, while the matched cohort did not. Cytokine levels were assessed in saliva and gingival crevicular fluid (GCF. Participants were sampled, examined, and radiographed every 6 months for 2-3 years. Disease was defined as radiographic evidence of BL. Saliva and GCF was collected at each visit, frozen, and then tested retrospectively after detection of BL. Sixteen subjects with Aa developed BL. Saliva from Aa-positive and Aa-negative healthy subjects was compared to subjects who developed BL. GCF was collected from 16 subjects with BL and from another 38 subjects who remained healthy. GCF from BL sites in the 16 subjects was compared to healthy sites in these same subjects and to healthy sites in subjects who remained healthy. Results showed that cytokines in saliva associated with acute inflammation were elevated in subjects who developed BL (i.e., MIP-1α MIP-1β IL-α, IL-1β and IL-8; p<0.01. MIP-1α was elevated 13-fold, 6 months prior to BL. When MIP-1α levels were set at 40 pg/ml, 98% of healthy sites were below that level (Specificity; whereas, 93% of sites with BL were higher (Sensitivity, with comparable Predictive Values of 98%; p<0.0001; 95% C.I. = 42.5-52.7. MIP-1α consistently showed elevated levels as a biomarker for BL in both saliva and GCF, 6 months prior to BL. MIP-1α continues to demonstrate its strong candidacy as a diagnostic biomarker for both subject and site vulnerability to BL.

  8. Candidate Prediction Models and Methods

    DEFF Research Database (Denmark)

    Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik

    2005-01-01

    This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...

  9. Melanoma risk prediction models

    Directory of Open Access Journals (Sweden)

    Nikolić Jelena

    2014-01-01

    Full Text Available Background/Aim. The lack of effective therapy for advanced stages of melanoma emphasizes the importance of preventive measures and screenings of population at risk. Identifying individuals at high risk should allow targeted screenings and follow-up involving those who would benefit most. The aim of this study was to identify most significant factors for melanoma prediction in our population and to create prognostic models for identification and differentiation of individuals at risk. Methods. This case-control study included 697 participants (341 patients and 356 controls that underwent extensive interview and skin examination in order to check risk factors for melanoma. Pairwise univariate statistical comparison was used for the coarse selection of the most significant risk factors. These factors were fed into logistic regression (LR and alternating decision trees (ADT prognostic models that were assessed for their usefulness in identification of patients at risk to develop melanoma. Validation of the LR model was done by Hosmer and Lemeshow test, whereas the ADT was validated by 10-fold cross-validation. The achieved sensitivity, specificity, accuracy and AUC for both models were calculated. The melanoma risk score (MRS based on the outcome of the LR model was presented. Results. The LR model showed that the following risk factors were associated with melanoma: sunbeds (OR = 4.018; 95% CI 1.724- 9.366 for those that sometimes used sunbeds, solar damage of the skin (OR = 8.274; 95% CI 2.661-25.730 for those with severe solar damage, hair color (OR = 3.222; 95% CI 1.984-5.231 for light brown/blond hair, the number of common naevi (over 100 naevi had OR = 3.57; 95% CI 1.427-8.931, the number of dysplastic naevi (from 1 to 10 dysplastic naevi OR was 2.672; 95% CI 1.572-4.540; for more than 10 naevi OR was 6.487; 95%; CI 1.993-21.119, Fitzpatricks phototype and the presence of congenital naevi. Red hair, phototype I and large congenital naevi were

  10. Posterior predictive checking of multiple imputation models.

    Science.gov (United States)

    Nguyen, Cattram D; Lee, Katherine J; Carlin, John B

    2015-07-01

    Multiple imputation is gaining popularity as a strategy for handling missing data, but there is a scarcity of tools for checking imputation models, a critical step in model fitting. Posterior predictive checking (PPC) has been recommended as an imputation diagnostic. PPC involves simulating "replicated" data from the posterior predictive distribution of the model under scrutiny. Model fit is assessed by examining whether the analysis from the observed data appears typical of results obtained from the replicates produced by the model. A proposed diagnostic measure is the posterior predictive "p-value", an extreme value of which (i.e., a value close to 0 or 1) suggests a misfit between the model and the data. The aim of this study was to evaluate the performance of the posterior predictive p-value as an imputation diagnostic. Using simulation methods, we deliberately misspecified imputation models to determine whether posterior predictive p-values were effective in identifying these problems. When estimating the regression parameter of interest, we found that more extreme p-values were associated with poorer imputation model performance, although the results highlighted that traditional thresholds for classical p-values do not apply in this context. A shortcoming of the PPC method was its reduced ability to detect misspecified models with increasing amounts of missing data. Despite the limitations of posterior predictive p-values, they appear to have a valuable place in the imputer's toolkit. In addition to automated checking using p-values, we recommend imputers perform graphical checks and examine other summaries of the test quantity distribution. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  11. Norms and values in sociohydrological models

    Science.gov (United States)

    Roobavannan, Mahendran; van Emmerik, Tim H. M.; Elshafei, Yasmina; Kandasamy, Jaya; Sanderson, Matthew R.; Vigneswaran, Saravanamuthu; Pande, Saket; Sivapalan, Murugesu

    2018-02-01

    Sustainable water resources management relies on understanding how societies and water systems coevolve. Many place-based sociohydrology (SH) modeling studies use proxies, such as environmental degradation, to capture key elements of the social component of system dynamics. Parameters of assumed relationships between environmental degradation and the human response to it are usually obtained through calibration. Since these relationships are not yet underpinned by social-science theories, confidence in the predictive power of such place-based sociohydrologic models remains low. The generalizability of SH models therefore requires major advances in incorporating more realistic relationships, underpinned by appropriate hydrological and social-science data and theories. The latter is a critical input, since human culture - especially values and norms arising from it - influences behavior and the consequences of behaviors. This paper reviews a key social-science theory that links cultural factors to environmental decision-making, assesses how to better incorporate social-science insights to enhance SH models, and raises important questions to be addressed in moving forward. This is done in the context of recent progress in sociohydrological studies and the gaps that remain to be filled. The paper concludes with a discussion of challenges and opportunities in terms of generalization of SH models and the use of available data to allow future prediction and model transfer to ungauged basins.

  12. Norms and values in sociohydrological models

    Directory of Open Access Journals (Sweden)

    M. Roobavannan

    2018-02-01

    Full Text Available Sustainable water resources management relies on understanding how societies and water systems coevolve. Many place-based sociohydrology (SH modeling studies use proxies, such as environmental degradation, to capture key elements of the social component of system dynamics. Parameters of assumed relationships between environmental degradation and the human response to it are usually obtained through calibration. Since these relationships are not yet underpinned by social-science theories, confidence in the predictive power of such place-based sociohydrologic models remains low. The generalizability of SH models therefore requires major advances in incorporating more realistic relationships, underpinned by appropriate hydrological and social-science data and theories. The latter is a critical input, since human culture – especially values and norms arising from it – influences behavior and the consequences of behaviors. This paper reviews a key social-science theory that links cultural factors to environmental decision-making, assesses how to better incorporate social-science insights to enhance SH models, and raises important questions to be addressed in moving forward. This is done in the context of recent progress in sociohydrological studies and the gaps that remain to be filled. The paper concludes with a discussion of challenges and opportunities in terms of generalization of SH models and the use of available data to allow future prediction and model transfer to ungauged basins.

  13. [Predictive value of Hodgkin's lymphoma tumor burden in present].

    Science.gov (United States)

    Kulyova, S A; Karitsky, A P

    2014-01-01

    Today approximately 70% of patients with Hodgkin lymphoma can be cured with the combined-modality therapy. Tumor burden, the importance of which was demonstrated 15 years ago for the first time, is a powerful prognostic factor. Data of literature of representations on predictive value of Hodgkin's lymphoma tumor burden are shown in the article. The difficult immunological relations between tumor cells and reactive ones lead to development of the main symptoms. Nevertheless, the collective sign of tumor burden shows the greatest influence on survival and on probability of resistance, which relative risk can be predicted on this variable and treatment program. Patients with bulky disease need escalated therapy with high-dose chemotherapy. Integration into predictive models of the variable will change an expected contribution of clinical and laboratory parameters in the regression analyses constructed on patients with Hodgkin's lymphoma. Today the role of diagnostic functional methods, in particular a positron emission tomography, for metabolic active measurement is conducted which allows excluding a reactive component.

  14. Predictive value of prostate-specific antigen for prostate cancer

    DEFF Research Database (Denmark)

    Shepherd, Leah; Borges, Alvaro Humberto; Ravn, Lene

    2014-01-01

    INTRODUCTION: Although prostate cancer (PCa) incidence is lower in HIV+ men than in HIV- men, the usefulness of prostate-specific antigen (PSA) screening in this population is not well defined and may have higher false negative rates than in HIV- men. We aimed to describe the kinetics and predict......INTRODUCTION: Although prostate cancer (PCa) incidence is lower in HIV+ men than in HIV- men, the usefulness of prostate-specific antigen (PSA) screening in this population is not well defined and may have higher false negative rates than in HIV- men. We aimed to describe the kinetics...... and predictive value of PSA in HIV+ men. METHODS: Men with PCa (n=21) and up to two matched controls (n=40) with prospectively stored plasma samples before PCa (or matched date in controls) were selected. Cases and controls were matched on date of first and last sample, age, region of residence and CD4 count...... at first sample date. Total PSA (tPSA), free PSA (fPSA), testosterone and sex hormone binding globulin (SHBG) were measured. Conditional logistic regression models investigated associations between markers and PCa. Sensitivity and specificity of using tPSA >4 µg/L to predict PCa was calculated. Mixed...

  15. Achieving Value in Primary Care: The Primary Care Value Model.

    Science.gov (United States)

    Rollow, William; Cucchiara, Peter

    2016-03-01

    The patient-centered medical home (PCMH) model provides a compelling vision for primary care transformation, but studies of its impact have used insufficiently patient-centered metrics with inconsistent results. We propose a framework for defining patient-centered value and a new model for value-based primary care transformation: the primary care value model (PCVM). We advocate for use of patient-centered value when measuring the impact of primary care transformation, recognition, and performance-based payment; for financial support and research and development to better define primary care value-creating activities and their implementation; and for use of the model to support primary care organizations in transformation. © 2016 Annals of Family Medicine, Inc.

  16. Predictive value of EEG in neonates with periventricular leukomalacia.

    NARCIS (Netherlands)

    Vermeulen, R.; Sie, L.T.L.; Jonkman, E.J.; Strijers, R.L.; Lafeber, H.N.; Uitdehaag, B.M.J.; Knaap, M.S. van der

    2003-01-01

    The aim of this study was to evaluate whether EEG (i.e. positive Rolandic sharp waves) can be used to predict neurodevelopment in newborn infants with periventricular leukomalacia and compare the predictive value with that of MRI. A sequential cohort of neonates (n=45; 33 males, 12 females; mean

  17. One-Step Dynamic Classifier Ensemble Model for Customer Value Segmentation with Missing Values

    Directory of Open Access Journals (Sweden)

    Jin Xiao

    2014-01-01

    Full Text Available Scientific customer value segmentation (CVS is the base of efficient customer relationship management, and customer credit scoring, fraud detection, and churn prediction all belong to CVS. In real CVS, the customer data usually include lots of missing values, which may affect the performance of CVS model greatly. This study proposes a one-step dynamic classifier ensemble model for missing values (ODCEM model. On the one hand, ODCEM integrates the preprocess of missing values and the classification modeling into one step; on the other hand, it utilizes multiple classifiers ensemble technology in constructing the classification models. The empirical results in credit scoring dataset “German” from UCI and the real customer churn prediction dataset “China churn” show that the ODCEM outperforms four commonly used “two-step” models and the ensemble based model LMF and can provide better decision support for market managers.

  18. Confidence scores for prediction models

    DEFF Research Database (Denmark)

    Gerds, Thomas Alexander; van de Wiel, MA

    2011-01-01

    In medical statistics, many alternative strategies are available for building a prediction model based on training data. Prediction models are routinely compared by means of their prediction performance in independent validation data. If only one data set is available for training and validation......, then rival strategies can still be compared based on repeated bootstraps of the same data. Often, however, the overall performance of rival strategies is similar and it is thus difficult to decide for one model. Here, we investigate the variability of the prediction models that results when the same...... to distinguish rival prediction models with similar prediction performances. Furthermore, on the subject level a confidence score may provide useful supplementary information for new patients who want to base a medical decision on predicted risk. The ideas are illustrated and discussed using data from cancer...

  19. A predictive model for dimensional errors in fused deposition modeling

    DEFF Research Database (Denmark)

    Stolfi, A.

    2015-01-01

    values of L (0.254 mm, 0.330 mm) was produced by comparing predicted values with external face-to-face measurements. After removing outliers, the results show that the developed two-parameter model can serve as tool for modeling the FDM dimensional behavior in a wide range of deposition angles....

  20. Prediction of 3-hydroxypyridin-4-one (HPO) log K1 values for Fe(III).

    Science.gov (United States)

    Chen, Yu-Lin; Barlow, Dave J; Kong, Xiao-Le; Ma, Yong-Min; Hider, Robert C

    2012-09-21

    As a means to aid in the design of 3-hydroxypyridin-4-ones (HPOs) intended for use as therapeutic Fe(3+) chelating agents, a novel methodology has been developed using quantum mechanical (QM) calculations for predicting the iron binding affinities of the compounds (more specifically, their log K(1) values). The reported/measured HPO log K(1) values were verified through their correlation with the corresponding sum of the compounds' ligating group pK(a) values. Using a training set of eleven HPOs with known log K(1) values, reliable predictions are shown to be obtained with QM calculations using the B3LYP/6-31+G(d)/CPCM model chemistry (with Bondi radii, and water as solvent). With this methodology, the observed log K(1) values for the training set compounds are closely matched by the predicted values, with the correlation between the observed and predicted values giving r(2) = 0.9. Predictions subsequently made by this method for a test set of 42 HPOs of known log K(1) values gave predicted values accurate to within ±0.32 log units. In order to further investigate the predictive power of the method, four novel HPOs were synthesised and their log K(1) values were determined experimentally. Comparison of these predicted log K(1) values against the measured values gave absolute deviations of 0.22 (13.87 vs. 14.09), 0.02 (14.31 vs. 14.29), 0.12 (14.62 vs. 14.50), and 0.13 (15.04 vs. 15.17). The prediction methodology reported here is the first to be provided for predicting the absolute log K(1) values of iron-chelating agents in the absence of pK(a) values.

  1. Predicting Customer Potential Value: an application in the insurance industry

    NARCIS (Netherlands)

    P.C. Verhoef (Peter); A.C.D. Donkers (Bas)

    2001-01-01

    textabstractFor effective Customer Relationship Management (CRM), it is essential to have information on the potential value of customers. Based on the interplay between potential value and realized value, managers can devise customer specific strategies. In this article we introduce a model for

  2. Land Use/Land Cover Change Modeling and the Prediction of Subsequent Changes in Ecosystem Service Values in a Coastal Area of China, the Su-Xi-Chang Region

    Directory of Open Access Journals (Sweden)

    Eshetu Yirsaw

    2017-07-01

    Full Text Available Monitoring the impact of current Land Use/Land Cover (LULC management practices on future Ecosystem Services (ESs provisioning has been emphasized because of the effect of such practices on ecological sustainability. We sought to model and predict the impacts of future LULC changes on subsequent changes in Ecosystem Service Value (ESV in fragile environments undergoing complex LULC changes, Su-Xi-Chang region. After mapping and classifying the LULC for the years 1990, 2000, and 2010 using GIS and remote sensing, a Cellular Automata (CA–Markov model was employed to model future LULC changes for the year 2020. ESV was predicted using the projected LULC data and the modified ES coefficients adopted by Xie et al. (2003. The projected results of the changes in LULC reveal that construction land expanded extensively, mainly at the expense of farmland, wetland, and water bodies. The predicted results of the ESVs indicate that water bodies and farmland are the dominant LULC categories, accounting for 90% of the total ESV. Over the study period, ESVs were diminished by 7.3915 billion CNY, mostly because of the decrease in farmland, water bodies, and wetland. A reasonable land use plan should be developed with an emphasis on controlling construction land encroachment on farmland, wetlands, and water bodies. The rules of ecological protection should be followed in LULC management to preserve ecological resources.

  3. Diagnostic value of newborn foot length to predict gestational age

    Directory of Open Access Journals (Sweden)

    Mutia Farah Fawziah

    2017-08-01

    Full Text Available Background  Identification of gestational age, especially within 48 hours of birth, is crucial for newborns, as the earlier preterm status is detected, the earlier the child can receive optimal management. Newborn foot length is an anthropometric measurement which is easy to perform, inexpensive, and potentially efficient for predicting gestational age. Objective  To analyze the diagnostic value of newborn foot length in predicting gestational age. Methods  This diagnostic study was performed between October 2016 and February 2017 in the High Care Unit of Neonates at Dr. Moewardi General Hospital, Surakarta. A total of 152 newborns were consecutively selected and underwent right foot length measurements before 96 hours of age. The correlation between newborn foot length to classify as full term and gestational age was analyzed with Spearman’s correlation test because of non-normal data distribution. The cut-off point of newborn foot length was calculated by receiver operating characteristic (ROC curve and diagnostic values of newborn foot length were analyzed by 2 x 2 table with SPSS 21.0 software. Results There were no significant differences between male and female newborns in terms of gestational age, birth weight, choronological age, and newborn foot length (P>0.05. Newborn foot length and gestational age had a significant correlation (r=0.53; P=0.000. The optimal cut-off newborn foot length to predict full term status was 7.1 cm. Newborn foot length below 7.1 cm had sensitivity 75%, specificity 98%, positive predictive value 94.3%, negative predictive value 90.6%, positive likelihood ratio 40.5, negative likelihood ratio 0.25, and post-test probability 94.29%, to predict preterm status in newborns. Conclusion  Newborn foot length can be used to predict gestational age, especially for the purpose of differentiating between preterm and full term newborns.

  4. Bootstrap prediction and Bayesian prediction under misspecified models

    OpenAIRE

    Fushiki, Tadayoshi

    2005-01-01

    We consider a statistical prediction problem under misspecified models. In a sense, Bayesian prediction is an optimal prediction method when an assumed model is true. Bootstrap prediction is obtained by applying Breiman's `bagging' method to a plug-in prediction. Bootstrap prediction can be considered to be an approximation to the Bayesian prediction under the assumption that the model is true. However, in applications, there are frequently deviations from the assumed model. In this paper, bo...

  5. Corrections to the free-nucleon values of the single-particle matrix elements of the M1 and Gamow-Teller operators, from a comparison of shell-model predictions with sd-shell data

    International Nuclear Information System (INIS)

    Brown, B.A.; Wildenthal, B.H.

    1983-01-01

    The magnetic dipole moments of states in mirror pairs of the sd-shell nuclei and the strengths of the Gamow-Teller beta decays which connect them are compared with predictions based on mixed-configuration shell-model wave functions. From this analysis we extract the average effective values of the single-particle matrix elements of the l, s, and [Y/sup( 2 )xs]/sup( 1 ) components of the M1 and Gamow-Teller operators acting on nucleons in the 0d/sub 5/2/, 1s/sub 1/2/, and 0d/sub 3/2/ orbits. These results are compared with the recent calculations by Towner and Khanna of the corrections to the free-nucleon values of these matrix elements which arise from the effects of isobar currents, mesonic-exchange currents, and mixing with configurations outside the sd shell

  6. Mean Value Modelling of Turbocharged SI Engines

    DEFF Research Database (Denmark)

    Müller, Martin; Hendricks, Elbert; Sorenson, Spencer C.

    1998-01-01

    The development of a computer simulation to predict the performance of a turbocharged spark ignition engine during transient operation. New models have been developed for the turbocharged and the intercooling system. An adiabatic model for the intake manifold is presented.......The development of a computer simulation to predict the performance of a turbocharged spark ignition engine during transient operation. New models have been developed for the turbocharged and the intercooling system. An adiabatic model for the intake manifold is presented....

  7. Prediction models in complex terrain

    DEFF Research Database (Denmark)

    Marti, I.; Nielsen, Torben Skov; Madsen, Henrik

    2001-01-01

    The objective of the work is to investigatethe performance of HIRLAM in complex terrain when used as input to energy production forecasting models, and to develop a statistical model to adapt HIRLAM prediction to the wind farm. The features of the terrain, specially the topography, influence...... the performance of HIRLAM in particular with respect to wind predictions. To estimate the performance of the model two spatial resolutions (0,5 Deg. and 0.2 Deg.) and different sets of HIRLAM variables were used to predict wind speed and energy production. The predictions of energy production for the wind farms...... are calculated using on-line measurements of power production as well as HIRLAM predictions as input thus taking advantage of the auto-correlation, which is present in the power production for shorter pediction horizons. Statistical models are used to discribe the relationship between observed energy production...

  8. Predicting quantitative and qualitative values of recreation participation

    Science.gov (United States)

    Elwood L., Jr. Shafer; George Moeller

    1971-01-01

    If future recreation consumption and associated intangible values can be predicted, the problem of rapid decision making in recreation-resource management can be reduced, and the problems of implementing those decisions can be anticipated. Management and research responsibilities for meeting recreation demand are discussed, and proved methods for forecasting recreation...

  9. A comparison of the yield, nutritional value and predicted production ...

    African Journals Online (AJOL)

    The yield, nutritional value and production potential of silage made from twenty one maize hybrids was compared. The digestibility of organic matter and predicted intake, mean retention time and milk production potential were found to differ between hybrids (p < 0.05). Acid detergent fibre content could not be used to ...

  10. High drain amylase and lipase values predict post-operative ...

    African Journals Online (AJOL)

    High drain amylase and lipase values predict post-operative pancreatitis for choledochal cyst. S Honda, T Okada, H Miyagi, M Minato, A Taketomi. Abstract. Background: Post-operative pancreatitis is a severe complication after cyst excision with hepaticoenterostomy (CEHE) for choledochal cysts. The aim of this study was ...

  11. The value of computed tomography-urography in predicting the ...

    African Journals Online (AJOL)

    Background The natural course of pelviureteric junction (PUJ) obstruction is variable. Of those who require surgical intervention, there is no definite reliable preoperative predictor of the likely postoperative outcome. We evaluated the value of preoperative computed tomography (CT)-urography in predicting the ...

  12. Predictive Value of Respiratory Rate Thresholds in Pneumonia ...

    African Journals Online (AJOL)

    A study was carried out to determine the predictive value of respiratory rate in the clinical diagnosis of pneumonia in 101 children with respiratory symptoms of <28 days duration. Clinical, demographic and anthropometric variables were obtained at presentation while confirmation of the diagnosis was by a chest x-ray in ...

  13. Predictive equations for spirometric reference values in a healthy ...

    African Journals Online (AJOL)

    Predictive equations for spirometric reference values in a healthy adult suburban population in Tanzania. TORIL M. KNUDSEN1, ODD MØRKVE1, SAYOKI MFINANGA2 and JON A. HARDIE3, 4. 1Centre for International Health, University of Bergen, Norway. 2National Institute for Medical Research, Muhimbili Medical ...

  14. E3value to BPMN model transformation

    NARCIS (Netherlands)

    Fatemi, Hassan; van Sinderen, Marten J.; Wieringa, Roelf J.; Wieringa, P.A.; Camarinha-Matos, Luis M.; Pereira Klen, Alexandra; Afsarmanesh, Hamidesh

    2011-01-01

    Business value and coordination process perspectives need to be taken into consideration while modeling business collaborations. The need for these two models stems from the importance of separating the how from the what concerns. A business value model shows what is offered by whom to whom while a

  15. MODEL PREDICTIVE CONTROL FUNDAMENTALS

    African Journals Online (AJOL)

    2012-07-02

    Jul 2, 2012 ... Linear MPC. 1. Uses linear model: ˙x = Ax + Bu. 2. Quadratic cost function: F = xT Qx + uT Ru. 3. Linear constraints: Hx + Gu < 0. 4. Quadratic program. Nonlinear MPC. 1. Nonlinear model: ˙x = f(x, u). 2. Cost function can be nonquadratic: F = (x, u). 3. Nonlinear constraints: h(x, u) < 0. 4. Nonlinear program.

  16. Modelling bankruptcy prediction models in Slovak companies

    Directory of Open Access Journals (Sweden)

    Kovacova Maria

    2017-01-01

    Full Text Available An intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression and early artificial intelligence models (e.g. artificial neural networks, there is a trend for transition to machine learning models (support vector machines, bagging, boosting, and random forest to predict bankruptcy one year prior to the event. Comparing the performance of this with unconventional approach with results obtained by discriminant analysis, logistic regression, and neural networks application, it has been found that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. On the other side the prediction accuracy of old and well known bankruptcy prediction models is quiet high. Therefore, we aim to analyse these in some way old models on the dataset of Slovak companies to validate their prediction ability in specific conditions. Furthermore, these models will be modelled according to new trends by calculating the influence of elimination of selected variables on the overall prediction ability of these models.

  17. Melanoma Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  18. Predictive models of moth development

    Science.gov (United States)

    Degree-day models link ambient temperature to insect life-stages, making such models valuable tools in integrated pest management. These models increase management efficacy by predicting pest phenology. In Wisconsin, the top insect pest of cranberry production is the cranberry fruitworm, Acrobasis v...

  19. Predictive Models and Computational Embryology

    Science.gov (United States)

    EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

  20. Can venous cord gas values predict fetal acidemia?

    Science.gov (United States)

    Swanson, Kate; Whelan, Anna R; Grobman, William A; Miller, Emily S

    2017-09-01

    Umbilical cord arterial blood gas values are used to diagnose fetal acidemia; however, arterial cord blood specimens are frequently not available. We sought to assess whether umbilical cord venous blood gas values can be used to reliably predict fetal acidemia. This is an observational study of women with a singleton gestation at a single tertiary care hospital who delivered from September 2010 through August 2015 and had both umbilical cord arterial and venous blood gas samples measured. Fetal acidemia was defined in 2 ways: (1) umbilical cord arterial pH gas values and the areas under the curve were calculated. Umbilical cord venous blood gas cutoffs associated with gas values. Copyright © 2017 Elsevier Inc. All rights reserved.

  1. A predictive model for dimensional errors in fused deposition modeling

    DEFF Research Database (Denmark)

    Stolfi, A.

    2015-01-01

    This work concerns the effect of deposition angle (a) and layer thickness (L) on the dimensional performance of FDM parts using a predictive model based on the geometrical description of the FDM filament profile. An experimental validation over the whole a range from 0° to 177° at 3° steps and two...... values of L (0.254 mm, 0.330 mm) was produced by comparing predicted values with external face-to-face measurements. After removing outliers, the results show that the developed two-parameter model can serve as tool for modeling the FDM dimensional behavior in a wide range of deposition angles....

  2. Predicting p Ka values from EEM atomic charges

    Science.gov (United States)

    2013-01-01

    The acid dissociation constant p Ka is a very important molecular property, and there is a strong interest in the development of reliable and fast methods for p Ka prediction. We have evaluated the p Ka prediction capabilities of QSPR models based on empirical atomic charges calculated by the Electronegativity Equalization Method (EEM). Specifically, we collected 18 EEM parameter sets created for 8 different quantum mechanical (QM) charge calculation schemes. Afterwards, we prepared a training set of 74 substituted phenols. Additionally, for each molecule we generated its dissociated form by removing the phenolic hydrogen. For all the molecules in the training set, we then calculated EEM charges using the 18 parameter sets, and the QM charges using the 8 above mentioned charge calculation schemes. For each type of QM and EEM charges, we created one QSPR model employing charges from the non-dissociated molecules (three descriptor QSPR models), and one QSPR model based on charges from both dissociated and non-dissociated molecules (QSPR models with five descriptors). Afterwards, we calculated the quality criteria and evaluated all the QSPR models obtained. We found that QSPR models employing the EEM charges proved as a good approach for the prediction of p Ka (63% of these models had R2 > 0.9, while the best had R2 = 0.924). As expected, QM QSPR models provided more accurate p Ka predictions than the EEM QSPR models but the differences were not significant. Furthermore, a big advantage of the EEM QSPR models is that their descriptors (i.e., EEM atomic charges) can be calculated markedly faster than the QM charge descriptors. Moreover, we found that the EEM QSPR models are not so strongly influenced by the selection of the charge calculation approach as the QM QSPR models. The robustness of the EEM QSPR models was subsequently confirmed by cross-validation. The applicability of EEM QSPR models for other chemical classes was illustrated by a case study focused on

  3. Predicting extreme Value at Risk: Nonparametric quantile regression with refinements from extreme value theory

    NARCIS (Netherlands)

    Schaumburg, J.

    2012-01-01

    A framework is introduced allowing us to apply nonparametric quantile regression to Value at Risk (VaR) prediction at any probability level of interest. A monotonized double kernel local linear estimator is used to estimate moderate (1%) conditional quantiles of index return distributions. For

  4. Predictive values of symptoms in relation to cancer diagnosis

    DEFF Research Database (Denmark)

    Krasnik, Ivan; Andersen, John Sahl

    years (6,6%-21,2%), but much lower in younger age groups. ”Change in bowel habits” and ”Significant general symptoms” are more uncertain (3,5%-8,5%). Breast cancer: ”Palpable suspect tumor” is well supported (8,1%-24%). The predictive value of ”Pitting of the skin”, ”Papil-areola eczema......Background/significance: Poorer prognosis for cancer patients in Denmark than in comparable countries has been shown and contributed to the introduction of accelerated diagnostic trajectories for patients suspicious for cancer in 2008. For all types of cancers the National Board of Health developed...... a manual describing the symptoms that should engender reasonable suspicion of malignancy (“alarm symptoms”) to the general practitioner. Objectives: To investigate the evidence in the literature of the predictive value (PPV) placed on the”alarm symptoms” for colon cancer, breast cancer, prostate cancer...

  5. Predictions models with neural nets

    Directory of Open Access Journals (Sweden)

    Vladimír Konečný

    2008-01-01

    Full Text Available The contribution is oriented to basic problem trends solution of economic pointers, using neural networks. Problems include choice of the suitable model and consequently configuration of neural nets, choice computational function of neurons and the way prediction learning. The contribution contains two basic models that use structure of multilayer neural nets and way of determination their configuration. It is postulate a simple rule for teaching period of neural net, to get most credible prediction.Experiments are executed with really data evolution of exchange rate Kč/Euro. The main reason of choice this time series is their availability for sufficient long period. In carry out of experiments the both given basic kind of prediction models with most frequent use functions of neurons are verified. Achieve prediction results are presented as in numerical and so in graphical forms.

  6. PREDICTION OF BASALT FIBER REINFORCED CONCRETE PAVEMENT BENDING STRENGTH VALUES

    OpenAIRE

    Hidayet BAYRAKTAR; Ayhan SAMANDAR; Suat SARIDEMİR

    2017-01-01

    This paper proposes the potential of artificial neural network (ANN) system for estimating the bending strength values of the basalt fiber reinforced concrete pavements. Three main influential parameters; namely basalt fiber ratio, density and slump value of the fresh concrete were selected as input data. The model was trained, tested using 400 data sets which were the results of on-site experiment tests. ANN system results were also compared with the experimental test results. The research r...

  7. Predictive analytics can support the ACO model.

    Science.gov (United States)

    Bradley, Paul

    2012-04-01

    Predictive analytics can be used to rapidly spot hard-to-identify opportunities to better manage care--a key tool in accountable care. When considering analytics models, healthcare providers should: Make value-based care a priority and act on information from analytics models. Create a road map that includes achievable steps, rather than major endeavors. Set long-term expectations and recognize that the effectiveness of an analytics program takes time, unlike revenue cycle initiatives that may show a quick return.

  8. Predicting standard penetration test N-value from cone penetration test data using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Bashar Tarawneh

    2017-01-01

    Full Text Available Standard Penetration Test (SPT and Cone Penetration Test (CPT are the most frequently used field tests to estimate soil parameters for geotechnical analysis and design. Numerous soil parameters are related to the SPT N-value. In contrast, CPT is becoming more popular for site investigation and geotechnical design. Correlation of CPT data with SPT N-value is very beneficial since most of the field parameters are related to SPT N-values. A back-propagation artificial neural network (ANN model was developed to predict the N60-value from CPT data. Data used in this study consisted of 109 CPT-SPT pairs for sand, sandy silt, and silty sand soils. The ANN model input variables are: CPT tip resistance (qc, effective vertical stress (σv′, and CPT sleeve friction (fs. A different set of SPT-CPT data was used to check the reliability of the developed ANN model. It was shown that ANN model either under-predicted the N60-value by 7–16% or over-predicted it by 7–20%. It is concluded that back-propagation neural networks is a good tool to predict N60-value from CPT data with acceptable accuracy.

  9. Predictive value of ventilatory inflection points determined under field conditions.

    Science.gov (United States)

    Heyde, Christian; Mahler, Hubert; Roecker, Kai; Gollhofer, Albert

    2016-01-01

    The aim of this study was to evaluate the predictive potential provided by two ventilatory inflection points (VIP1 and VIP2) examined in field without using gas analysis systems and uncomfortable facemasks. A calibrated respiratory inductance plethysmograph (RIP) and a computerised routine were utilised, respectively, to derive ventilation and to detect VIP1 and VIP2 during a standardised field ramp test on a 400 m running track on 81 participants. In addition, average running speed of a competitive 1000 m run (S1k) was observed as criterion. The predictive value of running speed at VIP1 (SVIP1) and the speed range between VIP1 and VIP2 in relation to VIP2 (VIPSPAN) was analysed via regression analysis. VIPSPAN rather than running speed at VIP2 (SVIP2) was operationalised as a predictor to consider the covariance between SVIP1 and SVIP2. SVIP1 and VIPSPAN, respectively, provided 58.9% and 22.9% of explained variance in regard to S1k. Considering covariance, the timing of two ventilatory inflection points provides predictive value in regard to a competitive 1000 m run. This is the first study to apply computerised detection of ventilatory inflection points in a field setting independent on measurements of the respiratory gas exchange and without using any facemasks.

  10. Diagnostic value of apparent diffusion coefficient value in prediction of grade for neuroepithelial tumors

    International Nuclear Information System (INIS)

    Chen Zhiye; Ma Lin

    2009-01-01

    Objective: To investigate the predictive value of ADC value in grading of neuroepithelial tumors. Methods: The clinical data and images of 70 patients with neuroepithelial tumors pathologically proven were collected and analyzed retrospectively. All the patients were classified into low (WHO I or II) and high (WHO III or IV) grade groups which included 40 and 30 cases respectively according to the 2007 WHO classification of tumours of the central nervous system. All the patients underwent plain and contrast-enhanced MR scan and DWI before surgery. The minimum ADC (MinADC) value was measured postoperatively on ADC maps. The Ki-67 labeling index (Ki-67 LI) of tumor tissue was determined by immunohistochemistry. MinADC values for two groups were analyzed using student t test, while the age and Ki-67 LI for the two groups was analyzed using Mann-Whitney test (P -3 mm 2 /s] of the low grade group was significantly higher than that [(0.74±0.18) x 10 -3 mm 2 /s] of the high grade group (t=5.42, P -3 mm 2 /s for the differentiation between high and low grade neuroepithelial tumors provided the best combination of sensitivity (90.0%) and specificity (77.5%) (receiver operating characteristic analysis). Conclusion: MinADC value is helpful for prediction of neuroepithelial tumor grade.. (authors)

  11. Can Hounsfield Unit Value Predict Type of Urinary Stones?

    Directory of Open Access Journals (Sweden)

    Alper Gok

    2014-03-01

    Full Text Available Aim: Aim of this study is to determine the role of Hounsfield unit (HU in predicting results of stone analysis. Material and Method: This study included 199 patients to whom percutaneous nephrolithotomy (PNL procedures were applied between January 2008 and May 2011 in our clinic. Before the procedure HU values of kidney stones were measured using non-contrast computed tomography. After the operation, obtained stone samples were analysed using X-ray diffraction technique. HU values were compared with stone analysis results. Results: Stone analysis revealed eight different stone types. Distribution of stone types and HU value ranges were as follows: 85% calcium oxalate monohydrate, 730-1130 HU; 38% calcium oxalate dihydrate, 510-810 HU; 21% uric acid, 320-550 HU; 23% struvite, 614-870 HU; 7% calcium hydrogene phosphate, 1100-1365 HU; 3% cystine, 630-674 HU; 15% mixed uric acid plus calcium oxalate, 499-840 HU; and 7% mixed cystine plus calcium phosphate, 430-520 HU. HU values of all stone types ranged between 320 and 1365. There was a statistically significant relation between HU values of uric acid and non uric acid stones (p

  12. Does Spontaneous Favorability to Power (vs. Universalism) Values Predict Spontaneous Prejudice and Discrimination?

    Science.gov (United States)

    Souchon, Nicolas; Maio, Gregory R; Hanel, Paul H P; Bardin, Brigitte

    2017-10-01

    We conducted five studies testing whether an implicit measure of favorability toward power over universalism values predicts spontaneous prejudice and discrimination. Studies 1 (N = 192) and 2 (N = 86) examined correlations between spontaneous favorability toward power (vs. universalism) values, achievement (vs. benevolence) values, and a spontaneous measure of prejudice toward ethnic minorities. Study 3 (N = 159) tested whether conditioning participants to associate power values with positive adjectives and universalism values with negative adjectives (or inversely) affects spontaneous prejudice. Study 4 (N = 95) tested whether decision bias toward female handball players could be predicted by spontaneous attitude toward power (vs. universalism) values. Study 5 (N = 123) examined correlations between spontaneous attitude toward power (vs. universalism) values, spontaneous importance toward power (vs. universalism) values, and spontaneous prejudice toward Black African people. Spontaneous positivity toward power (vs. universalism) values was associated with spontaneous negativity toward minorities and predicted gender bias in a decision task, whereas the explicit measures did not. These results indicate that the implicit assessment of evaluative responses attached to human values helps to model value-attitude-behavior relations. © 2016 The Authors. Journal of Personality Published by Wiley Periodicals, Inc.

  13. Value-Oriented Coordination Process Modeling

    NARCIS (Netherlands)

    Fatemi, Hassan; van Sinderen, Marten J.; Wieringa, Roelf J.; Hull, Richard; Mendling, Jan; Tai, Stefan

    Business webs are collections of enterprises designed to jointly satisfy a consumer need. Designing business webs calls for modeling the collaboration of enterprises from different perspectives, in particular the business value and coordination process perspectives, and for mutually aligning these

  14. Comparison of perceived value structural models

    Directory of Open Access Journals (Sweden)

    Sunčana Piri Rajh

    2012-07-01

    Full Text Available Perceived value has been considered an important determinant of consumer shopping behavior and studied as such for a long period of time. According to one research stream, perceived value is a variable determined by perceived quality and perceived sacrifice. Another research stream suggests that the perception of value is a result of the consumer risk perception. This implies the presence of two somewhat independent research streams that are integrated by a third research stream – the one suggesting that perceived value is a result of perceived quality and perceived sacrifices while perceived (performance and financial risk mediates the relationship between perceived quality and perceived sacrifices on the one hand, and perceived value on the other. This paper describes the three approaches (models that have been mentioned. The aim of the paper is to determine which of the observed models show the most acceptable level of fit to the empirical data. Using the survey method, research involving three product categories has been conducted on a sample of Croatian consumers. Collected data was analyzed by the structural equation modeling (SEM method. Research has shown an appropriate level of fit of each observed model to the empirical data. However, the model measuring the effect of perceived risk on perceived value indicates the best level of fit, which implies that perceived performance risk and perceived financial risk are the best predictors of perceived value.

  15. What do saliency models predict?

    Science.gov (United States)

    Koehler, Kathryn; Guo, Fei; Zhang, Sheng; Eckstein, Miguel P.

    2014-01-01

    Saliency models have been frequently used to predict eye movements made during image viewing without a specified task (free viewing). Use of a single image set to systematically compare free viewing to other tasks has never been performed. We investigated the effect of task differences on the ability of three models of saliency to predict the performance of humans viewing a novel database of 800 natural images. We introduced a novel task where 100 observers made explicit perceptual judgments about the most salient image region. Other groups of observers performed a free viewing task, saliency search task, or cued object search task. Behavior on the popular free viewing task was not best predicted by standard saliency models. Instead, the models most accurately predicted the explicit saliency selections and eye movements made while performing saliency judgments. Observers' fixations varied similarly across images for the saliency and free viewing tasks, suggesting that these two tasks are related. The variability of observers' eye movements was modulated by the task (lowest for the object search task and greatest for the free viewing and saliency search tasks) as well as the clutter content of the images. Eye movement variability in saliency search and free viewing might be also limited by inherent variation of what observers consider salient. Our results contribute to understanding the tasks and behavioral measures for which saliency models are best suited as predictors of human behavior, the relationship across various perceptual tasks, and the factors contributing to observer variability in fixational eye movements. PMID:24618107

  16. The Predictive Value of Germline Polymorphisms in Patients with NSCLC

    DEFF Research Database (Denmark)

    Nygaard, Anneli Dowler; Spindler, Karen-Lise Garm; Andersen, Rikke Fredslund

    2010-01-01

    urgently needed. Single Nucleotide Polymorphisms (SNPs) are stable markers of potential clinical value and the study aimed at evaluating their use in lung cancer patients given standard chemotherapy. Genomic DNA was extracted from a pre-treatment blood sample drawn from patients with advanced Non....... Haplotypes were estimated and analyzed when relevant. There were no significant associations between SNPs in the EGF system or the DNA-repair system and RR, PFS or OS. In contrast, the VEGF+405, VEGF-460 and VEGF-2579, heterozygous patients had a higher response rate and longer PFS than homozygous patients....... Haplotype analysis of the VEGF+405 and VEGF- 460 supported our findings. These results were, however, not confirmed in the validation cohort. Although significant results regarding VEGF related SNPs, in the primary analysis, no predictive value of a broad panel of SNPs in NSCLC was found in the validation...

  17. Positive Predictive Value of BI-RADS MR Imaging

    Science.gov (United States)

    Gatsonis, Constantine; Hanna, Lucy; DeMartini, Wendy B.; Lehman, Constance

    2012-01-01

    Purpose: To evaluate the positive predictive values (PPVs) of Breast Imaging and Reporting Data Systems (BI-RADS) assessment categories for breast magnetic resonance (MR) imaging and to identify the BI-RADS MR imaging lesion features most predictive of cancer. Materials and Methods: This institutional review board–approved HIPAA-compliant prospective multicenter study was performed with written informed consent. Breast MR imaging studies of the contralateral breast in women with a recent diagnosis of breast cancer were prospectively evaluated. Contralateral breast MR imaging BI-RADS assessment categories, morphologic descriptors for foci, masses, non-masslike enhancement (NMLE), and kinetic features were assessed for predictive values for malignancy. PPV of each imaging characteristic of interest was estimated, and logistic regression analysis was used to examine the predictive ability of combinations of characteristics. Results: Of 969 participants, 71.3% had a BI-RADS category 1 or 2 assessment; 10.9%, a BI-RADS category 3 assessment; 10.0%, a BI-RADS category 4 or 5 assessment; and 7.7%, a BI-RADS category 0 assessment on the basis of initial MR images. Thirty-one cancers were detected with MR imaging. Overall PPV for BI-RADS category 4 and 5 lesions was 0.278, with 17 cancers in patients with a BI-RADS category 4 lesion (PPV, 0.205) and 10 cancers in patients with a BI-RADS category 5 lesion (PPV, 0.714). Of the cancers, one was a focus, 17 were masses, and 13 were NMLEs. For masses, irregular shape, irregular margins, spiculated margins, and marked internal enhancement were most predictive of malignancy. For NMLEs, ductal, clumped, and reticular or dendritic enhancement were the features most frequently seen with malignancy. Kinetic enhancement features were less predictive of malignancy than were morphologic features. Conclusion: Standardized terminology of the BI-RADS lexicon enables quantification of the likelihood of malignancy for MR imaging

  18. Positive predictive value of BI-RADS MR imaging.

    Science.gov (United States)

    Mahoney, Mary C; Gatsonis, Constantine; Hanna, Lucy; DeMartini, Wendy B; Lehman, Constance

    2012-07-01

    To evaluate the positive predictive values (PPVs) of Breast Imaging and Reporting Data Systems (BI-RADS) assessment categories for breast magnetic resonance (MR) imaging and to identify the BI-RADS MR imaging lesion features most predictive of cancer. This institutional review board-approved HIPAA-compliant prospective multicenter study was performed with written informed consent. Breast MR imaging studies of the contralateral breast in women with a recent diagnosis of breast cancer were prospectively evaluated. Contralateral breast MR imaging BI-RADS assessment categories, morphologic descriptors for foci, masses, non-masslike enhancement (NMLE), and kinetic features were assessed for predictive values for malignancy. PPV of each imaging characteristic of interest was estimated, and logistic regression analysis was used to examine the predictive ability of combinations of characteristics. Of 969 participants, 71.3% had a BI-RADS category 1 or 2 assessment; 10.9%, a BI-RADS category 3 assessment; 10.0%, a BI-RADS category 4 or 5 assessment; and 7.7%, a BI-RADS category 0 assessment on the basis of initial MR images. Thirty-one cancers were detected with MR imaging. Overall PPV for BI-RADS category 4 and 5 lesions was 0.278, with 17 cancers in patients with a BI-RADS category 4 lesion (PPV, 0.205) and 10 cancers in patients with a BI-RADS category 5 lesion (PPV, 0.714). Of the cancers, one was a focus, 17 were masses, and 13 were NMLEs. For masses, irregular shape, irregular margins, spiculated margins, and marked internal enhancement were most predictive of malignancy. For NMLEs, ductal, clumped, and reticular or dendritic enhancement were the features most frequently seen with malignancy. Kinetic enhancement features were less predictive of malignancy than were morphologic features. Standardized terminology of the BI-RADS lexicon enables quantification of the likelihood of malignancy for MR imaging-detected lesions through careful evaluation of lesion features. In

  19. Reducing dimensionality for prediction of genome-wide breeding values

    Directory of Open Access Journals (Sweden)

    Woolliams John A

    2009-03-01

    Full Text Available Abstract Partial least square regression (PLSR and principal component regression (PCR are methods designed for situations where the number of predictors is larger than the number of records. The aim was to compare the accuracy of genome-wide breeding values (EBV produced using PLSR and PCR with a Bayesian method, 'BayesB'. Marker densities of 1, 2, 4 and 8 Ne markers/Morgan were evaluated when the effective population size (Ne was 100. The correlation between true breeding value and estimated breeding value increased with density from 0.611 to 0.681 and 0.604 to 0.658 using PLSR and PCR respectively, with an overall advantage to PLSR of 0.016 (s.e = 0.008. Both methods gave a lower accuracy compared to the 'BayesB', for which accuracy increased from 0.690 to 0.860. PLSR and PCR appeared less responsive to increased marker density with the advantage of 'BayesB' increasing by 17% from a marker density of 1 to 8Ne/M. PCR and PLSR showed greater bias than 'BayesB' in predicting breeding values at all densities. Although, the PLSR and PCR were computationally faster and simpler, these advantages do not outweigh the reduction in accuracy, and there is a benefit in obtaining relevant prior information from the distribution of gene effects.

  20. p-values for model evaluation

    International Nuclear Information System (INIS)

    Beaujean, F.; Caldwell, A.; Kollar, D.; Kroeninger, K.

    2011-01-01

    Deciding whether a model provides a good description of data is often based on a goodness-of-fit criterion summarized by a p-value. Although there is considerable confusion concerning the meaning of p-values, leading to their misuse, they are nevertheless of practical importance in common data analysis tasks. We motivate their application using a Bayesian argumentation. We then describe commonly and less commonly known discrepancy variables and how they are used to define p-values. The distribution of these are then extracted for examples modeled on typical data analysis tasks, and comments on their usefulness for determining goodness-of-fit are given.

  1. Fast and simultaneous prediction of animal feed nutritive values using near infrared reflectance spectroscopy

    Science.gov (United States)

    Samadi; Wajizah, S.; Munawar, A. A.

    2018-02-01

    Feed plays an important factor in animal production. The purpose of this study is to apply NIRS method in determining feed values. NIRS spectra data were acquired for feed samples in wavelength range of 1000 - 2500 nm with 32 scans and 0.2 nm wavelength. Spectral data were corrected by de-trending (DT) and standard normal variate (SNV) methods. Prediction of in vitro dry matter digestibility (IVDMD) and in vitro organic matter digestibility (IVOMD) were established as model by using principal component regression (PCR) and validated using leave one out cross validation (LOOCV). Prediction performance was quantified using coefficient correlation (r) and residual predictive deviation (RPD) index. The results showed that IVDMD and IVOMD can be predicted by using SNV spectra data with r and RPD index: 0.93 and 2.78 for IVDMD ; 0.90 and 2.35 for IVOMD respectively. In conclusion, NIRS technique appears feasible to predict animal feed nutritive values.

  2. p-values for model evaluation

    Energy Technology Data Exchange (ETDEWEB)

    Beaujean, Frederik; Caldwell, Allen [Max-Planck-Institut fuer Physik, Muenchen (Germany); Kollar, Daniel [CERN, Genf (Switzerland); Kroeninger, Kevin [Georg-August-Universitaet, Goettingen (Germany)

    2011-07-01

    In the analysis of experimental results it is often necessary to pass a judgment on the validity of a model as a representation of the data. A quantitative procedure to decide whether a model provides a good description of data is often based on a specific test statistic and a p-value summarizing both the data and the statistic's sampling distribution. Although there is considerable confusion concerning the meaning of p-values, leading to their misuse, they are nevertheless of practical importance in common data analysis tasks. We motivate the application of p-values using a Bayesian argumentation. We then describe commonly and less commonly known test statistics and how they are used to define p-values. The distribution of these are then extracted for examples modeled on typical new physics searches in high energy physics. We comment on their usefulness for determining goodness-of-fit and highlight some common pitfalls.

  3. Seizure Prediction and Detection via Phase and Amplitude Lock Values.

    Science.gov (United States)

    Myers, Mark H; Padmanabha, Akshay; Hossain, Gahangir; de Jongh Curry, Amy L; Blaha, Charles D

    2016-01-01

    A robust seizure prediction methodology would enable a "closed-loop" system that would only activate as impending seizure activity is detected. Such a system would eliminate ongoing stimulation to the brain, thereby eliminating such side effects as coughing, hoarseness, voice alteration, and paresthesias (Murphy et al., 1998; Ben-Menachem, 2001), while preserving overall battery life of the system. The seizure prediction and detection algorithm uses Phase/Amplitude Lock Values (PLV/ALV) which calculate the difference of phase and amplitude between electroencephalogram (EEG) electrodes local and remote to the epileptic event. PLV is used as the seizure prediction marker and signifies the emergence of abnormal neuronal activations through local neuron populations. PLV/ALVs are used as seizure detection markers to demarcate the seizure event, or when the local seizure event has propagated throughout the brain turning into a grand-mal event. We verify the performance of this methodology against the "CHB-MIT Scalp EEG Database" which features seizure attributes for testing. Through this testing, we can demonstrate a high degree of sensivity and precision of our methodology between pre-ictal and ictal events.

  4. Seizure Prediction and Detection via Phase and Amplitude Lock Values

    Directory of Open Access Journals (Sweden)

    Mark H Myers

    2016-03-01

    Full Text Available A robust seizure prediction methodology would enable a ‘closed-loop’ system that would only activate as impending seizure activity is detected. Such a system would eliminate ongoing stimulation to the brain, thereby eliminating such side effects as coughing, hoarseness, voice alteration, and paresthesias (Murphy et al., 1998, Ben-Menachem, 2001, while preserving overall battery life of the system. The seizure prediction and detection algorithm uses Phase/Amplitude Lock Values (PLV/ALV which calculate the difference of phase and amplitude between EEG electrodes local and remote to the epileptic event. PLV is used as the seizure prediction marker and signifies the emergence of abnormal neuronal activations through local neuron populations. PLV/ALVs are used as seizure detection markers to demarcate the seizure event, or when the local seizure event has propagated throughout the brain turning into a grand-mal event. We verify the performance of this methodology against the ‘CHB-MIT Scalp EEG Database’ which features seizure attributes for testing. Through this testing, we can demonstrate a high degree of sensivity and precision of our methodology between pre-ictal and ictal events.

  5. Interpretation of Spirometry: Selection of Predicted Values and Defining Abnormality.

    Science.gov (United States)

    Chhabra, S K

    2015-01-01

    Spirometry is the most frequently performed investigation to evaluate pulmonary function. It provides clinically useful information on the mechanical properties of the lung and the thoracic cage and aids in taking management-related decisions in a wide spectrum of diseases and disorders. Few measurements in medicine are so dependent on factors related to equipment, operator and the patient. Good spirometry requires quality assured measurements and a systematic approach to interpretation. Standard guidelines on the technical aspects of equipment and their calibration as well as the test procedure have been developed and revised from time-to-time. Strict compliance with standardisation guidelines ensures quality control. Interpretation of spirometry data is based only on two basic measurements--the forced vital capacity (FVC) and the forced expiratory volume in 1 second (FEV1) and their ratio, FEV1/FVC. A meaningful and clinically useful interpretation of the measured data requires a systematic approach and consideration of several important issues. Central to interpretation is the understanding of the development and application of prediction equations. Selection of prediction equations that are appropriate for the ethnic origin of the patient is vital to avoid erroneous interpretation. Defining abnormal values is a debatable but critical aspect of spirometry. A statistically valid definition of the lower limits of normal has been advocated as the better method over the more commonly used approach of defining abnormality as a fixed percentage of the predicted value. Spirometry rarely provides a specific diagnosis. Examination of the flow-volume curve and the measured data provides information to define patterns of ventilatory impairment. Spirometry must be interpreted in conjunction with clinical information including results of other investigations.

  6. Link Prediction via Sparse Gaussian Graphical Model

    Directory of Open Access Journals (Sweden)

    Liangliang Zhang

    2016-01-01

    Full Text Available Link prediction is an important task in complex network analysis. Traditional link prediction methods are limited by network topology and lack of node property information, which makes predicting links challenging. In this study, we address link prediction using a sparse Gaussian graphical model and demonstrate its theoretical and practical effectiveness. In theory, link prediction is executed by estimating the inverse covariance matrix of samples to overcome information limits. The proposed method was evaluated with four small and four large real-world datasets. The experimental results show that the area under the curve (AUC value obtained by the proposed method improved by an average of 3% and 12.5% compared to 13 mainstream similarity methods, respectively. This method outperforms the baseline method, and the prediction accuracy is superior to mainstream methods when using only 80% of the training set. The method also provides significantly higher AUC values when using only 60% in Dolphin and Taro datasets. Furthermore, the error rate of the proposed method demonstrates superior performance with all datasets compared to mainstream methods.

  7. [Prediction of SPAD value in oilseed rape leaves using hyperspectral imaging technique].

    Science.gov (United States)

    Ding, Xi-bin; Liu, Fei; Zhang, Chu; He, Yong

    2015-02-01

    In the present work, prediction models of SPAD value (Soil and Plant Analyzer Development, often used as a parameter to indicate chlorophyll content) in oilseed rape leaves were successfully built using hyperspectral imaging technique. The hy perspectral images of 160 oilseed rape leaf samples in the spectral range of 380-1030 nm were acquired. Average spectrum was extracted from the region of interest (ROI) of each sample. We chose spectral data in the spectral range of 500-900 nm for analysis. Using Monte Carlo partial least squares(MC-PLS) algorithm, 13 samples were identified as outliers and eliminated. Based on the spectral information and measured SPAD values of the rest 147 samples, several estimation models have been built based on different parameters using different algorithms for comparison, including: (1) a SPAD value estimation model based on partial least squares(PLS) in the whole wavelength region of 500-900 nm; (2) a SPAD value estimation model based on successive projections algorithmcombined with PLS(SPA-PLS); (3) 4 kind of simple experience SPAD value estimation models in which red edge position was used as an argument; (4) 4 kind of simple experience SPAD value estimation models in which three vegetation indexes R710/R760, (R750-R705)/(R750-R705) and R860/(R550 x R708), which all have been proved to have a good relevance with chlorophyll content, were used as an argument respectively; (5) a SPAD value estimation model based on PLS using the 3 vegetation indexes mentioned above. The results indicate that the optimal prediction performance is achieved by PLS model in the whole wavelength region of 500-900 nm, which has a correlation coefficient(r(p)) of 0.8339 and a root mean squares error of predicted (RMSEP) of 1.52. The SPA-PLS model can provide avery close prediction result while the calibration computation has been significantly reduced and the calibration speed has been accelerated sharply. For simple experience models based on red edge

  8. From business value model to coordination process model

    NARCIS (Netherlands)

    Fatemi, Hassan; Wieringa, Roelf J.; Poler, R.; van Sinderen, Marten J.; Sanchis, R.

    2009-01-01

    The increased complexity of business webs calls for modeling the collaboration of enterprises from different perspectives, in particular the business and process perspectives, and for mutually aligning these perspectives. Business value modeling and coordination process modeling both are necessary

  9. Multivariate linear regression analysis to identify general factors for quantitative predictions of implant stability quotient values.

    Directory of Open Access Journals (Sweden)

    Hairong Huang

    Full Text Available This study identified potential general influencing factors for a mathematical prediction of implant stability quotient (ISQ values in clinical practice.We collected the ISQ values of 557 implants from 2 different brands (SICace and Osstem placed by 2 surgeons in 336 patients. Surgeon 1 placed 329 SICace implants, and surgeon 2 placed 113 SICace implants and 115 Osstem implants. ISQ measurements were taken at T1 (immediately after implant placement and T2 (before dental restoration. A multivariate linear regression model was used to analyze the influence of the following 11 candidate factors for stability prediction: sex, age, maxillary/mandibular location, bone type, immediate/delayed implantation, bone grafting, insertion torque, I-stage or II-stage healing pattern, implant diameter, implant length and T1-T2 time interval.The need for bone grafting as a predictor significantly influenced ISQ values in all three groups at T1 (weight coefficients ranging from -4 to -5. In contrast, implant diameter consistently influenced the ISQ values in all three groups at T2 (weight coefficients ranging from 3.4 to 4.2. Other factors, such as sex, age, I/II-stage implantation and bone type, did not significantly influence ISQ values at T2, and implant length did not significantly influence ISQ values at T1 or T2.These findings provide a rational basis for mathematical models to quantitatively predict the ISQ values of implants in clinical practice.

  10. A new likelihood approach to inference about predictive values of diagnostic tests in paired designs.

    Science.gov (United States)

    Tsou, Tsung-Shan

    2018-02-01

    Intuitively, one only needs patients with two positive screening test results for positive predictive values comparison, and those with two negative screening test results for contrasting negative predictive values. Nevertheless, current existing methods rely on the multinomial model that includes superfluous parameters unnecessary for specific comparisons. This practice results in complex statistics formulas. We introduce a novel likelihood approach that fits the intuition by including a minimum number of parameters of interest in paired designs. It is demonstrated that our robust score test statistic is identical to a newly proposed weighted generalized score test statistic. Simulations and real data analysis are used for illustration.

  11. Sensitivity and predictive value of ultrasound in pediatric cholecystitis.

    Science.gov (United States)

    Tsai, Jacqueline; Sulkowski, Jason P; Cooper, Jennifer N; Mattei, Peter; Deans, Katherine J; Minneci, Peter C

    2013-09-01

    Ultrasonography has a high sensitivity and positive predictive value (PPV) for diagnosing cholecystitis in adults. The objective of this study was to determine the sensitivity and PPV of ultrasonography in the diagnosis of pediatric cholecystitis. We performed a single-institution retrospective review of the records of all patients undergoing cholecystectomy with a preoperative ultrasound during 2005-2010. We calculated sensitivity, specificity, and PPV using pathologic findings as the standard for the diagnosis of cholecystitis. In the 223 included patients, the median (interquartile range) age was 14 y (11-16 y); and 64% were female. Preoperative symptoms of abdominal pain were reported in 98% of patients. A diagnosis of cholecystitis was reported in 10% (23 of 223) of ultrasound readings. Pathologic diagnosis of cholecystitis was present in 80% (179 of 223) of cholecystectomy specimens, with 8% (15 of 179) having acute cholecystitis, 83% (148 of 179) chronic cholecystitis, and 9% (16 of 179) both. Sensitivity of ultrasound findings ranged from 6% for Murphy's sign to 66% for cholelithiasis. Positive predictive values ranged from 67% for Murphy's sign to 87% for gallbladder sludge. Presence of any one ultrasound sign had a sensitivity of 82% and PPV of 80%. Ultrasound findings in pediatric cholecystitis have lower sensitivities and PPVs than reported in adults. These differences may be explained by the higher prevalence of chronic cholecystitis in children, which suggests that children may have milder episodes of self-limited gallbladder inflammation compared with adults, which may lead to a delay in treatment. Copyright © 2013 Elsevier Inc. All rights reserved.

  12. [Predictive value of procalcitonin in children with suspected sepsis].

    Science.gov (United States)

    Bustos B, Raúl; Padilla P, Oslando

    2015-01-01

    The use of biomarkers could be a tool for diagnosis, prognosis and stratifying children with sepsis. Our main goal was to analyze the value of procalcitonin (PCT), C reactive protein (CRP) and lactate in predicting mortality, septic shock and the stratification in children with suspected sepsis Prospective study in 81 patients. Plasma levels of PCT, CRP and lactate were measured at admission in the pediatric intensive care unit. Patients were categorized into systemic inflammatory response syndrome, sepsis, severe sepsis and septic shock. Concentrations of PCT (ng/mL) increased significantly according to the severity of sepsis: 0.36 (0-1.2) for systemic inflammatory response syndrome; 1.96 (0.4-3.5) for sepsis; 7.5 (3.9-11.1) for severe sepsis; and 58.9 (35.1-82.7) for septic shock (P<.001). Compared to CRP and lactate, the area under the ROC curve revealed a good discriminative power of PCT to predict septic shock and mortality, 0.91 (95% CI: 0.83-0.97) and 0.80 (95% CI: 0.69-0.88), respectively. In contrast to CRP and lactate, the determination of PCT in pediatric intensive care unit admission is a good predictor of mortality and septic shock and can stratify patients according to severity of sepsis. Copyright © 2015 Sociedad Chilena de Pediatría. Publicado por Elsevier España, S.L.U. All rights reserved.

  13. The value of acute phase reactants in predicting preterm delivery.

    Science.gov (United States)

    Cetinkaya, Salih; Ozaksit, Gulnur; Biberoglu, Ebru Hacer; Oskovi, Asli; Kirbas, Ayse

    2017-12-01

    We aimed to determine the potential value of maternal serum levels of acute phase reactants in the prediction of preterm delivery in women with threatened preterm labor (TPL). Ninety-one pregnant women diagnosed with TPL and 83 healthy pregnant women as a control group were included in this prospective controlled study. All the pregnant women were followed until delivery and obstetric data and the serum levels of acute phase reactants were recorded for each participant. The study group was further divided into two groups according to the gestational age at delivery, which include women delivering prematurely and the ones who gave birth at term. Serum albumin levels were significantly lower and mean serum ferritin levels were significantly higher in the study groups when compared the control group. Although an association between decreased serum albumin level and TPL, also between increased serum ferritin levels and preterm birth and low birth weight were demonstrated, more extensive studies are needed to clarify the potential use of the acute phase reactants in the prediction of preterm birth.

  14. Variants of Modeling Dwelling Market Value

    Directory of Open Access Journals (Sweden)

    Barańska Anna

    2014-10-01

    Full Text Available The object of this paper is to determine real estate market value on the basis of a multidimensional function model in different variants: A - directly from the model estimated on the basis of a big database, B - from the same model form, but estimated on the basis of a reduced database consisting of dwellings most similar to the estimated one, and C - based on modeled prices corrected by random correction, calculated from random deviations for dwellings most similar to the assessed one. In the framework of statistical inference procedures, the resulting comparison was carried out by parametric significance tests. They were applied to draw conclusions on the analyzed variants

  15. PREDICTION OF BULLS’ SLAUGHTER VALUE FROM GROWTH DATA USING ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    Krzysztof ADAMCZYK

    2006-02-01

    Full Text Available The objective of this research was to investigate the usefulness of artifi cial neural network (ANN in the prediction of slaughter value of young crossbred bulls based on growth data. The studies were carried out on 104 bulls fattened from 120 days of life until the weight of 500 kg. The bulls were group fed using mainly farm feeds. After slaughter the carcasses were dissected and meat was subjected to physico-chemical and organoleptic analyses. The obtained data were used for the development of an artifi cial neural network model of slaughter value prediction. It was found that some slaughter value traits (hot carcass, cold half-carcass, neck and round weights, bone content in dissected elements in half-carcass, meat pH, dry-matter and protein contents in meat and meat tenderness and juiciness can be predicted with a considerably high accuracy using the artifi cial neural network.

  16. Modelling the predictive performance of credit scoring

    Directory of Open Access Journals (Sweden)

    Shi-Wei Shen

    2013-07-01

    Research purpose: The purpose of this empirical paper was to examine the predictive performance of credit scoring systems in Taiwan. Motivation for the study: Corporate lending remains a major business line for financial institutions. However, in light of the recent global financial crises, it has become extremely important for financial institutions to implement rigorous means of assessing clients seeking access to credit facilities. Research design, approach and method: Using a data sample of 10 349 observations drawn between 1992 and 2010, logistic regression models were utilised to examine the predictive performance of credit scoring systems. Main findings: A test of Goodness of fit demonstrated that credit scoring models that incorporated the Taiwan Corporate Credit Risk Index (TCRI, micro- and also macroeconomic variables possessed greater predictive power. This suggests that macroeconomic variables do have explanatory power for default credit risk. Practical/managerial implications: The originality in the study was that three models were developed to predict corporate firms’ defaults based on different microeconomic and macroeconomic factors such as the TCRI, asset growth rates, stock index and gross domestic product. Contribution/value-add: The study utilises different goodness of fits and receiver operator characteristics during the examination of the robustness of the predictive power of these factors.

  17. Value of different precipitation data for flood prediction in an alpine catchment: A Bayesian approach

    Science.gov (United States)

    Sikorska, A. E.; Seibert, J.

    2018-01-01

    Flooding induced by heavy precipitation is one of the most severe natural hazards in alpine catchments. To accurately predict such events, accurate and representative precipitation data are required. Estimating catchment precipitation is, however, difficult due to its high spatial, and, in the mountains, elevation-dependent variability. These inaccuracies, together with runoff model limitations, translate into uncertainty in runoff estimates. Thus, in this study, we investigate the value of three precipitation datasets, commonly used in hydrological studies, i.e., station network precipitation (SNP), interpolated grid precipitation (IGP) and radar-based precipitation (RBP), for flood predictions in an alpine catchment. To quantify their effects on runoff simulations, we perform a Bayesian uncertainty analysis with an improved description of model systematic errors. By using periods of different lengths for model calibration, we explore the information content of these three datasets for runoff predictions. Our results from an alpine catchment showed that using SNP resulted in the largest predictive uncertainty and the lowest model performance evaluated by the Nash-Sutcliffe efficiency. This performance improved from 0.674 to 0.774 with IGP, and to 0.829 with RBP. The latter two datasets were also much more informative than SNP, as half as many calibration data points were required to obtain a good model performance. Thus, our results show that the various types of precipitation data differ in their value for flood predictions in an alpine catchment and indicate RBP as the most useful dataset.

  18. A fast EM algorithm for BayesA-like prediction of genomic breeding values.

    Directory of Open Access Journals (Sweden)

    Xiaochen Sun

    Full Text Available Prediction accuracies of estimated breeding values for economically important traits are expected to benefit from genomic information. Single nucleotide polymorphism (SNP panels used in genomic prediction are increasing in density, but the Markov Chain Monte Carlo (MCMC estimation of SNP effects can be quite time consuming or slow to converge when a large number of SNPs are fitted simultaneously in a linear mixed model. Here we present an EM algorithm (termed "fastBayesA" without MCMC. This fastBayesA approach treats the variances of SNP effects as missing data and uses a joint posterior mode of effects compared to the commonly used BayesA which bases predictions on posterior means of effects. In each EM iteration, SNP effects are predicted as a linear combination of best linear unbiased predictions of breeding values from a mixed linear animal model that incorporates a weighted marker-based realized relationship matrix. Method fastBayesA converges after a few iterations to a joint posterior mode of SNP effects under the BayesA model. When applied to simulated quantitative traits with a range of genetic architectures, fastBayesA is shown to predict GEBV as accurately as BayesA but with less computing effort per SNP than BayesA. Method fastBayesA can be used as a computationally efficient substitute for BayesA, especially when an increasing number of markers bring unreasonable computational burden or slow convergence to MCMC approaches.

  19. An Alignment Model for Collaborative Value Networks

    Science.gov (United States)

    Bremer, Carlos; Azevedo, Rodrigo Cambiaghi; Klen, Alexandra Pereira

    This paper presents parts of the work carried out in several global organizations through the development of strategic projects with high tactical and operational complexity. By investing in long-term relationships, strongly operating in the transformation of the competitive model and focusing on the value chain management, the main aim of these projects was the alignment of multiple value chains. The projects were led by the Axia Transformation Methodology as well as by its Management Model and following the principles of Project Management. As a concrete result of the efforts made in the last years in the Brazilian market this work also introduces the Alignment Model which supports the transformation process that the companies undergo.

  20. Comparison of selective genotyping strategies for prediction of breeding values in a population undergoing selection.

    Science.gov (United States)

    Boligon, A A; Long, N; Albuquerque, L G; Weigel, K A; Gianola, D; Rosa, G J M

    2012-12-01

    Genomewide marker information can improve the reliability of breeding value predictions for young selection candidates in genomic selection. However, the cost of genotyping limits its use to elite animals, and how such selective genotyping affects predictive ability of genomic selection models is an open question. We performed a simulation study to evaluate the quality of breeding value predictions for selection candidates based on different selective genotyping strategies in a population undergoing selection. The genome consisted of 10 chromosomes of 100 cM each. After 5,000 generations of random mating with a population size of 100 (50 males and 50 females), generation G(0) (reference population) was produced via a full factorial mating between the 50 males and 50 females from generation 5,000. Different levels of selection intensities (animals with the largest yield deviation value) in G(0) or random sampling (no selection) were used to produce offspring of G(0) generation (G(1)). Five genotyping strategies were used to choose 500 animals in G(0) to be genotyped: 1) Random: randomly selected animals, 2) Top: animals with largest yield deviation values, 3) Bottom: animals with lowest yield deviations values, 4) Extreme: animals with the 250 largest and the 250 lowest yield deviations values, and 5) Less Related: less genetically related animals. The number of individuals in G(0) and G(1) was fixed at 2,500 each, and different levels of heritability were considered (0.10, 0.25, and 0.50). Additionally, all 5 selective genotyping strategies (Random, Top, Bottom, Extreme, and Less Related) were applied to an indicator trait in generation G(0,) and the results were evaluated for the target trait in generation G(1), with the genetic correlation between the 2 traits set to 0.50. The 5 genotyping strategies applied to individuals in G(0) (reference population) were compared in terms of their ability to predict the genetic values of the animals in G(1) (selection

  1. Prediction of Breeding Values for Dairy Cattle Using Artificial Neural Networks and Neuro-Fuzzy Systems

    Science.gov (United States)

    Shahinfar, Saleh; Mehrabani-Yeganeh, Hassan; Lucas, Caro; Kalhor, Ahmad; Kazemian, Majid; Weigel, Kent A.

    2012-01-01

    Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV) of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP) with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production. PMID:22991575

  2. Prediction of Breeding Values for Dairy Cattle Using Artificial Neural Networks and Neuro-Fuzzy Systems

    Directory of Open Access Journals (Sweden)

    Saleh Shahinfar

    2012-01-01

    Full Text Available Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production.

  3. Prediction of breeding values for dairy cattle using artificial neural networks and neuro-fuzzy systems.

    Science.gov (United States)

    Shahinfar, Saleh; Mehrabani-Yeganeh, Hassan; Lucas, Caro; Kalhor, Ahmad; Kazemian, Majid; Weigel, Kent A

    2012-01-01

    Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV) of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP) with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production.

  4. Testing and analysis of internal hardwood log defect prediction models

    Science.gov (United States)

    R. Edward. Thomas

    2011-01-01

    The severity and location of internal defects determine the quality and value of lumber sawn from hardwood logs. Models have been developed to predict the size and position of internal defects based on external defect indicator measurements. These models were shown to predict approximately 80% of all internal knots based on external knot indicators. However, the size...

  5. Foundation Settlement Prediction Based on a Novel NGM Model

    Directory of Open Access Journals (Sweden)

    Peng-Yu Chen

    2014-01-01

    Full Text Available Prediction of foundation or subgrade settlement is very important during engineering construction. According to the fact that there are lots of settlement-time sequences with a nonhomogeneous index trend, a novel grey forecasting model called NGM (1,1,k,c model is proposed in this paper. With an optimized whitenization differential equation, the proposed NGM (1,1,k,c model has the property of white exponential law coincidence and can predict a pure nonhomogeneous index sequence precisely. We used two case studies to verify the predictive effect of NGM (1,1,k,c model for settlement prediction. The results show that this model can achieve excellent prediction accuracy; thus, the model is quite suitable for simulation and prediction of approximate nonhomogeneous index sequence and has excellent application value in settlement prediction.

  6. Models for setting ATM parameter values

    DEFF Research Database (Denmark)

    Blaabjerg, Søren; Gravey, A.; Romæuf, L.

    1996-01-01

    presents approximate methods and discusses their applicability. We then discuss the problem of obtaining traffic characteristic values for a connection that has crossed a series of switching nodes. This problem is particularly relevant for the traffic contract components corresponding to ICIs...... (CDV) tolerance(s). The values taken by these traffic parameters characterize the so-called ''Worst Case Traffic'' that is used by CAC procedures for accepting a new connection and allocating resources to it. Conformance to the negotiated traffic characteristics is defined, at the ingress User...... essential to set traffic characteristic values that are relevant to the considered cell stream, and that ensure that the amount of non-conforming traffic is small. Using a queueing model representation for the GCRA formalism, several methods are available for choosing the traffic characteristics. This paper...

  7. Hematocrit and hematocrit viscosity ratio during exercise in athletes: Even closer to predicted optimal values?

    Science.gov (United States)

    Brun, Jean-Frédéric; Varlet-Marie, Emmanuelle; Raynaud de Mauverger, Eric

    2016-01-01

    The hemorheological theory of optimal hematocrit suggests that the best value of hematocrit (hct) should be that which results in the highest value of the hematocrit/viscosity (h/η) ratio. Trained athletes compared to sedentary subjects have a lower hct, but a higher h/η, and endurance training reduces the discrepancy between the actual hct and the ⪡ideal⪢ hct that can be predicted with a theoretical curve of h/η vs hct constructed with Quemada's model. In this study we investigated what becomes this homeostasis of h/η and hct during acute exercise in 19 athletes performing a 25 min exercise test. VO2max is negatively correlated to resting hct and positively correlated to discrepancy between actual and ideal resting hct which is correlated to the maximal rise in hct during exercise. Predicted and actual values of the h/η were fairly correlated (r = 0.970 p < 0.001) but the actual value was lower at rest and this discrepancy vanished at 25 min exercise. Exercise-induced decrease in discrepancy between actual and theoretical h/η was negatively correlated with the score of overtraining. All these findings suggest that h/η is a regulated parameter and that its model-predicted ⪡optimal⪢ values yield a ⪡theoretical optimal⪢ hct which is close to the actual value and even closer when athletes are well trained. In addition, acute exercise sets h/η closer from its predicted ideal value and this adaptation is impaired when athletes quote elevated scores on the overtraining questionnaire.

  8. [Predictive value of pediatrics end-stage liver disease or model for end-stage liver disease score in the prognosis of pediatric acute liver failure treated with artificial liver support system].

    Science.gov (United States)

    Jinhao, Tao; Weiming, Chen; Jing, Hu; Jun, He; Jian, Ma; Peng, Shi; Zhujin, Lu; Guoping, Lu; Yimin, Zhu

    2015-04-01

    To investigate the predictive value of pediatrics end-stage liver disease (PELD) or the model for end-stage liver disease (MELD) in the prognosis of pediatric acute liver failure (PALF) treated with artificial liver support system (ALSS). The clinical data of 47 children with acute liver failure seen from August 2008 to July 2013 treated in Children's Hospital, Fudan University were analyzed. Thirty children were treated with ALSS in addition to conventional comprehensive medical treatment (ALSS group). Seventeen children were treated with only conventional comprehensive medical treatment (control group). The main biochemical parameters and coagulation function parameters before and after treatment were compared in the ALSS group and the mortality rates were compared between the two groups. The patients were graded by PELD or MELD when they were hospitalized and the relationship of PELD or MELD scores and mortalities of child patients with the receiver operating characteristic curve (ROC) were analyzed. There were significant differences in total bilirubin (TB) ((302 ± 208) vs. (161 ± 129) µmol/L); alanine aminotransferase (ALT) ((161 ± 225) vs. (761 ± 834) U/L); aspartate aminotransferase ( AST) (66 (35, 123 ) vs. 447 (184, 1,129 ) U/L) ; international normalized ratio (INR) ((2.6 ± 1.6) vs. (5.1 ± 4.0)); prothrombin time activity percentage (PTA) ((42 ± 25)% vs. (22 ± 13)%); albumin( ALB) ((35 ± 5) vs. (33 ± 6) g/L) in the ALSS group after treatment. Through the ROC curve analysis, the best PELD/MELD threshold was 25 to predict the patients survival after ALSS therapy, with a sensitivity of 92. 3% , and a specificity of 94.1% at the cutoff point. The area under the ROC curve was 0. 912. The mortality of patients with PELD or MELD score below 25 in the ALSS group (1/13) was lower than the control group (3/4) (P = 0.022), and the mortality of patients with PELD or MELD score over 25 (16/17) was higher than that of the control group (10/13) (P = 0

  9. Cultural Values Predicting Acculturation Orientations: Operationalizing a Quantitative Measure

    Science.gov (United States)

    Ehala, Martin

    2012-01-01

    This article proposes that acculturation orientations are related to two sets of cultural values: utilitarianism (Ut) and traditionalism (Tr). While utilitarian values enhance assimilation, traditional values support language and identity maintenance. It is proposed that the propensity to either end of this value opposition can be measured by an…

  10. Negative predictive value of cardiac troponin for predicting adverse cardiac events following blunt chest trauma.

    Science.gov (United States)

    Guild, Cameron S; deShazo, Matthew; Geraci, Stephen A

    2014-01-01

    Cardiac-specific troponins (Tns) are sensitive and specific markers of myocardial injury that have been shown to be predictive of outcomes in many cardiac and noncardiac conditions. We sought to determine whether normal cardiac Tn concentrations obtained during the first 24 hours following blunt chest trauma would predict good cardiac outcomes. A PubMed/MEDLINE search was performed to identify prospective studies in patients with blunt chest trauma in which serial cardiac TnT or TnI values were measured within 24 hours of admission and clinical outcomes assessed. Ten studies qualified for review. Studies that used the lower reference limit of Tn as the cutoff for cardiac injury showed 100% negative predictive value (NPV) for developing cardiac complications, whereas studies using higher Tn cutoffs showed wider variation in NPV (50%-98%). Cardiac Tn measured within 24 hours using the lower reference limit (LRL) as the cutoff appears to have excellent NPV for clinically significant adverse cardiac events. This could allow for early discharge after a 24-hour observation period in otherwise uncomplicated blunt chest trauma patients and avoid the need for more expensive cardiac imaging and additional resource utilization.

  11. FINITE ELEMENT MODEL FOR PREDICTING RESIDUAL ...

    African Journals Online (AJOL)

    direction (σx) had a maximum value of 375MPa (tensile) and minimum value of ... These results shows that the residual stresses obtained by prediction from the finite element method are in fair agreement with the experimental results.

  12. Advanced empirical estimate of information value for credit scoring models

    Directory of Open Access Journals (Sweden)

    Martin Řezáč

    2011-01-01

    Full Text Available Credit scoring, it is a term for a wide spectrum of predictive models and their underlying techniques that aid financial institutions in granting credits. These methods decide who will get credit, how much credit they should get, and what further strategies will enhance the profitability of the borrowers to the lenders. Many statistical tools are avaiable for measuring quality, within the meaning of the predictive power, of credit scoring models. Because it is impossible to use a scoring model effectively without knowing how good it is, quality indexes like Gini, Kolmogorov-Smirnov statisic and Information value are used to assess quality of given credit scoring model. The paper deals primarily with the Information value, sometimes called divergency. Commonly it is computed by discretisation of data into bins using deciles. One constraint is required to be met in this case. Number of cases have to be nonzero for all bins. If this constraint is not fulfilled there are some practical procedures for preserving finite results. As an alternative method to the empirical estimates one can use the kernel smoothing theory, which allows to estimate unknown densities and consequently, using some numerical method for integration, to estimate value of the Information value. The main contribution of this paper is a proposal and description of the empirical estimate with supervised interval selection. This advanced estimate is based on requirement to have at least k, where k is a positive integer, observations of socres of both good and bad client in each considered interval. A simulation study shows that this estimate outperform both the empirical estimate using deciles and the kernel estimate. Furthermore it shows high dependency on choice of the parameter k. If we choose too small value, we get overestimated value of the Information value, and vice versa. Adjusted square root of number of bad clients seems to be a reasonable compromise.

  13. Comparison of collinearity mitigation techniques used in predicting BLUP breeding values and genetic gains over generations

    CSIR Research Space (South Africa)

    Eatwell, KA

    2011-01-01

    Full Text Available predicted breeding values of parents (forward prediction) with those realised in progeny (backward prediction of parents). Numeric precision had an impact on intergenerational correlations of BLUPs of some scenarios, indicating that it may not always...

  14. From Business Value Model to Coordination Process Model

    Science.gov (United States)

    Fatemi, Hassan; van Sinderen, Marten; Wieringa, Roel

    The increased complexity of business webs calls for modeling the collaboration of enterprises from different perspectives, in particular the business and process perspectives, and for mutually aligning these perspectives. Business value modeling and coordination process modeling both are necessary for a good e-business design, but these activities have different goals and use different concepts. Nevertheless, the resulting models should be consistent with each other because they refer to the same system from different perspectives. Hence, checking the consistency between these models or producing one based on the other would be of high value. In this paper we discuss the issue of achieving consistency in multi-level e-business design and give guidelines to produce consistent coordination process models from business value models in a stepwise manner.

  15. Using Heuristic Value Prediction and Dynamic Task Granularity Resizing to Improve Software Speculation

    Directory of Open Access Journals (Sweden)

    Fan Xu

    2014-01-01

    Full Text Available Exploiting potential thread-level parallelism (TLP is becoming the key factor to improving performance of programs on multicore or many-core systems. Among various kinds of parallel execution models, the software-based speculative parallel model has become a research focus due to its low cost, high efficiency, flexibility, and scalability. The performance of the guest program under the software-based speculative parallel execution model is closely related to the speculation accuracy, the control overhead, and the rollback overhead of the model. In this paper, we first analyzed the conventional speculative parallel model and presented an analytic model of its expectation of the overall overhead, then optimized the conventional model based on the analytic model, and finally proposed a novel speculative parallel model named HEUSPEC. The HEUSPEC model includes three key techniques, namely, the heuristic value prediction, the value based correctness checking, and the dynamic task granularity resizing. We have implemented the runtime system of the model in ANSI C language. The experiment results show that when the speedup of the HEUSPEC model can reach 2.20 on the average (15% higher than conventional model when depth is equal to 3 and 4.51 on the average (12% higher than conventional model when speculative depth is equal to 7. Besides, it shows good scalability and lower memory cost.

  16. Using heuristic value prediction and dynamic task granularity resizing to improve software speculation.

    Science.gov (United States)

    Xu, Fan; Shen, Li; Wang, Zhiying; Su, Bo; Guo, Hui; Chen, Wei

    2014-01-01

    Exploiting potential thread-level parallelism (TLP) is becoming the key factor to improving performance of programs on multicore or many-core systems. Among various kinds of parallel execution models, the software-based speculative parallel model has become a research focus due to its low cost, high efficiency, flexibility, and scalability. The performance of the guest program under the software-based speculative parallel execution model is closely related to the speculation accuracy, the control overhead, and the rollback overhead of the model. In this paper, we first analyzed the conventional speculative parallel model and presented an analytic model of its expectation of the overall overhead, then optimized the conventional model based on the analytic model, and finally proposed a novel speculative parallel model named HEUSPEC. The HEUSPEC model includes three key techniques, namely, the heuristic value prediction, the value based correctness checking, and the dynamic task granularity resizing. We have implemented the runtime system of the model in ANSI C language. The experiment results show that when the speedup of the HEUSPEC model can reach 2.20 on the average (15% higher than conventional model) when depth is equal to 3 and 4.51 on the average (12% higher than conventional model) when speculative depth is equal to 7. Besides, it shows good scalability and lower memory cost.

  17. Is blood glucose predictable from previous values? A solicitation for data.

    Science.gov (United States)

    Bremer, T; Gough, D A

    1999-03-01

    An important question about blood glucose control in diabetes is, Can present and future blood glucose values be predicted from recent blood glucose history? If this is possible, new continuous blood glucose monitoring technologies under development may lead to qualitatively better therapeutic capabilities. Not only could continuous monitoring technologies alert a user when a hypoglycemic episode or other blood glucose excursion is underway, but measurements may also provide sufficient information to predict near-future blood glucose values. A predictive capability based only on recent blood glucose history would be advantageous because there would be no need to involve models of glucose and insulin distribution, with their inherent requirement for detailed accounting of vascular glucose loads and insulin availability. Published data analyzed here indicate that blood glucose dynamics are not random, and that blood glucose values can be predicted, at least for the near future, from frequently sampled previous values. Data useful in further exploring this concept are limited, however, and an appeal is made for collection of more.

  18. Skill and relative economic value of medium-range hydrological ensemble predictions

    Directory of Open Access Journals (Sweden)

    E. Roulin

    2007-01-01

    Full Text Available A hydrological ensemble prediction system, integrating a water balance model with ensemble precipitation forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF Ensemble Prediction System (EPS, is evaluated for two Belgian catchments using verification methods borrowed from meteorology. The skill of the probability forecast that the streamflow exceeds a given level is measured with the Brier Skill Score. Then the value of the system is assessed using a cost-loss decision model. The verification results of the hydrological ensemble predictions are compared with the corresponding results obtained for simpler alternatives as the one obtained by using of the deterministic forecast of ECMWF which is characterized by a higher spatial resolution or by using of the EPS ensemble mean.

  19. Proving the ecosystem value through hydrological modelling

    Science.gov (United States)

    Dorner, W.; Spachinger, K.; Porter, M.; Metzka, R.

    2008-11-01

    Ecosystems provide valuable functions. Also natural floodplains and river structures offer different types of ecosystem functions such as habitat function, recreational area and natural detention. From an economic stand point the loss (or rehabilitation) of these natural systems and their provided natural services can be valued as a damage (or benefit). Consequently these natural goods and services must be economically valued in project assessments e.g. cost-benefit-analysis or cost comparison. Especially in smaller catchments and river systems exists significant evidence that natural flood detention reduces flood risk and contributes to flood protection. Several research projects evaluated the mitigating effect of land use, river training and the loss of natural flood plains on development, peak and volume of floods. The presented project analysis the hypothesis that ignoring natural detention and hydrological ecosystem services could result in economically inefficient solutions for flood protection and mitigation. In test areas, subcatchments of the Danube in Germany, a combination of hydrological and hydrodynamic models with economic evaluation techniques was applied. Different forms of land use, river structure and flood protection measures were assed and compared from a hydrological and economic point of view. A hydrodynamic model was used to simulate flows to assess the extent of flood affected areas and damages to buildings and infrastructure as well as to investigate the impacts of levees and river structure on a local scale. These model results provided the basis for an economic assessment. Different economic valuation techniques, such as flood damage functions, cost comparison method and substation-approach were used to compare the outcomes of different hydrological scenarios from an economic point of view and value the ecosystem service. The results give significant evidence that natural detention must be evaluated as part of flood mitigation projects

  20. Proving the ecosystem value through hydrological modelling

    International Nuclear Information System (INIS)

    Dorner, W; Spachinger, K; Metzka, R; Porter, M

    2008-01-01

    Ecosystems provide valuable functions. Also natural floodplains and river structures offer different types of ecosystem functions such as habitat function, recreational area and natural detention. From an economic stand point the loss (or rehabilitation) of these natural systems and their provided natural services can be valued as a damage (or benefit). Consequently these natural goods and services must be economically valued in project assessments e.g. cost-benefit-analysis or cost comparison. Especially in smaller catchments and river systems exists significant evidence that natural flood detention reduces flood risk and contributes to flood protection. Several research projects evaluated the mitigating effect of land use, river training and the loss of natural flood plains on development, peak and volume of floods. The presented project analysis the hypothesis that ignoring natural detention and hydrological ecosystem services could result in economically inefficient solutions for flood protection and mitigation. In test areas, subcatchments of the Danube in Germany, a combination of hydrological and hydrodynamic models with economic evaluation techniques was applied. Different forms of land use, river structure and flood protection measures were assed and compared from a hydrological and economic point of view. A hydrodynamic model was used to simulate flows to assess the extent of flood affected areas and damages to buildings and infrastructure as well as to investigate the impacts of levees and river structure on a local scale. These model results provided the basis for an economic assessment. Different economic valuation techniques, such as flood damage functions, cost comparison method and substation-approach were used to compare the outcomes of different hydrological scenarios from an economic point of view and value the ecosystem service. The results give significant evidence that natural detention must be evaluated as part of flood mitigation projects

  1. Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers.

    Science.gov (United States)

    Crossa, José; Campos, Gustavo de Los; Pérez, Paulino; Gianola, Daniel; Burgueño, Juan; Araus, José Luis; Makumbi, Dan; Singh, Ravi P; Dreisigacker, Susanne; Yan, Jianbing; Arief, Vivi; Banziger, Marianne; Braun, Hans-Joachim

    2010-10-01

    The availability of dense molecular markers has made possible the use of genomic selection (GS) for plant breeding. However, the evaluation of models for GS in real plant populations is very limited. This article evaluates the performance of parametric and semiparametric models for GS using wheat (Triticum aestivum L.) and maize (Zea mays) data in which different traits were measured in several environmental conditions. The findings, based on extensive cross-validations, indicate that models including marker information had higher predictive ability than pedigree-based models. In the wheat data set, and relative to a pedigree model, gains in predictive ability due to inclusion of markers ranged from 7.7 to 35.7%. Correlation between observed and predictive values in the maize data set achieved values up to 0.79. Estimates of marker effects were different across environmental conditions, indicating that genotype × environment interaction is an important component of genetic variability. These results indicate that GS in plant breeding can be an effective strategy for selecting among lines whose phenotypes have yet to be observed.

  2. Belief about nicotine selectively modulates value and reward prediction error signals in smokers.

    Science.gov (United States)

    Gu, Xiaosi; Lohrenz, Terry; Salas, Ramiro; Baldwin, Philip R; Soltani, Alireza; Kirk, Ulrich; Cinciripini, Paul M; Montague, P Read

    2015-02-24

    Little is known about how prior beliefs impact biophysically described processes in the presence of neuroactive drugs, which presents a profound challenge to the understanding of the mechanisms and treatments of addiction. We engineered smokers' prior beliefs about the presence of nicotine in a cigarette smoked before a functional magnetic resonance imaging session where subjects carried out a sequential choice task. Using a model-based approach, we show that smokers' beliefs about nicotine specifically modulated learning signals (value and reward prediction error) defined by a computational model of mesolimbic dopamine systems. Belief of "no nicotine in cigarette" (compared with "nicotine in cigarette") strongly diminished neural responses in the striatum to value and reward prediction errors and reduced the impact of both on smokers' choices. These effects of belief could not be explained by global changes in visual attention and were specific to value and reward prediction errors. Thus, by modulating the expression of computationally explicit signals important for valuation and choice, beliefs can override the physical presence of a potent neuroactive compound like nicotine. These selective effects of belief demonstrate that belief can modulate model-based parameters important for learning. The implications of these findings may be far ranging because belief-dependent effects on learning signals could impact a host of other behaviors in addiction as well as in other mental health problems.

  3. Predicting and Modeling RNA Architecture

    Science.gov (United States)

    Westhof, Eric; Masquida, Benoît; Jossinet, Fabrice

    2011-01-01

    SUMMARY A general approach for modeling the architecture of large and structured RNA molecules is described. The method exploits the modularity and the hierarchical folding of RNA architecture that is viewed as the assembly of preformed double-stranded helices defined by Watson-Crick base pairs and RNA modules maintained by non-Watson-Crick base pairs. Despite the extensive molecular neutrality observed in RNA structures, specificity in RNA folding is achieved through global constraints like lengths of helices, coaxiality of helical stacks, and structures adopted at the junctions of helices. The Assemble integrated suite of computer tools allows for sequence and structure analysis as well as interactive modeling by homology or ab initio assembly with possibilities for fitting within electronic density maps. The local key role of non-Watson-Crick pairs guides RNA architecture formation and offers metrics for assessing the accuracy of three-dimensional models in a more useful way than usual root mean square deviation (RMSD) values. PMID:20504963

  4. Modeling the value of strategic actions in the superior colliculus

    Directory of Open Access Journals (Sweden)

    Dhushan Thevarajah

    2010-02-01

    Full Text Available In learning models of strategic game play, an agent constructs a valuation (action value over possible future choices as a function of past actions and rewards. Choices are then stochastic functions of these action values. Our goal is to uncover a neural signal that correlates with the action value posited by behavioral learning models. We measured activity from neurons in the superior colliculus (SC, a midbrain region involved in planning saccadic eye movements, in monkeys while they performed two saccade tasks. In the strategic task, monkeys competed against a computer in a saccade version of the mixed-strategy game “matching-pennies”. In the instructed task, stochastic saccades were elicited through explicit instruction rather than free choices. In both tasks, neuronal activity and behavior were shaped by past actions and rewards with more recent events exerting a larger influence. Further, SC activity predicted upcoming choices during the strategic task and upcoming reaction times during the instructed task. Finally, we found that neuronal activity in both tasks correlated with an established learning model, the Experience Weighted Attraction model of action valuation (Ho, Camerer, and Chong, 2007. Collectively, our results provide evidence that action values hypothesized by learning models are represented in the motor planning regions of the brain in a manner that could be used to select strategic actions.

  5. Modelling of physical properties - databases, uncertainties and predictive power

    DEFF Research Database (Denmark)

    Gani, Rafiqul

    Physical and thermodynamic property in the form of raw data or estimated values for pure compounds and mixtures are important pre-requisites for performing tasks such as, process design, simulation and optimization; computer aided molecular/mixture (product) design; and, product-process analysis...... in the estimated/predicted property values, how to assess the quality and reliability of the estimated/predicted property values? The paper will review a class of models for prediction of physical and thermodynamic properties of organic chemicals and their mixtures based on the combined group contribution – atom...

  6. Cardiovascular disease prediction: do pulmonary disease-related chest CT features have added value?

    International Nuclear Information System (INIS)

    Jairam, Pushpa M.; Jong, Pim A. de; Mali, Willem P.T.M.; Isgum, Ivana; Graaf, Yolanda van der

    2015-01-01

    Certain pulmonary diseases are associated with cardiovascular disease (CVD). Therefore we investigated the incremental predictive value of pulmonary, mediastinal and pleural features over cardiovascular imaging findings. A total of 10,410 patients underwent diagnostic chest CT for non-cardiovascular indications. Using a case-cohort approach, we visually graded CTs from the cases and from an approximately 10 % random sample of the baseline cohort (n = 1,203) for cardiovascular, pulmonary, mediastinal and pleural findings. The incremental value of pulmonary disease-related CT findings above cardiovascular imaging findings in cardiovascular event risk prediction was quantified by comparing discrimination and reclassification. During a mean follow-up of 3.7 years (max. 7.0 years), 1,148 CVD events (cases) were identified. Addition of pulmonary, mediastinal and pleural features to a cardiovascular imaging findings-based prediction model led to marginal improvement of discrimination (increase in c-index from 0.72 (95 % CI 0.71-0.74) to 0.74 (95 % CI 0.72-0.75)) and reclassification measures (net reclassification index 6.5 % (p < 0.01)). Pulmonary, mediastinal and pleural features have limited predictive value in the identification of subjects at high risk of CVD events beyond cardiovascular findings on diagnostic chest CT scans. (orig.)

  7. Prediction of Genomic Breeding Values for feed efficiency and related traits in pigs

    DEFF Research Database (Denmark)

    Do, Duy Ngoc; Janss, Luc; Strathe, Anders Bjerring

    2014-01-01

    .31-0.32 for DFI and RFI, respectively, and approx. 1.5% higher than that of GBLUP method. However , BPL models are more biased than GBLUP method for both traits and use of different power parameters has no effect on predictive ability of the models. Partitioning of genetic variance showed that SNP groups either...... by position (intron, exon, downstream, upstream and 5’UTR) or by function (missense and protein-altering) have similar average explained variance per SNP, except 3’UTR SNPs which explain approx. 3 times more variance then SNPs in the other groups. This study supports use of BPL models for both GWAS...... with different power parameters to investigate genetic architecture of RFI, to predict genomic breeding values, and to partition genetic variances for different SNP groups. Data were 1272 Duroc pigs with both genotypic and phenotypic records for RFI as well as daily feed intake (DFI). The gene mapping confirmed...

  8. Estimation of partial least squares regression prediction uncertainty when the reference values carry a sizeable measurement error

    NARCIS (Netherlands)

    Fernandez Pierna, J.A.; Lin, L.; Wahl, F.; Faber, N.M.; Massart, D.L.

    2003-01-01

    The prediction uncertainty is studied when using a multivariate partial least squares regression (PLSR) model constructed with reference values that contain a sizeable measurement error. Several approximate expressions for calculating a sample-specific standard error of prediction have been proposed

  9. Prediction of cereal feed value using spectroscopy and chemometrics

    DEFF Research Database (Denmark)

    Jørgensen, Johannes Ravn; Gislum, René

    2009-01-01

    Feed value in form of FEsv (Feed unit / kg dry matter, for piglets) and FEso (Feed unit / kg dry matter, for sows), EDOM (Enzyme Degradable Organic Matter) and EDOMi (Enzyme Degradable Organic Matter, Ileum) is used in the feed evaluation system for pigs. Analysis of feed value have highlighted t...

  10. Predictive Models for Normal Fetal Cardiac Structures.

    Science.gov (United States)

    Krishnan, Anita; Pike, Jodi I; McCarter, Robert; Fulgium, Amanda L; Wilson, Emmanuel; Donofrio, Mary T; Sable, Craig A

    2016-12-01

    Clinicians rely on age- and size-specific measures of cardiac structures to diagnose cardiac disease. No universally accepted normative data exist for fetal cardiac structures, and most fetal cardiac centers do not use the same standards. The aim of this study was to derive predictive models for Z scores for 13 commonly evaluated fetal cardiac structures using a large heterogeneous population of fetuses without structural cardiac defects. The study used archived normal fetal echocardiograms in representative fetuses aged 12 to 39 weeks. Thirteen cardiac dimensions were remeasured by a blinded echocardiographer from digitally stored clips. Studies with inadequate imaging views were excluded. Regression models were developed to relate each dimension to estimated gestational age (EGA) by dates, biparietal diameter, femur length, and estimated fetal weight by the Hadlock formula. Dimension outcomes were transformed (e.g., using the logarithm or square root) as necessary to meet the normality assumption. Higher order terms, quadratic or cubic, were added as needed to improve model fit. Information criteria and adjusted R 2 values were used to guide final model selection. Each Z-score equation is based on measurements derived from 296 to 414 unique fetuses. EGA yielded the best predictive model for the majority of dimensions; adjusted R 2 values ranged from 0.72 to 0.893. However, each of the other highly correlated (r > 0.94) biometric parameters was an acceptable surrogate for EGA. In most cases, the best fitting model included squared and cubic terms to introduce curvilinearity. For each dimension, models based on EGA provided the best fit for determining normal measurements of fetal cardiac structures. Nevertheless, other biometric parameters, including femur length, biparietal diameter, and estimated fetal weight provided results that were nearly as good. Comprehensive Z-score results are available on the basis of highly predictive models derived from gestational

  11. Structural features that predict real-value fluctuations of globular proteins.

    Science.gov (United States)

    Jamroz, Michal; Kolinski, Andrzej; Kihara, Daisuke

    2012-05-01

    It is crucial to consider dynamics for understanding the biological function of proteins. We used a large number of molecular dynamics (MD) trajectories of nonhomologous proteins as references and examined static structural features of proteins that are most relevant to fluctuations. We examined correlation of individual structural features with fluctuations and further investigated effective combinations of features for predicting the real value of residue fluctuations using the support vector regression (SVR). It was found that some structural features have higher correlation than crystallographic B-factors with fluctuations observed in MD trajectories. Moreover, SVR that uses combinations of static structural features showed accurate prediction of fluctuations with an average Pearson's correlation coefficient of 0.669 and a root mean square error of 1.04 Å. This correlation coefficient is higher than the one observed in predictions by the Gaussian network model (GNM). An advantage of the developed method over the GNMs is that the former predicts the real value of fluctuation. The results help improve our understanding of relationships between protein structure and fluctuation. Furthermore, the developed method provides a convienient practial way to predict fluctuations of proteins using easily computed static structural features of proteins. Copyright © 2012 Wiley Periodicals, Inc.

  12. Large-scale genomic prediction using singular value decomposition of the genotype matrix.

    Science.gov (United States)

    Ødegård, Jørgen; Indahl, Ulf; Strandén, Ismo; Meuwissen, Theo H E

    2018-02-28

    For marker effect models and genomic animal models, computational requirements increase with the number of loci and the number of genotyped individuals, respectively. In the latter case, the inverse genomic relationship matrix (GRM) is typically needed, which is computationally demanding to compute for large datasets. Thus, there is a great need for dimensionality-reduction methods that can analyze massive genomic data. For this purpose, we developed reduced-dimension singular value decomposition (SVD) based models for genomic prediction. Fast SVD is performed by analyzing different chromosomes/genome segments in parallel and/or by restricting SVD to a limited core of genotyped individuals, producing chromosome- or segment-specific principal components (PC). Given a limited effective population size, nearly all the genetic variation can be effectively captured by a limited number of PC. Genomic prediction can then be performed either by PC ridge regression (PCRR) or by genomic animal models using an inverse GRM computed from the chosen PC (PCIG). In the latter case, computation of the inverse GRM will be feasible for any number of genotyped individuals and can be readily produced row- or element-wise. Using simulated data, we show that PCRR and PCIG models, using chromosome-wise SVD of a core sample of individuals, are appropriate for genomic prediction in a larger population, and results in virtually identical predicted breeding values as the original full-dimension genomic model (r = 1.000). Compared with other algorithms (e.g. algorithm for proven and young animals, APY), the (chromosome-wise SVD-based) PCRR and PCIG models were more robust to size of the core sample, giving nearly identical results even down to 500 core individuals. The method was also successfully tested on a large multi-breed dataset. SVD can be used for dimensionality reduction of large genomic datasets. After SVD, genomic prediction using dense genomic data and many genotyped individuals

  13. A model to predict the beginning of the pollen season

    DEFF Research Database (Denmark)

    Toldam-Andersen, Torben Bo

    1991-01-01

    In order to predict the beginning of the pollen season, a model comprising the Utah phenoclirnatography Chill Unit (CU) and ASYMCUR-Growing Degree Hour (GDH) submodels were used to predict the first bloom in Alms, Ulttirrs and Berirln. The model relates environmental temperatures to rest completion...... and bud development. As phenologic parameter 14 years of pollen counts were used. The observed datcs for the beginning of the pollen seasons were defined from the pollen counts and compared with the model prediction. The CU and GDH submodels were used as: 1. A fixed day model, using only the GDH model...... for fruit trees are generally applicable, and give a reasonable description of the growth processes of other trees. This type of model can therefore be of value in predicting the start of the pollen season. The predicted dates were generally within 3-5 days of the observed. Finally the possibility of frost...

  14. Prediction of Skid Resistance Value of Glass Fiber-Reinforced Tiling Materials

    Directory of Open Access Journals (Sweden)

    Sadik Alper Yildizel

    2017-01-01

    Full Text Available This research focuses on the use of adaptive artificial neural network system for evaluating the skid resistance value (British Pendulum Number; BPN of the glass fiber-reinforced tiling materials. During the creation of the neural model, four main factors were considered: fiber, calcium carbonate content, sand blasting, and polishing properties of the specimens. The model was trained, tested, and compared with the on-site test results. As per the comparison of the outcomes of the study, the analysis and on-site test results showed that there is a great potential for the prediction of BPN of glass fiber-reinforced tiling materials by using developed neural system.

  15. Iowa calibration of MEPDG performance prediction models.

    Science.gov (United States)

    2013-06-01

    This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement : performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 : representative p...

  16. Predictive equations for spirometric reference values in a healthy ...

    African Journals Online (AJOL)

    Participants performed spirometry and answered questionnaires regarding respiratory symptoms and socioeconomic conditions. Anthropometric data were collected. Selection of subjects for generation of reference values followed American Thoracic Society (ATS) recommendations. Data were analyzed using multiple ...

  17. Model complexity control for hydrologic prediction

    NARCIS (Netherlands)

    Schoups, G.; Van de Giesen, N.C.; Savenije, H.H.G.

    2008-01-01

    A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore

  18. Predictive Value Of Biochemical Markers In Pregnancy Induced ...

    African Journals Online (AJOL)

    The aim of this study was to identify predictive markers for early diagnosis of women who are at risk of gestational hypertension or preeclampsia. ... All cases were subjected to the estimation of human chorionic gonadotropin (hCG), tumor necrosis factor alpha (TNF-α), C-reactive protein (CRP), nitric oxide (NO) and the lipid ...

  19. The predictive value of proteinuria in acute pancreatitis

    NARCIS (Netherlands)

    Zuidema, M. J.; van Santvoort, H. C.; Besselink, M. G.; van Ramshorst, B.; Boerma, D.; Timmer, R.; Bollen, T. L.; Weusten, B. L. A. M.

    2014-01-01

    Acute pancreatitis has a highly variable clinical course. Early and reliable predictors for the severity of acute pancreatitis are lacking. Proteinuria appears to be a useful predictor of disease severity and outcome in a variety of clinical conditions. This study aims to investigate the predictive

  20. Issues in Value-at-Risk Modeling and Evaluation

    NARCIS (Netherlands)

    J. Daníelsson (Jón); C.G. de Vries (Casper); B.N. Jorgensen (Bjørn); P.F. Christoffersen (Peter); F.X. Diebold (Francis); T. Schuermann (Til); J.A. Lopez (Jose); B. Hirtle (Beverly)

    1998-01-01

    textabstractDiscusses the issues in value-at-risk modeling and evaluation. Value of value at risk; Horizon problems and extreme events in financial risk management; Methods of evaluating value-at-risk estimates.

  1. Comparison of Predictive Models for the Early Diagnosis of Diabetes.

    Science.gov (United States)

    Jahani, Meysam; Mahdavi, Mahdi

    2016-04-01

    This study develops neural network models to improve the prediction of diabetes using clinical and lifestyle characteristics. Prediction models were developed using a combination of approaches and concepts. We used memetic algorithms to update weights and to improve prediction accuracy of models. In the first step, the optimum amount for neural network parameters such as momentum rate, transfer function, and error function were obtained through trial and error and based on the results of previous studies. In the second step, optimum parameters were applied to memetic algorithms in order to improve the accuracy of prediction. This preliminary analysis showed that the accuracy of neural networks is 88%. In the third step, the accuracy of neural network models was improved using a memetic algorithm and resulted model was compared with a logistic regression model using a confusion matrix and receiver operating characteristic curve (ROC). The memetic algorithm improved the accuracy from 88.0% to 93.2%. We also found that memetic algorithm had a higher accuracy than the model from the genetic algorithm and a regression model. Among models, the regression model has the least accuracy. For the memetic algorithm model the amount of sensitivity, specificity, positive predictive value, negative predictive value, and ROC are 96.2, 95.3, 93.8, 92.4, and 0.958 respectively. The results of this study provide a basis to design a Decision Support System for risk management and planning of care for individuals at risk of diabetes.

  2. Episodic memories predict adaptive value-based decision-making

    Science.gov (United States)

    Murty, Vishnu; FeldmanHall, Oriel; Hunter, Lindsay E.; Phelps, Elizabeth A; Davachi, Lila

    2016-01-01

    Prior research illustrates that memory can guide value-based decision-making. For example, previous work has implicated both working memory and procedural memory (i.e., reinforcement learning) in guiding choice. However, other types of memories, such as episodic memory, may also influence decision-making. Here we test the role for episodic memory—specifically item versus associative memory—in supporting value-based choice. Participants completed a task where they first learned the value associated with trial unique lotteries. After a short delay, they completed a decision-making task where they could choose to re-engage with previously encountered lotteries, or new never before seen lotteries. Finally, participants completed a surprise memory test for the lotteries and their associated values. Results indicate that participants chose to re-engage more often with lotteries that resulted in high versus low rewards. Critically, participants not only formed detailed, associative memories for the reward values coupled with individual lotteries, but also exhibited adaptive decision-making only when they had intact associative memory. We further found that the relationship between adaptive choice and associative memory generalized to more complex, ecologically valid choice behavior, such as social decision-making. However, individuals more strongly encode experiences of social violations—such as being treated unfairly, suggesting a bias for how individuals form associative memories within social contexts. Together, these findings provide an important integration of episodic memory and decision-making literatures to better understand key mechanisms supporting adaptive behavior. PMID:26999046

  3. 21 CFR 868.1890 - Predictive pulmonary-function value calculator.

    Science.gov (United States)

    2010-04-01

    ... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Predictive pulmonary-function value calculator. 868.1890 Section 868.1890 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN... pulmonary-function value calculator. (a) Identification. A predictive pulmonary-function value calculator is...

  4. Climate predictability and prediction skill on seasonal time scales over South America from CHFP models

    Science.gov (United States)

    Osman, Marisol; Vera, C. S.

    2017-10-01

    This work presents an assessment of the predictability and skill of climate anomalies over South America. The study was made considering a multi-model ensemble of seasonal forecasts for surface air temperature, precipitation and regional circulation, from coupled global circulation models included in the Climate Historical Forecast Project. Predictability was evaluated through the estimation of the signal-to-total variance ratio while prediction skill was assessed computing anomaly correlation coefficients. Both indicators present over the continent higher values at the tropics than at the extratropics for both, surface air temperature and precipitation. Moreover, predictability and prediction skill for temperature are slightly higher in DJF than in JJA while for precipitation they exhibit similar levels in both seasons. The largest values of predictability and skill for both variables and seasons are found over northwestern South America while modest but still significant values for extratropical precipitation at southeastern South America and the extratropical Andes. The predictability levels in ENSO years of both variables are slightly higher, although with the same spatial distribution, than that obtained considering all years. Nevertheless, predictability at the tropics for both variables and seasons diminishes in both warm and cold ENSO years respect to that in all years. The latter can be attributed to changes in signal rather than in the noise. Predictability and prediction skill for low-level winds and upper-level zonal winds over South America was also assessed. Maximum levels of predictability for low-level winds were found were maximum mean values are observed, i.e. the regions associated with the equatorial trade winds, the midlatitudes westerlies and the South American Low-Level Jet. Predictability maxima for upper-level zonal winds locate where the subtropical jet peaks. Seasonal changes in wind predictability are observed that seem to be related to

  5. A Bayesian method and its variational approximation for prediction of genomic breeding values in multiple traits

    Directory of Open Access Journals (Sweden)

    Hayashi Takeshi

    2013-01-01

    Full Text Available Abstract Background Genomic selection is an effective tool for animal and plant breeding, allowing effective individual selection without phenotypic records through the prediction of genomic breeding value (GBV. To date, genomic selection has focused on a single trait. However, actual breeding often targets multiple correlated traits, and, therefore, joint analysis taking into consideration the correlation between traits, which might result in more accurate GBV prediction than analyzing each trait separately, is suitable for multi-trait genomic selection. This would require an extension of the prediction model for single-trait GBV to multi-trait case. As the computational burden of multi-trait analysis is even higher than that of single-trait analysis, an effective computational method for constructing a multi-trait prediction model is also needed. Results We described a Bayesian regression model incorporating variable selection for jointly predicting GBVs of multiple traits and devised both an MCMC iteration and variational approximation for Bayesian estimation of parameters in this multi-trait model. The proposed Bayesian procedures with MCMC iteration and variational approximation were referred to as MCBayes and varBayes, respectively. Using simulated datasets of SNP genotypes and phenotypes for three traits with high and low heritabilities, we compared the accuracy in predicting GBVs between multi-trait and single-trait analyses as well as between MCBayes and varBayes. The results showed that, compared to single-trait analysis, multi-trait analysis enabled much more accurate GBV prediction for low-heritability traits correlated with high-heritability traits, by utilizing the correlation structure between traits, while the prediction accuracy for uncorrelated low-heritability traits was comparable or less with multi-trait analysis in comparison with single-trait analysis depending on the setting for prior probability that a SNP has zero

  6. Fixed recurrence and slip models better predict earthquake behavior than the time- and slip-predictable models 1: repeating earthquakes

    Science.gov (United States)

    Rubinstein, Justin L.; Ellsworth, William L.; Chen, Kate Huihsuan; Uchida, Naoki

    2012-01-01

    The behavior of individual events in repeating earthquake sequences in California, Taiwan and Japan is better predicted by a model with fixed inter-event time or fixed slip than it is by the time- and slip-predictable models for earthquake occurrence. Given that repeating earthquakes are highly regular in both inter-event time and seismic moment, the time- and slip-predictable models seem ideally suited to explain their behavior. Taken together with evidence from the companion manuscript that shows similar results for laboratory experiments we conclude that the short-term predictions of the time- and slip-predictable models should be rejected in favor of earthquake models that assume either fixed slip or fixed recurrence interval. This implies that the elastic rebound model underlying the time- and slip-predictable models offers no additional value in describing earthquake behavior in an event-to-event sense, but its value in a long-term sense cannot be determined. These models likely fail because they rely on assumptions that oversimplify the earthquake cycle. We note that the time and slip of these events is predicted quite well by fixed slip and fixed recurrence models, so in some sense they are time- and slip-predictable. While fixed recurrence and slip models better predict repeating earthquake behavior than the time- and slip-predictable models, we observe a correlation between slip and the preceding recurrence time for many repeating earthquake sequences in Parkfield, California. This correlation is not found in other regions, and the sequences with the correlative slip-predictable behavior are not distinguishable from nearby earthquake sequences that do not exhibit this behavior.

  7. Incremental value of the CT coronary calcium score for the prediction of coronary artery disease

    International Nuclear Information System (INIS)

    Genders, Tessa S.S.; Pugliese, Francesca; Mollet, Nico R.; Meijboom, W. Bob; Weustink, Annick C.; Mieghem, Carlos A.G. van; Feyter, Pim J. de; Hunink, M.G.M.

    2010-01-01

    To validate published prediction models for the presence of obstructive coronary artery disease (CAD) in patients with new onset stable typical or atypical angina pectoris and to assess the incremental value of the CT coronary calcium score (CTCS). We searched the literature for clinical prediction rules for the diagnosis of obstructive CAD, defined as ≥50% stenosis in at least one vessel on conventional coronary angiography. Significant variables were re-analysed in our dataset of 254 patients with logistic regression. CTCS was subsequently included in the models. The area under the receiver operating characteristic curve (AUC) was calculated to assess diagnostic performance. Re-analysing the variables used by Diamond and Forrester yielded an AUC of 0.798, which increased to 0.890 by adding CTCS. For Pryor, Morise 1994, Morise 1997 and Shaw the AUC increased from 0.838 to 0.901, 0.831 to 0.899, 0.840 to 0.898 and 0.833 to 0.899. CTCS significantly improved model performance in each model. Validation demonstrated good diagnostic performance across all models. CTCS improves the prediction of the presence of obstructive CAD, independent of clinical predictors, and should be considered in its diagnostic work-up. (orig.)

  8. Staying Power of Churn Prediction Models

    NARCIS (Netherlands)

    Risselada, Hans; Verhoef, Peter C.; Bijmolt, Tammo H. A.

    In this paper, we study the staying power of various churn prediction models. Staying power is defined as the predictive performance of a model in a number of periods after the estimation period. We examine two methods, logit models and classification trees, both with and without applying a bagging

  9. [The value of 5-HTT gene polymorphism for the assessment and prediction of male adolescence violence].

    Science.gov (United States)

    Yu, Yue; Liu, Xiang; Yang, Zhen-xing; Qiu, Chang-jian; Ma, Xiao-hong

    2012-08-01

    To establish an adolescent violence crime prediction model, and to assess the value of serotonin transporter (5-HTT) gene polymorphism for the assessment and prediction of violent crime. Investigative tools were used to analyze the difference in personality dimensions, social support, coping styles, aggressiveness, impulsivity, and family condition scale between 223 adolescents with violence behavior and 148 adolescents without violence behavior. The distribution of 5-HTT gene polymorphisms (5-HTTLPR and 5-HTTVNTR) was compared between the two groups. The role of 5-HTT gene polymorphism on adolescent personality, impulsion and aggression scale also was also analyzed. Stepwise logistic regression was used to establish a predictive model for adolescent violent crime. Significant difference was found between the violence group and the control group on multiple dimensions of psychology and environment scales. However, no statistical difference was found with regard to the 5-HTT genotypes and alleles between adolescents with violent behaviors and normal controls. The rate of prediction accuracy was not significantly improved when 5-HTT gene polymorphism was taken into the model. The violent crime of adolescents was closely related with social and environmental factors. No association was found between 5-HTT polymorphisms and adolescent violence criminal behavior.

  10. Predictive value of prostate specific antigen in a European HIV-positive cohort

    DEFF Research Database (Denmark)

    Shepherd, Leah; Borges, Álvaro H; Ravn, Lene

    2016-01-01

    BACKGROUND: It is common practice to use prostate specific antigen (PSA) ≥4.0 ng/ml as a clinical indicator for men at risk of prostate cancer (PCa), however, this is unverified in HIV+ men. We aimed to describe kinetics and predictive value of PSA for PCa in HIV+ men. METHODS: A nested case...... control study of 21 men with PCa and 40 matched-controls within EuroSIDA was conducted. Prospectively stored plasma samples before PCa (or matched date in controls) were measured for the following markers: total PSA (tPSA), free PSA (fPSA), testosterone and sex hormone binding globulin (SHBG). Conditional...... logistic regression models investigated associations between markers and PCa. Mixed models were used to describe kinetics. Sensitivity and specificity of using tPSA >4 ng/ml to predict PCa was calculated. Receiver operating characteristic curves were used to identify optimal cutoffs in HIV+ men for total...

  11. Burnout in radiation therapists: the predictive value of selected stressors

    International Nuclear Information System (INIS)

    Akroyd, Duane; Caison, Amy; Adams, Robert D.

    2002-01-01

    Purpose: As cancer caregivers, radiation therapists experience a variety of stresses that may develop into burnout, which has been demonstrated to impact patient care, employee health, and organizational effectiveness. The purpose of the study was to assess the levels of radiation therapists' burnout at three stages. Additionally, the ability of selected workplace variables to predict each of the three stages of burnout was examined. Methods and Materials: We used descriptive and inferential statistical analyses on reliable and valid instruments, which measured stress, burnout, and social support. Results: Radiation therapists have high levels of the first two stages of burnout: emotional exhaustion and depersonalization. Although personal stress, organizational stress, guidance, reassurance of worth, and work load predicted 50% or more of the variance in emotional exhaustion and depersonalization, their predictive ability for personal accomplishment was low. Conclusion: Efforts to alleviate burnout among radiation therapists within an organization should have positive effects, including increased quality of patient care, improved quality of work life, higher levels of job satisfaction, and commitment and lower staff turnover

  12. Cost/benefit and predictive value of radioimmunoassay

    International Nuclear Information System (INIS)

    Albertini, A.; Ekins, R.P.; Galen, R.S.

    1984-01-01

    The present symposium is organized to discuss the benefits of radioimmunoassay. The discussion includes several aspects: the real diagnostic values of the measurements; the way of organization to maximise the diagnostic reliability and usefulness whilst minimizing the real costs; the prospects existing for the improvement of current methods and, implicitly, of cost/benefit ratios. (Auth.)

  13. FPGA implementation of predictive degradation model for engine oil lifetime

    Science.gov (United States)

    Idros, M. F. M.; Razak, A. H. A.; Junid, S. A. M. Al; Suliman, S. I.; Halim, A. K.

    2018-03-01

    This paper presents the implementation of linear regression model for degradation prediction on Register Transfer Logic (RTL) using QuartusII. A stationary model had been identified in the degradation trend for the engine oil in a vehicle in time series method. As for RTL implementation, the degradation model is written in Verilog HDL and the data input are taken at a certain time. Clock divider had been designed to support the timing sequence of input data. At every five data, a regression analysis is adapted for slope variation determination and prediction calculation. Here, only the negative value are taken as the consideration for the prediction purposes for less number of logic gate. Least Square Method is adapted to get the best linear model based on the mean values of time series data. The coded algorithm has been implemented on FPGA for validation purposes. The result shows the prediction time to change the engine oil.

  14. The Advancement Value Chain: An Exploratory Model

    Science.gov (United States)

    Leonard, Edward F., III

    2005-01-01

    Since the introduction of the value chain concept in 1985, several varying, yet virtually similar, value chains have been developed for the business enterprise. Shifting to higher education, can a value chain be found that links together the various activities of advancement so that an institution's leaders can actually look at the philanthropic…

  15. Genome-Enabled Prediction of Breeding Values for Feedlot Average Daily Weight Gain in Nelore Cattle

    Directory of Open Access Journals (Sweden)

    Adriana L. Somavilla

    2017-06-01

    Full Text Available Nelore is the most economically important cattle breed in Brazil, and the use of genetically improved animals has contributed to increased beef production efficiency. The Brazilian beef feedlot industry has grown considerably in the last decade, so the selection of animals with higher growth rates on feedlot has become quite important. Genomic selection (GS could be used to reduce generation intervals and improve the rate of genetic gains. The aim of this study was to evaluate the prediction of genomic-estimated breeding values (GEBV for average daily weight gain (ADG in 718 feedlot-finished Nelore steers. Analyses of three Bayesian model specifications [Bayesian GBLUP (BGBLUP, BayesA, and BayesCπ] were performed with four genotype panels [Illumina BovineHD BeadChip, TagSNPs, and GeneSeek High- and Low-density indicus (HDi and LDi, respectively]. Estimates of Pearson correlations, regression coefficients, and mean squared errors were used to assess accuracy and bias of predictions. Overall, the BayesCπ model resulted in less biased predictions. Accuracies ranged from 0.18 to 0.27, which are reasonable values given the heritability estimates (from 0.40 to 0.44 and sample size (568 animals in the training population. Furthermore, results from Bos taurus indicus panels were as informative as those from Illumina BovineHD, indicating that they could be used to implement GS at lower costs.

  16. NIRS prediction of the feed value of temperate forages: efficacy of four calibration strategies.

    Science.gov (United States)

    Andueza, D; Picard, F; Jestin, M; Andrieu, J; Baumont, R

    2011-05-01

    Near infrared reflectance spectroscopy (NIRS) of 924 fresh temperate forages were used to develop calibration models for chemical composition - crude ash (CA) and crude protein (CP) - organic matter digestibility (OMD) and voluntary intake (VI). We used 110 samples to assess the models. Four calibration strategies for determining forage quality were compared: (i) species-specific calibration, (ii) family-specific calibration, (iii) a global procedure and (iv) a local approach. Forage calibration data sets displayed CA values ranging from 52 to 205 g/kg of dry matter (DM), CP values from 50 to 280 g/kg DM, OMD values from 0.48 to 0.85 g/g and VI values from 22.5 to 115.2 g DM/kg metabolic body weight (BW0.75). The calibration models performed well for all the variables except for VI. For CA, local procedure showed lower standard error of prediction (SEP) than species-specific, family-specific or global models. For CP, the calibration models all showed similar SEP values (11.13, 11.08, 11.38 and 11.34 g/kg DM for species-specific, family-specific, global and local approaches). For OMD, the local procedure gave a similar SEP (0.024 g/g) to specific species and global procedures (0.027 g/g) and a lower SEP than the family-specific approach (0.028 g/g). For VI, the local approach and species-specific calibration showed lower SEP (7.08 and 7.16 g/kg BW0.75) than the broad-based calibrations (8.09 and 8.34 g/kg BW0.75 for family-specific model and global procedure, respectively). Local calibration may thus offer a practical way to develop robust universal equations for animal response determinations.

  17. Comparison of Prediction-Error-Modelling Criteria

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Jørgensen, Sten Bay

    2007-01-01

    Single and multi-step prediction-error-methods based on the maximum likelihood and least squares criteria are compared. The prediction-error methods studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model, which is a r...

  18. Prediction of chemical composition and peroxide value in unground pet foods by near-infrared spectroscopy.

    Science.gov (United States)

    De Marchi, M; Righi, F; Meneghesso, M; Manfrin, D; Ricci, R

    2018-02-01

    The massive development of the pet food industry in recent years has lead to the formulation of hundreds of canine and feline complete extruded foods with the objective of meeting both the needs of the animals and numerous demands from pet owners. In the meantime, highly variable raw material compositions and the industry's new production techniques oblige manufacturers to monitor all phases of the extrusion process closely in order to ensure the targeted composition and quality of the products. This study aimed at evaluating the potential of infrared technology (visible and near-infrared spectrophotometer; 570-1842 nm) in predicting the chemical composition and peroxide value (PV) of unground commercial extruded dog foods. Six hundred and forty-nine commercial extruded dog foods were collected. For each product, an unground aliquot was analysed by infrared instrument while a second aliquot was sent to a laboratory for proximate analysis and PV quantification. The wide range of extruded dog food typologies included in the study was responsible for the wide variability observed within each nutritional trait, especially crude fibre and ash. The mean value of the 208 pet foods sampled for PV quantification was 17.49 mEq O 2 /kg fat (min 2.2 and max 94.10 mEq O 2 /kg fat). The coefficients of determination in cross-validation of NIRS prediction models were 0.77, 0.97, 0.83, 0.86, 0.78 and 0.94 for moisture, crude protein, crude fat, crude fibre, ash and nitrogen-free extract (NFE) respectively. PV prediction was less precise, as demonstrated by the coefficient of determination in cross-validation (0.66). The results demonstrated the potential of NIRS in predicting chemical composition in unground samples, with lower accuracy for moisture and ash, while PV prediction models suggest use for screening purposes only. © 2016 Blackwell Verlag GmbH.

  19. Predicting blood transfusion using automated analysis of pulse oximetry signals and laboratory values.

    Science.gov (United States)

    Shackelford, Stacy; Yang, Shiming; Hu, Peter; Miller, Catriona; Anazodo, Amechi; Galvagno, Samuel; Wang, Yulei; Hartsky, Lauren; Fang, Raymond; Mackenzie, Colin

    2015-10-01

    Identification of hemorrhaging trauma patients and prediction of blood transfusion needs in near real time will expedite care of the critically injured. We hypothesized that automated analysis of pulse oximetry signals in combination with laboratory values and vital signs obtained at the time of triage would predict the need for blood transfusion with accuracy greater than that of triage vital signs or pulse oximetry analysis alone. Continuous pulse oximetry signals were recorded for directly admitted trauma patients with abnormal prehospital shock index (heart rate [HR] / systolic blood pressure) of 0.62 or greater. Predictions of blood transfusion within 24 hours were compared using Delong's method for area under the receiver operating characteristic (AUROC) curves to determine the optimal combination of triage vital signs (prehospital HR + systolic blood pressure), pulse oximetry features (40 waveform features, O2 saturation, HR), and laboratory values (hematocrit, electrolytes, bicarbonate, prothrombin time, international normalization ratio, lactate) in multivariate logistic regression models. We enrolled 1,191 patients; 339 were excluded because of incomplete data; 40 received blood within 3 hours; and 14 received massive transfusion. Triage vital signs predicted need for transfusion within 3 hours (AUROC, 0.59) and massive transfusion (AUROC, 0.70). Pulse oximetry for 15 minutes predicted transfusion more accurately than triage vital signs for both time frames (3-hour AUROC, 0.74; p = 0.004) (massive transfusion AUROC, 0.88; p transfusion prediction (3-hour AUROC, 0.84; p transfusion AUROC, 0.91; p blood transfusion during trauma resuscitation more accurately than triage vital signs or pulse oximetry analysis alone. Results suggest automated calculations from a noninvasive vital sign monitor interfaced with a point-of-care laboratory device may support clinical decisions by recognizing patients with hemorrhage sufficient to need transfusion. Epidemiologic

  20. Inferential ecosystem models, from network data to prediction

    Science.gov (United States)

    James S. Clark; Pankaj Agarwal; David M. Bell; Paul G. Flikkema; Alan Gelfand; Xuanlong Nguyen; Eric Ward; Jun. Yang

    2011-01-01

    Recent developments suggest that predictive modeling could begin to play a larger role not only for data analysis, but also for data collection. We address the example of efficient wireless sensor networks, where inferential ecosystem models can be used to weigh the value of an observation against the cost of data collection. Transmission costs make observations ‘‘...

  1. Multi-model ensemble schemes for predicting northeast monsoon ...

    Indian Academy of Sciences (India)

    An attempt has been made to improve the accuracy of predicted rainfall using three different multi-model ensemble (MME) schemes, viz., simple arithmetic mean of models (EM), principal component regression (PCR) and singular value decomposition based multiple linear regressions (SVD). It is found out that among ...

  2. Predictive value of radioculography in patients with lumbago-sciatica

    International Nuclear Information System (INIS)

    Espersen, J.O.; Kosteljanetz, M.; Halaburt, H.; Miletic, T.

    1984-01-01

    One hundred patients with symptoms of lumbo-sacral root compression were prospectively and consecutively assigned to operation based alone on clinical findings. A preoperative myelogram was performed in all patients and described without a knowledge of the clinical features. All patients were explored for the clinically and myelographically relevant disc. When the myelogram was normal (16 patients) both lower lumbar interspaces were exposed. In 58 patients a herniated disc was revealed at surgery. Only 'myelographic herniation' with indentation of the contrast column was accompanied by a high frequency of disc herniation at surgery (73-87%). In cases with normal myelograms only 5% had a disc herniation. The severity of the myelographic finding was clearly correlated to the frequency of positive surgical findings and good outcomes. The preoperative radiculogram gives a high degree of certainty in the preoperative evaluation whether a surgical lesion is present or not and reveals a precise prediction of the outcome of surgery. (Author)

  3. Assessment of performance of survival prediction models for cancer prognosis

    Directory of Open Access Journals (Sweden)

    Chen Hung-Chia

    2012-07-01

    Full Text Available Abstract Background Cancer survival studies are commonly analyzed using survival-time prediction models for cancer prognosis. A number of different performance metrics are used to ascertain the concordance between the predicted risk score of each patient and the actual survival time, but these metrics can sometimes conflict. Alternatively, patients are sometimes divided into two classes according to a survival-time threshold, and binary classifiers are applied to predict each patient’s class. Although this approach has several drawbacks, it does provide natural performance metrics such as positive and negative predictive values to enable unambiguous assessments. Methods We compare the survival-time prediction and survival-time threshold approaches to analyzing cancer survival studies. We review and compare common performance metrics for the two approaches. We present new randomization tests and cross-validation methods to enable unambiguous statistical inferences for several performance metrics used with the survival-time prediction approach. We consider five survival prediction models consisting of one clinical model, two gene expression models, and two models from combinations of clinical and gene expression models. Results A public breast cancer dataset was used to compare several performance metrics using five prediction models. 1 For some prediction models, the hazard ratio from fitting a Cox proportional hazards model was significant, but the two-group comparison was insignificant, and vice versa. 2 The randomization test and cross-validation were generally consistent with the p-values obtained from the standard performance metrics. 3 Binary classifiers highly depended on how the risk groups were defined; a slight change of the survival threshold for assignment of classes led to very different prediction results. Conclusions 1 Different performance metrics for evaluation of a survival prediction model may give different conclusions in

  4. Combining GPS measurements and IRI model predictions

    International Nuclear Information System (INIS)

    Hernandez-Pajares, M.; Juan, J.M.; Sanz, J.; Bilitza, D.

    2002-01-01

    The free electrons distributed in the ionosphere (between one hundred and thousands of km in height) produce a frequency-dependent effect on Global Positioning System (GPS) signals: a delay in the pseudo-orange and an advance in the carrier phase. These effects are proportional to the columnar electron density between the satellite and receiver, i.e. the integrated electron density along the ray path. Global ionospheric TEC (total electron content) maps can be obtained with GPS data from a network of ground IGS (international GPS service) reference stations with an accuracy of few TEC units. The comparison with the TOPEX TEC, mainly measured over the oceans far from the IGS stations, shows a mean bias and standard deviation of about 2 and 5 TECUs respectively. The discrepancies between the STEC predictions and the observed values show an RMS typically below 5 TECUs (which also includes the alignment code noise). he existence of a growing database 2-hourly global TEC maps and with resolution of 5x2.5 degrees in longitude and latitude can be used to improve the IRI prediction capability of the TEC. When the IRI predictions and the GPS estimations are compared for a three month period around the Solar Maximum, they are in good agreement for middle latitudes. An over-determination of IRI TEC has been found at the extreme latitudes, the IRI predictions being, typically two times higher than the GPS estimations. Finally, local fits of the IRI model can be done by tuning the SSN from STEC GPS observations

  5. Calibration of PMIS pavement performance prediction models.

    Science.gov (United States)

    2012-02-01

    Improve the accuracy of TxDOTs existing pavement performance prediction models through calibrating these models using actual field data obtained from the Pavement Management Information System (PMIS). : Ensure logical performance superiority patte...

  6. Predictive Model Assessment for Count Data

    National Research Council Canada - National Science Library

    Czado, Claudia; Gneiting, Tilmann; Held, Leonhard

    2007-01-01

    .... In case studies, we critique count regression models for patent data, and assess the predictive performance of Bayesian age-period-cohort models for larynx cancer counts in Germany. Key words: Calibration...

  7. Modeling and Prediction Using Stochastic Differential Equations

    DEFF Research Database (Denmark)

    Juhl, Rune; Møller, Jan Kloppenborg; Jørgensen, John Bagterp

    2016-01-01

    deterministic and can predict the future perfectly. A more realistic approach would be to allow for randomness in the model due to e.g., the model be too simple or errors in input. We describe a modeling and prediction setup which better reflects reality and suggests stochastic differential equations (SDEs......) for modeling and forecasting. It is argued that this gives models and predictions which better reflect reality. The SDE approach also offers a more adequate framework for modeling and a number of efficient tools for model building. A software package (CTSM-R) for SDE-based modeling is briefly described....... that describes the variation between subjects. The ODE setup implies that the variation for a single subject is described by a single parameter (or vector), namely the variance (covariance) of the residuals. Furthermore the prediction of the states is given as the solution to the ODEs and hence assumed...

  8. Comparison of perceived value structural models

    OpenAIRE

    Sunčana Piri Rajh

    2012-01-01

    Perceived value has been considered an important determinant of consumer shopping behavior and studied as such for a long period of time. According to one research stream, perceived value is a variable determined by perceived quality and perceived sacrifice. Another research stream suggests that the perception of value is a result of the consumer risk perception. This implies the presence of two somewhat independent research streams that are integrated by a third research stream – the one sug...

  9. Getting off on the right foot: subjective value versus economic value in predicting longitudinal job outcomes from job offer negotiations.

    Science.gov (United States)

    Curhan, Jared R; Elfenbein, Hillary Anger; Kilduff, Gavin J

    2009-03-01

    Although negotiation experiences can affect a negotiator's ensuing attitudes and behavior, little is known about their long-term consequences. Using a longitudinal survey design, the authors tested the degree to which economic and subjective value achieved in job offer negotiations predicts employees' subsequent job attitudes and intentions concerning turnover. Results indicate that subjective value predicts greater compensation satisfaction and job satisfaction and lower turnover intention measured 1 year later. Surprisingly, the economic outcomes that negotiators achieved had no apparent effects on these factors. Implications, limitations, and future directions are discussed. (c) 2009 APA, all rights reserved.

  10. Probing for the Multiplicative Term in Modern Expectancy-Value Theory: A Latent Interaction Modeling Study

    Science.gov (United States)

    Trautwein, Ulrich; Marsh, Herbert W.; Nagengast, Benjamin; Ludtke, Oliver; Nagy, Gabriel; Jonkmann, Kathrin

    2012-01-01

    In modern expectancy-value theory (EVT) in educational psychology, expectancy and value beliefs additively predict performance, persistence, and task choice. In contrast to earlier formulations of EVT, the multiplicative term Expectancy x Value in regression-type models typically plays no major role in educational psychology. The present study…

  11. Predictive models for arteriovenous fistula maturation.

    Science.gov (United States)

    Al Shakarchi, Julien; McGrogan, Damian; Van der Veer, Sabine; Sperrin, Matthew; Inston, Nicholas

    2016-05-07

    Haemodialysis (HD) is a lifeline therapy for patients with end-stage renal disease (ESRD). A critical factor in the survival of renal dialysis patients is the surgical creation of vascular access, and international guidelines recommend arteriovenous fistulas (AVF) as the gold standard of vascular access for haemodialysis. Despite this, AVFs have been associated with high failure rates. Although risk factors for AVF failure have been identified, their utility for predicting AVF failure through predictive models remains unclear. The objectives of this review are to systematically and critically assess the methodology and reporting of studies developing prognostic predictive models for AVF outcomes and assess them for suitability in clinical practice. Electronic databases were searched for studies reporting prognostic predictive models for AVF outcomes. Dual review was conducted to identify studies that reported on the development or validation of a model constructed to predict AVF outcome following creation. Data were extracted on study characteristics, risk predictors, statistical methodology, model type, as well as validation process. We included four different studies reporting five different predictive models. Parameters identified that were common to all scoring system were age and cardiovascular disease. This review has found a small number of predictive models in vascular access. The disparity between each study limits the development of a unified predictive model.

  12. Model Predictive Control Fundamentals | Orukpe | Nigerian Journal ...

    African Journals Online (AJOL)

    Model Predictive Control (MPC) has developed considerably over the last two decades, both within the research control community and in industries. MPC strategy involves the optimization of a performance index with respect to some future control sequence, using predictions of the output signal based on a process model, ...

  13. Unreachable Setpoints in Model Predictive Control

    DEFF Research Database (Denmark)

    Rawlings, James B.; Bonné, Dennis; Jørgensen, John Bagterp

    2008-01-01

    In this work, a new model predictive controller is developed that handles unreachable setpoints better than traditional model predictive control methods. The new controller induces an interesting fast/slow asymmetry in the tracking response of the system. Nominal asymptotic stability of the optim...

  14. Predictive value of ocular trauma score in open globe combat eye injuries

    International Nuclear Information System (INIS)

    Islam, Q.

    2016-01-01

    Prediction of final visual outcome in ocular injuries is of paramount importance and various prognostic models have been proposed to predict final visual outcome. The objective of this study was to validate the predictive value of ocular trauma score (OTS) in patients with combat related open globe injuries and to evaluate the factors affecting the final visual outcome. Methods: Data of 93 patients admitted in AFIO Rawalpindi between Jan 2010 to June 2014 with combat related open globe ocular injuries was analysed. Initial and final best corrected visual acuity (BCVA) was categorized as No Light Perception (NLP), Light Perception (LP) to Hand Movement (HM), 1/200-19/200, 20/200-20/50, and =20/40. OTS was calculated for each eye by assigning numerical raw points to six variables and then scores were stratified into five OTS categories. Results: Mean age of study population was 28.77 ± 8.37 years. Presenting visual acuity was <20/200 (6/60) in 103 (96.23%) eyes. However, final BCVA of =20/40 (6/12) was achieved in 18 (16.82%) eyes, while 72 (67.28%) eyes had final BCVA of <20/200 (6/60). Final visual outcome in our study were similar to those in OTS study, except for NLP in category 1 (81% vs. 74%) and =20/40 in category 3 (30% vs. 41%). The OTS model predicted visual survival (LP or better) with a sensitivity of 94.80% and predicted no vision (NLP) with a specificity of 100%. Conclusion: OTS is a reliable tool for assessment of ocular injuries and predicting final visual outcome at the outset. (author)

  15. Predictive values of symptoms in relation to cancer diagnosis

    DEFF Research Database (Denmark)

    Krasnik, Ivan; Andersen, John Sahl

    /ulceration” and ”Clinically suspect axillary lymph nodes” was not found in the literature. Prostate cancer: One study shows a high PPV for rectal examination (12%). The value of “Lower urinary tract symptoms” is more uncertain (1,0%-3,0%). PPV of ”Perianal pain” and ”Haemospermia” are not described in the literature. Lung...... cancer: For “Haemoptysis” a high PPV for elderly patients was found (8,4%-20,4%). PPV of “Cough”, ”Pain in the thorax”, ”Dyspnoea” and ”General symptoms” are small (0,4-1,1%).. Conclusion: A few of the “alarm symptoms” show high PPVs. For many symptoms the PPV is not known. To improve diagnostic judgment...

  16. Predictive value of nerve trunk size in the neonate.

    Science.gov (United States)

    Rassouli-Kirchmeier, Roxana; Lok, Maarten Janssen; Kusters, Benno; Nagtegaal, Iris; Köster, Nils; van der Steeg, Herjan; Wijnen, Marc; de Blaauw, Ivo

    2014-08-01

    The diagnosis of Hirschsprung's disease (HD) remains challenging. The identification of ganglion cells is difficult and acetycholine esterase (AChE) staining can be subject to a great variability, particularly in the neonatal period (trunks greater than 40 µm are considered to be predictive for HD. The aim of this study was to evaluate the usefulness of measuring nerve trunk size in the newborn with HD. Out of 292 biopsies 69 could be reanalyzed by three independent researchers. 40 µm was used as cutoff point for nerve trunk size. They were subdivided into three groups: (a) diagnosis of HD certain at the first biopsy, (b) no HD and (c) diagnosis of HD remains doubtful and re-biopsy taken. In 87 % of group A nerve trunk size was ≥ 40 µm (SD 13.8). In 84 % of group B trunk size was trunk size. Using 40 µm as the cutoff point gave 13 % false-negative and 16 % false-positive cases. Measurement of the nerve trunk in the neonatal period does not seem to be a reliable method for detecting HD.

  17. Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database.

    Science.gov (United States)

    Wessler, Benjamin S; Lai Yh, Lana; Kramer, Whitney; Cangelosi, Michael; Raman, Gowri; Lutz, Jennifer S; Kent, David M

    2015-07-01

    Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease, there are numerous CPMs available although the extent of this literature is not well described. We conducted a systematic review for articles containing CPMs for cardiovascular disease published between January 1990 and May 2012. Cardiovascular disease includes coronary heart disease, heart failure, arrhythmias, stroke, venous thromboembolism, and peripheral vascular disease. We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. Seven hundred seventeen (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions, including 215 CPMs for patients with coronary artery disease, 168 CPMs for population samples, and 79 models for patients with heart failure. There are 77 distinct index/outcome pairings. Of the de novo models in this database, 450 (63%) report a c-statistic and 259 (36%) report some information on calibration. There is an abundance of CPMs available for a wide assortment of cardiovascular disease conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood. © 2015 American Heart Association, Inc.

  18. Hybrid approaches to physiologic modeling and prediction

    Science.gov (United States)

    Olengü, Nicholas O.; Reifman, Jaques

    2005-05-01

    This paper explores how the accuracy of a first-principles physiological model can be enhanced by integrating data-driven, "black-box" models with the original model to form a "hybrid" model system. Both linear (autoregressive) and nonlinear (neural network) data-driven techniques are separately combined with a first-principles model to predict human body core temperature. Rectal core temperature data from nine volunteers, subject to four 30/10-minute cycles of moderate exercise/rest regimen in both CONTROL and HUMID environmental conditions, are used to develop and test the approach. The results show significant improvements in prediction accuracy, with average improvements of up to 30% for prediction horizons of 20 minutes. The models developed from one subject's data are also used in the prediction of another subject's core temperature. Initial results for this approach for a 20-minute horizon show no significant improvement over the first-principles model by itself.

  19. Poisson Mixture Regression Models for Heart Disease Prediction

    Science.gov (United States)

    Erol, Hamza

    2016-01-01

    Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

  20. Value of direct immunofluorescence in predicting remission in pemphigus vulgaris.

    Science.gov (United States)

    Balighi, Kamran; Taheri, Arash; Mansoori, Parisa; Chams, Cheida

    2006-11-01

    Pemphigus vulgaris is characterized by the presence of autoantibodies to desmogleins. Multiple relapses and remission may occur during the course of the disease. The goal of this study was to determine whether direct immunofluorescence study has any value in detecting immunological remission of pemphigus vulgaris. Fifty-seven patients with pemphigus vulgaris who were in clinical remission for at least 3 months, while taking prednisolone 5-7.5 mg/day, were recruited retrospectively for the study. Direct immunofluorescence study had been performed in all patients after a period of at least 3 months in clinical remission. Treatment had been discontinued in all patients with negative results of direct immunofluorescence. Of 57 patients who were in clinical remission, 24 patients (42%) had negative and 33 patients (58%) had positive results of direct immunofluorescence. Eleven patients (46%) with negative results of direct immunofluorescence relapsed within the first year of the follow-up period. Nine patients with negative direct immunofluorescence had a history of more than 6 months of clinical remission before direct immunofluorescence study. Among them, two patients (22%) relapsed. None of four patients with history of more than 12 months of clinical remission before a negative direct immunofluorescence study relapsed. Negative direct immunofluorescence is an indicator of immunological remission in patients with pemphigus vulgaris after 6-12 months in clinical remission.

  1. Prediction of pKa values using the PM6 semiempirical method

    Directory of Open Access Journals (Sweden)

    Jimmy C. Kromann

    2016-08-01

    Full Text Available The PM6 semiempirical method and the dispersion and hydrogen bond-corrected PM6-D3H+ method are used together with the SMD and COSMO continuum solvation models to predict pKa values of pyridines, alcohols, phenols, benzoic acids, carboxylic acids, and phenols using isodesmic reactions and compared to published ab initio results. The pKa values of pyridines, alcohols, phenols, and benzoic acids considered in this study can generally be predicted with PM6 and ab initio methods to within the same overall accuracy, with average mean absolute differences (MADs of 0.6–0.7 pH units. For carboxylic acids, the accuracy (0.7–1.0 pH units is also comparable to ab initio results if a single outlier is removed. For primary, secondary, and tertiary amines the accuracy is, respectively, similar (0.5–0.6, slightly worse (0.5–1.0, and worse (1.0–2.5, provided that di- and tri-ethylamine are used as reference molecules for secondary and tertiary amines. When applied to a drug-like molecule where an empirical pKa predictor exhibits a large (4.9 pH unit error, we find that the errors for PM6-based predictions are roughly the same in magnitude but opposite in sign. As a result, most of the PM6-based methods predict the correct protonation state at physiological pH, while the empirical predictor does not. The computational cost is around 2–5 min per conformer per core processor, making PM6-based pKa prediction computationally efficient enough to be used for high-throughput screening using on the order of 100 core processors.

  2. Predictive value of Sp1/Sp3/FLIP signature for prostate cancer recurrence.

    Directory of Open Access Journals (Sweden)

    Roble G Bedolla

    Full Text Available Prediction of prostate cancer prognosis is challenging and predictive biomarkers of recurrence remain elusive. Although prostate specific antigen (PSA has high sensitivity (90% at a PSA level of 4.0 ng/mL, its low specificity leads to many false positive results and considerable overtreatment of patients and its performance at lower ranges is poor. Given the histopathological and molecular heterogeneity of prostate cancer, we propose that a panel of markers will be a better tool than a single marker. We tested a panel of markers composed of the anti-apoptotic protein FLIP and its transcriptional regulators Sp1 and Sp3 using prostate tissues from 64 patients with recurrent and non-recurrent cancer who underwent radical prostatectomy as primary treatment for prostate cancer and were followed with PSA measurements for at least 5 years. Immunohistochemical staining for Sp1, Sp3, and FLIP was performed on these tissues and scored based on the proportion and intensity of staining. The predictive value of the FLIP/Sp1/Sp3 signature for clinical outcome (recurrence vs. non-recurrence was explored with logistic regression, and combinations of FLIP/Sp1/Sp3 and Gleason score were analyzed with a stepwise (backward and forward logistic model. The discrimination of the markers was identified by sensitivity-specificity analysis and the diagnostic value of FLIP/Sp1/Sp3 was determined using area under the curve (AUC for receiver operator characteristic curves. The AUCs for FLIP, Sp1, Sp3, and Gleason score for predicting PSA failure and non-failure were 0.71, 0.66, 0.68, and 0.76, respectively. However, this increased to 0.93 when combined. Thus, the "biomarker signature" of FLIP/Sp1/Sp3 combined with Gleason score predicted disease recurrence and stratified patients who are likely to benefit from more aggressive treatment.

  3. Value Reappraisal as a Conceptual Model for Task-Value Interventions

    Science.gov (United States)

    Acee, Taylor W.; Weinstein, Claire Ellen; Hoang, Theresa V.; Flaggs, Darolyn A.

    2018-01-01

    We discuss task-value interventions as one type of relevance intervention and propose a process model of value reappraisal whereby task-value interventions elicit cognitive-affective responses that lead to attitude change and in turn affect academic outcomes. The model incorporates a metacognitive component showing that students can intentionally…

  4. Serum HER-2: Sensitivity, specificity, and predictive values for detecting metastatic recurrence in breast cancer patients

    DEFF Research Database (Denmark)

    Sørensen, Patricia Diana; Jakobsen, Erik Hugger; Madsen, Jonna Skov

    2013-01-01

    The aim of this study was to determine the sensitivity, specificity, and predictive values of serum HER-2 for detecting metastatic recurrence in breast cancer patients.......The aim of this study was to determine the sensitivity, specificity, and predictive values of serum HER-2 for detecting metastatic recurrence in breast cancer patients....

  5. Prediction of hourly solar radiation with multi-model framework

    International Nuclear Information System (INIS)

    Wu, Ji; Chan, Chee Keong

    2013-01-01

    Highlights: • A novel approach to predict solar radiation through the use of clustering paradigms. • Development of prediction models based on the intrinsic pattern observed in each cluster. • Prediction based on proper clustering and selection of model on current time provides better results than other methods. • Experiments were conducted on actual solar radiation data obtained from a weather station in Singapore. - Abstract: In this paper, a novel multi-model prediction framework for prediction of solar radiation is proposed. The framework started with the assumption that there are several patterns embedded in the solar radiation series. To extract the underlying pattern, the solar radiation series is first segmented into smaller subsequences, and the subsequences are further grouped into different clusters. For each cluster, an appropriate prediction model is trained. Hence a procedure for pattern identification is developed to identify the proper pattern that fits the current period. Based on this pattern, the corresponding prediction model is applied to obtain the prediction value. The prediction result of the proposed framework is then compared to other techniques. It is shown that the proposed framework provides superior performance as compared to others

  6. Models of Affective Decision Making: How Do Feelings Predict Choice?

    Science.gov (United States)

    Charpentier, Caroline J; De Neve, Jan-Emmanuel; Li, Xinyi; Roiser, Jonathan P; Sharot, Tali

    2016-06-01

    Intuitively, how you feel about potential outcomes will determine your decisions. Indeed, an implicit assumption in one of the most influential theories in psychology, prospect theory, is that feelings govern choice. Surprisingly, however, very little is known about the rules by which feelings are transformed into decisions. Here, we specified a computational model that used feelings to predict choices. We found that this model predicted choice better than existing value-based models, showing a unique contribution of feelings to decisions, over and above value. Similar to the value function in prospect theory, our feeling function showed diminished sensitivity to outcomes as value increased. However, loss aversion in choice was explained by an asymmetry in how feelings about losses and gains were weighted when making a decision, not by an asymmetry in the feelings themselves. The results provide new insights into how feelings are utilized to reach a decision. © The Author(s) 2016.

  7. Assessment of prognostic predictive value at the mycosis fungoides

    Directory of Open Access Journals (Sweden)

    A. S. Zhukov

    2017-01-01

    Full Text Available Micosis fungoides is a primary skin lymphoma characterized with indolent disease course and favorable prognosis. Опіу at some patients one can observe aggressive development of the disease to malignant stage with the exracutaneous outspread. the modern data about the prognostic factors are presented in the review. Disclosure of these factors allows to forecast the course of disease. there is given attention to integral estimation of survival rates on the ground of tNMB-staging sand estimation of the CUP-index. Definition of combination of different prognostic factors would allow to create prognostic models enabling to diagnose on the early stages of disease the patients with high risk of progression of mycosis fungoides.

  8. Predictive value of clinical history compared with urodynamic study in 1,179 women

    Directory of Open Access Journals (Sweden)

    Jorge Milhem Haddad

    2016-02-01

    Full Text Available SUMMARY Objective: to determine the positive predictive value of clinical history in comparison with urodynamic study for the diagnosis of urinary incontinence. Methods: retrospective analysis comparing clinical history and urodynamic evaluation of 1,179 women with urinary incontinence. The urodynamic study was considered the gold standard, whereas the clinical history was the new test to be assessed. This was established after analyzing each method as the gold standard through the difference between their positive predictive values. Results: the positive predictive values of clinical history compared with urodynamic study for diagnosis of stress urinary incontinence, overactive bladder and mixed urinary incontinence were, respectively, 37% (95% CI 31-44, 40% (95% CI 33-47 and 16% (95% CI 14-19. Conclusion: we concluded that the positive predictive value of clinical history was low compared with urodynamic study for urinary incontinence diagnosis. The positive predictive value was low even among women with pure stress urinary incontinence.

  9. Traditional versus modern values and interpersonal factors predicting stress response syndromes in a Swiss elderly population.

    Science.gov (United States)

    Müller, Mario; Forstmeier, Simon; Wagner, Birgit; Maercker, Andreas

    2011-12-01

    Stress response syndromes like adjustment disorders or complicated grief are assumed to be shaped by social-cultural factors in addition to biological and psychological factors. In previous research, value orientations and interpersonal factors were found to jointly predict those syndromes (Maercker, A., Mohiyeddini, C., Müller, M., Xie, W., Hui Yang, Z., Wang, J., & Müller, J. ( 2009 ). Traditional versus modern values, self-perceived interpersonal factors, and posttraumatic stress in Chinese and German crime victims. Psychology and Psychotherapy, 82(Pt 2), 219-232.). In addition to the previous finding, the current study using Swiss elderly (65-97 years) aimed to replicate patterns of predictors leading to stress responses in people with recent bereavement or severe life events. Traditional (conformity, tradition and benevolence) and modern values (stimulation, hedonism and achievement) and two self-perceived interpersonal mediator processes (disclosure intentions and social acknowledgement as a victim) were assessed. The current study confirms the previous model in parts, that is, the indirect path from social acknowledgment to stress response syndrome is mediated by disclosure intentions. Traditional values and not modern values explained substantial variance for disclosure intentions and are therefore indirectly linked to worse mental health outcomes whereas the direct association appears somewhat controversial in light of previous findings.

  10. Predictions for mt and MW in minimal supersymmetric models

    International Nuclear Information System (INIS)

    Buchmueller, O.; Ellis, J.R.; Flaecher, H.; Isidori, G.

    2009-12-01

    Using a frequentist analysis of experimental constraints within two versions of the minimal supersymmetric extension of the Standard Model, we derive the predictions for the top quark mass, m t , and the W boson mass, m W . We find that the supersymmetric predictions for both m t and m W , obtained by incorporating all the relevant experimental information and state-of-the-art theoretical predictions, are highly compatible with the experimental values with small remaining uncertainties, yielding an improvement compared to the case of the Standard Model. (orig.)

  11. Model predictive control classical, robust and stochastic

    CERN Document Server

    Kouvaritakis, Basil

    2016-01-01

    For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplic...

  12. Survival prediction model for postoperative hepatocellular carcinoma patients.

    Science.gov (United States)

    Ren, Zhihui; He, Shasha; Fan, Xiaotang; He, Fangping; Sang, Wei; Bao, Yongxing; Ren, Weixin; Zhao, Jinming; Ji, Xuewen; Wen, Hao

    2017-09-01

    This study is to establish a predictive index (PI) model of 5-year survival rate for patients with hepatocellular carcinoma (HCC) after radical resection and to evaluate its prediction sensitivity, specificity, and accuracy.Patients underwent HCC surgical resection were enrolled and randomly divided into prediction model group (101 patients) and model evaluation group (100 patients). Cox regression model was used for univariate and multivariate survival analysis. A PI model was established based on multivariate analysis and receiver operating characteristic (ROC) curve was drawn accordingly. The area under ROC (AUROC) and PI cutoff value was identified.Multiple Cox regression analysis of prediction model group showed that neutrophil to lymphocyte ratio, histological grade, microvascular invasion, positive resection margin, number of tumor, and postoperative transcatheter arterial chemoembolization treatment were the independent predictors for the 5-year survival rate for HCC patients. The model was PI = 0.377 × NLR + 0.554 × HG + 0.927 × PRM + 0.778 × MVI + 0.740 × NT - 0.831 × transcatheter arterial chemoembolization (TACE). In the prediction model group, AUROC was 0.832 and the PI cutoff value was 3.38. The sensitivity, specificity, and accuracy were 78.0%, 80%, and 79.2%, respectively. In model evaluation group, AUROC was 0.822, and the PI cutoff value was well corresponded to the prediction model group with sensitivity, specificity, and accuracy of 85.0%, 83.3%, and 84.0%, respectively.The PI model can quantify the mortality risk of hepatitis B related HCC with high sensitivity, specificity, and accuracy.

  13. A prediction model for assessing residential radon concentration in Switzerland

    International Nuclear Information System (INIS)

    Hauri, Dimitri D.; Huss, Anke; Zimmermann, Frank; Kuehni, Claudia E.; Röösli, Martin

    2012-01-01

    Indoor radon is regularly measured in Switzerland. However, a nationwide model to predict residential radon levels has not been developed. The aim of this study was to develop a prediction model to assess indoor radon concentrations in Switzerland. The model was based on 44,631 measurements from the nationwide Swiss radon database collected between 1994 and 2004. Of these, 80% randomly selected measurements were used for model development and the remaining 20% for an independent model validation. A multivariable log-linear regression model was fitted and relevant predictors selected according to evidence from the literature, the adjusted R², the Akaike's information criterion (AIC), and the Bayesian information criterion (BIC). The prediction model was evaluated by calculating Spearman rank correlation between measured and predicted values. Additionally, the predicted values were categorised into three categories (50th, 50th–90th and 90th percentile) and compared with measured categories using a weighted Kappa statistic. The most relevant predictors for indoor radon levels were tectonic units and year of construction of the building, followed by soil texture, degree of urbanisation, floor of the building where the measurement was taken and housing type (P-values <0.001 for all). Mean predicted radon values (geometric mean) were 66 Bq/m³ (interquartile range 40–111 Bq/m³) in the lowest exposure category, 126 Bq/m³ (69–215 Bq/m³) in the medium category, and 219 Bq/m³ (108–427 Bq/m³) in the highest category. Spearman correlation between predictions and measurements was 0.45 (95%-CI: 0.44; 0.46) for the development dataset and 0.44 (95%-CI: 0.42; 0.46) for the validation dataset. Kappa coefficients were 0.31 for the development and 0.30 for the validation dataset, respectively. The model explained 20% overall variability (adjusted R²). In conclusion, this residential radon prediction model, based on a large number of measurements, was demonstrated to be

  14. Predictive value of the smell identification test for nigrostriatal dopaminergic depletion in Korean tremor patients.

    Science.gov (United States)

    Hong, Jin Yong; Chung, Seok Jong; Lee, Ji E; Sunwoo, Mun Kyung; Lee, Phil Hyu; Sohn, Young H

    2013-11-01

    The predictive value of Cross-Cultural Smell Identification Test for nigrostriatal dopaminergic depletion in Korean tremor patients has yet to be assessed. Three hundred nineteen drug-naive patients who visited our clinic for the diagnosis of their tremor, and took both Cross-Cultural Smell Identification Test and dopamine transporter PET were included in the data analysis. Visual grading of each PET image was performed by two independent neurologists. Smell test scores were significantly correlated to the striatal dopaminergic activity (Kendall's τb = -0.291, p smell test score alone appeared to have relatively weak power for predicting dopaminergic depletion (area under the curve = 0.693). Multivariate logistic regression model with inclusion of the patient's age and symptom duration as independent variables enhanced predictive power for dopaminergic depletion (area under the curve = 0.812). These results demonstrated that Cross-Cultural Smell Identification Test measurements alone may be insufficient to predict striatal dopaminergic depletion in Korean tremor patients. Copyright © 2013 Elsevier Ltd. All rights reserved.

  15. Personality, emotions and coping styles: predictive value for the evolution of cancer patients.

    Science.gov (United States)

    Cardenal, Violeta; Cerezo, M Victoria; Martínez, Joaquina; Ortiz-Tallo, Margarita; José Blanca, M

    2012-07-01

    This study had a twofold goal: to define differences in psychological aspects between cancer patients and a control group and to explore the predictive value of such aspects for the evolution of the disease two years later. Firstly, personality, anxiety, anger and depression were assessed in both groups. Results of t-analyses revealed significant group differences. In personality, cancer patients had higher levels of neuroticism and lower levels of extraversion, agreeableness and conscientiousness than the control group. In emotional variables, cancer patients had higher levels of anxiety and some aspects of anger, but there were no group differences in depression levels. Secondly, applying a quasi-prospective design, the predictive value of personality, emotions and coping styles for the evolution of cancer (favourable or unfavourable) was explored using generalized linear models and logistic regression. A four-predictor logistic model was fitted: Anger Expression-In, Resignation, Self-blame and Conscientiousness, indicating that the higher Anger Expression-in, Resignation, and Self-blame scores together with a lower Conscientiousness score, the more likely it is for patients' cancer to evolve unfavourably. These results indicate the crucial role of psychological aspects for the evolution of the disease and the need to include such aspects in the design of clinical interventions.

  16. PV panel model based on datasheet values

    DEFF Research Database (Denmark)

    Sera, Dezso; Teodorescu, Remus; Rodriguez, Pedro

    2007-01-01

    This work presents the construction of a model for a PV panel using the single-diode five-parameters model, based exclusively on data-sheet parameters. The model takes into account the series and parallel (shunt) resistance of the panel. The equivalent circuit and the basic equations of the PV cell...

  17. The New Digital Media Value Network: Proposing an Interactive Model of Digital Media Value Activities

    Directory of Open Access Journals (Sweden)

    Sylvia Chan-Olmsted

    2016-07-01

    Full Text Available This study models the dynamic nature of today’s media markets using the framework of value-adding activities in the provision and consumption of media products. The proposed user-centric approach introduces the notion that the actions of external users, social media, and interfaces affect the internal value activities of media firms via a feedback loop, and therefore should themselves be considered value activities. The model also suggests a more comprehensive list of indicators for value assessment.

  18. Demographic Factors and Hospital Size Predict Patient Satisfaction Variance- Implications for Hospital Value-Based Purchasing

    Science.gov (United States)

    McFarland, Daniel C.; Ornstein, Katherine; Holcombe, Randall F.

    2016-01-01

    Background Hospital Value-Based Purchasing (HVBP) incentivizes quality performance based healthcare by linking payments directly to patient satisfaction scores obtained from Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) surveys. Lower HCAHPS scores appear to cluster in heterogeneous population dense areas and could bias CMS reimbursement. Objective Assess nonrandom variation in patient satisfaction as determined by HCAHPS. Design Multivariate regression modeling was performed for individual dimensions of HCAHPS and aggregate scores. Standardized partial regression coefficients assessed strengths of predictors. Weighted Individual (hospital) Patient Satisfaction Adjusted Score (WIPSAS) utilized four highly predictive variables and hospitals were re-ranked accordingly. Setting 3,907 HVBP-participating hospitals. Patients 934,800 patient surveys, by most conservative estimate. Measurements 3,144 county demographics (U.S. Census), and HCAHPS. Results Hospital size and primary language (‘non-English speaking’) most strongly predicted unfavorable HCAHPS scores while education and white ethnicity most strongly predicted favorable HCAHPS scores. The average adjusted patient satisfaction scores calculated by WIPSAS approximated the national average of HCAHPS scores. However, WIPSAS changed hospital rankings by variable amounts depending on the strength of the predictive variables in the hospitals’ locations. Structural and demographic characteristics that predict lower scores were accounted for by WIPSAS that also improved rankings of many safety-net hospitals and academic medical centers in diverse areas. Conclusions Demographic and structural factors (e.g., hospital beds) predict patient satisfaction scores even after CMS adjustments. CMS should consider WIPSAS or a similar adjustment to account for the severity of patient satisfaction inequities that hospitals could strive to correct. PMID:25940305

  19. A Global Model for Bankruptcy Prediction.

    Science.gov (United States)

    Alaminos, David; Del Castillo, Agustín; Fernández, Manuel Ángel

    2016-01-01

    The recent world financial crisis has increased the number of bankruptcies in numerous countries and has resulted in a new area of research which responds to the need to predict this phenomenon, not only at the level of individual countries, but also at a global level, offering explanations of the common characteristics shared by the affected companies. Nevertheless, few studies focus on the prediction of bankruptcies globally. In order to compensate for this lack of empirical literature, this study has used a methodological framework of logistic regression to construct predictive bankruptcy models for Asia, Europe and America, and other global models for the whole world. The objective is to construct a global model with a high capacity for predicting bankruptcy in any region of the world. The results obtained have allowed us to confirm the superiority of the global model in comparison to regional models over periods of up to three years prior to bankruptcy.

  20. Modeling value creation with enterprise architecture

    NARCIS (Netherlands)

    Singh, Prince Mayurank; Jonkers, H.; Iacob, Maria Eugenia; van Sinderen, Marten J.

    2014-01-01

    Firms may not succeed in business if strategies are not properly implemented in practice. Every firm needs to know, represent and master its value creation logic, not only to stay in business but also to keep growing. This paper is about focusing on an important topic in the field of strategic

  1. The added value of business models

    NARCIS (Netherlands)

    Vliet, Harry van

    An overview of innovations in a particular area, for example retail developments in the fashion sector (Van Vliet, 2014), and a subsequent discussion about the probability as to whether these innovations will realise a ‘breakthrough’, has to be supplemented with the question of what the added value

  2. Fingerprint verification prediction model in hand dermatitis.

    Science.gov (United States)

    Lee, Chew K; Chang, Choong C; Johor, Asmah; Othman, Puwira; Baba, Roshidah

    2015-07-01

    Hand dermatitis associated fingerprint changes is a significant problem and affects fingerprint verification processes. This study was done to develop a clinically useful prediction model for fingerprint verification in patients with hand dermatitis. A case-control study involving 100 patients with hand dermatitis. All patients verified their thumbprints against their identity card. Registered fingerprints were randomized into a model derivation and model validation group. Predictive model was derived using multiple logistic regression. Validation was done using the goodness-of-fit test. The fingerprint verification prediction model consists of a major criterion (fingerprint dystrophy area of ≥ 25%) and two minor criteria (long horizontal lines and long vertical lines). The presence of the major criterion predicts it will almost always fail verification, while presence of both minor criteria and presence of one minor criterion predict high and low risk of fingerprint verification failure, respectively. When none of the criteria are met, the fingerprint almost always passes the verification. The area under the receiver operating characteristic curve was 0.937, and the goodness-of-fit test showed agreement between the observed and expected number (P = 0.26). The derived fingerprint verification failure prediction model is validated and highly discriminatory in predicting risk of fingerprint verification in patients with hand dermatitis. © 2014 The International Society of Dermatology.

  3. Massive Predictive Modeling using Oracle R Enterprise

    CERN Multimedia

    CERN. Geneva

    2014-01-01

    R is fast becoming the lingua franca for analyzing data via statistics, visualization, and predictive analytics. For enterprise-scale data, R users have three main concerns: scalability, performance, and production deployment. Oracle's R-based technologies - Oracle R Distribution, Oracle R Enterprise, Oracle R Connector for Hadoop, and the R package ROracle - address these concerns. In this talk, we introduce Oracle's R technologies, highlighting how each enables R users to achieve scalability and performance while making production deployment of R results a natural outcome of the data analyst/scientist efforts. The focus then turns to Oracle R Enterprise with code examples using the transparency layer and embedded R execution, targeting massive predictive modeling. One goal behind massive predictive modeling is to build models per entity, such as customers, zip codes, simulations, in an effort to understand behavior and tailor predictions at the entity level. Predictions...

  4. The Predictive Value of the EWGSOP Definition of Sarcopenia: Results From the InCHIANTI Study.

    Science.gov (United States)

    Bianchi, Lara; Ferrucci, Luigi; Cherubini, Antonio; Maggio, Marcello; Bandinelli, Stefania; Savino, Elisabetta; Brombo, Gloria; Zuliani, Giovanni; Guralnik, Jack M; Landi, Francesco; Volpato, Stefano

    2016-02-01

    Sarcopenia is associated with increased risk of adverse outcomes in older people. Aim of the study was to explore the predictive value of the European Working Group on Sarcopenia in Older People (EWGSOP) diagnostic algorithm in terms of disability, hospitalization, and mortality and analyze the specific role of grip strength and walking speed as diagnostic criteria for sarcopenia. Longitudinal analysis of 538 participants enrolled in the InCHIANTI study. Sarcopenia was defined as having low muscle mass plus low grip strength or low gait speed (EWGSOP criteria). Muscle mass was assessed using bioimpedance analysis. Cox proportional and logistic regression models were used to assess risk of death, hospitalization, and disability for sarcopenic people and to investigate the individual contributions of grip strength and walking speed to the predictive value of the EWGSOP's algorithm. Prevalence of EWGSOP-defined sarcopenia at baseline was 10.2%. After adjusting for potential confounders, sarcopenia was associated with disability (odds ratio 3.15; 95% confidence interval [CI] 1.41-7.05), hospitalization (hazard ratio [HR] 1.57; 95% CI 1.03-2.41), and mortality (HR 1.88; 95% CI 0.91-3.91). The association between an alternative sarcopenic phenotype, defined only by the presence of low muscle mass and low grip strength, and both disability and mortality were similar to the association with the phenotypes defined by low muscle mass and low walking speed or by the EWGSOP algorithm. The EWGSOP's phenotype is a good predictor of incident disability, hospitalization and death. Assessment of only muscle weakness, in addition to low muscle mass, provided similar predictive value as compared to the original algorithm. © The Author 2015. 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.

  5. Integrating geophysics and hydrology for reducing the uncertainty of groundwater model predictions and improved prediction performance

    DEFF Research Database (Denmark)

    Christensen, Nikolaj Kruse; Christensen, Steen; Ferre, Ty

    constructed from geological and hydrological data. However, geophysical data are increasingly used to inform hydrogeologic models because they are collected at lower cost and much higher density than geological and hydrological data. Despite increased use of geophysics, it is still unclear whether...... the integration of geophysical data in the construction of a groundwater model increases the prediction performance. We suggest that modelers should perform a hydrogeophysical “test-bench” analysis of the likely value of geophysics data for improving groundwater model prediction performance before actually...... collecting geophysical data. At a minimum, an analysis should be conducted assuming settings that are favorable for the chosen geophysical method. If the analysis suggests that data collected by the geophysical method is unlikely to improve model prediction performance under these favorable settings...

  6. Predictive Model of Systemic Toxicity (SOT)

    Science.gov (United States)

    In an effort to ensure chemical safety in light of regulatory advances away from reliance on animal testing, USEPA and L’Oréal have collaborated to develop a quantitative systemic toxicity prediction model. Prediction of human systemic toxicity has proved difficult and remains a ...

  7. Testicular Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  8. Pancreatic Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  9. Colorectal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  10. Prostate Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  11. Bladder Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  12. Esophageal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  13. Cervical Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  14. Breast Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  15. Lung Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  16. Liver Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  17. Ovarian Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  18. Posterior Predictive Model Checking in Bayesian Networks

    Science.gov (United States)

    Crawford, Aaron

    2014-01-01

    This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex…

  19. Heating value prediction for combustible fraction of municipal solid waste in Semarang using backpropagation neural network

    Science.gov (United States)

    Khuriati, Ainie; Setiabudi, Wahyu; Nur, Muhammad; Istadi, Istadi

    2015-12-01

    Backpropgation neural network was trained to predict of combustible fraction heating value of MSW from the physical composition. Waste-to-Energy (WtE) is a viable option for municipal solid waste (MSW) management. The influence of the heating value of municipal solid waste (MSW) is very important on the implementation of WtE systems. As MSW is heterogeneous material, direct heating value measurements are often not feasible. In this study an empirical model was developed to describe the heating value of the combustible fraction of municipal solid waste as a function of its physical composition of MSW using backpropagation neural network. Sampling process was carried out at Jatibarang landfill. The weight of each sorting sample taken from each discharged MSW vehicle load is 100 kg. The MSW physical components were grouped into paper wastes, absorbent hygiene product waste, styrofoam waste, HD plastic waste, plastic waste, rubber waste, textile waste, wood waste, yard wastes, kitchen waste, coco waste, and miscellaneous combustible waste. Network was trained by 24 datasets with 1200, 769, and 210 epochs. The results of this analysis showed that the correlation from the physical composition is better than multiple regression method .

  20. Uncertainties in model-based outcome predictions for treatment planning

    International Nuclear Information System (INIS)

    Deasy, Joseph O.; Chao, K.S. Clifford; Markman, Jerry

    2001-01-01

    Purpose: Model-based treatment-plan-specific outcome predictions (such as normal tissue complication probability [NTCP] or the relative reduction in salivary function) are typically presented without reference to underlying uncertainties. We provide a method to assess the reliability of treatment-plan-specific dose-volume outcome model predictions. Methods and Materials: A practical method is proposed for evaluating model prediction based on the original input data together with bootstrap-based estimates of parameter uncertainties. The general framework is applicable to continuous variable predictions (e.g., prediction of long-term salivary function) and dichotomous variable predictions (e.g., tumor control probability [TCP] or NTCP). Using bootstrap resampling, a histogram of the likelihood of alternative parameter values is generated. For a given patient and treatment plan we generate a histogram of alternative model results by computing the model predicted outcome for each parameter set in the bootstrap list. Residual uncertainty ('noise') is accounted for by adding a random component to the computed outcome values. The residual noise distribution is estimated from the original fit between model predictions and patient data. Results: The method is demonstrated using a continuous-endpoint model to predict long-term salivary function for head-and-neck cancer patients. Histograms represent the probabilities for the level of posttreatment salivary function based on the input clinical data, the salivary function model, and the three-dimensional dose distribution. For some patients there is significant uncertainty in the prediction of xerostomia, whereas for other patients the predictions are expected to be more reliable. In contrast, TCP and NTCP endpoints are dichotomous, and parameter uncertainties should be folded directly into the estimated probabilities, thereby improving the accuracy of the estimates. Using bootstrap parameter estimates, competing treatment

  1. Multiple Steps Prediction with Nonlinear ARX Models

    OpenAIRE

    Zhang, Qinghua; Ljung, Lennart

    2007-01-01

    NLARX (NonLinear AutoRegressive with eXogenous inputs) models are frequently used in black-box nonlinear system identication. Though it is easy to make one step ahead prediction with such models, multiple steps prediction is far from trivial. The main difficulty is that in general there is no easy way to compute the mathematical expectation of an output conditioned by past measurements. An optimal solution would require intensive numerical computations related to nonlinear filltering. The pur...

  2. Preprocedural Prediction Model for Contrast-Induced Nephropathy Patients.

    Science.gov (United States)

    Yin, Wen-Jun; Yi, Yi-Hu; Guan, Xiao-Feng; Zhou, Ling-Yun; Wang, Jiang-Lin; Li, Dai-Yang; Zuo, Xiao-Cong

    2017-02-03

    Several models have been developed for prediction of contrast-induced nephropathy (CIN); however, they only contain patients receiving intra-arterial contrast media for coronary angiographic procedures, which represent a small proportion of all contrast procedures. In addition, most of them evaluate radiological interventional procedure-related variables. So it is necessary for us to develop a model for prediction of CIN before radiological procedures among patients administered contrast media. A total of 8800 patients undergoing contrast administration were randomly assigned in a 4:1 ratio to development and validation data sets. CIN was defined as an increase of 25% and/or 0.5 mg/dL in serum creatinine within 72 hours above the baseline value. Preprocedural clinical variables were used to develop the prediction model from the training data set by the machine learning method of random forest, and 5-fold cross-validation was used to evaluate the prediction accuracies of the model. Finally we tested this model in the validation data set. The incidence of CIN was 13.38%. We built a prediction model with 13 preprocedural variables selected from 83 variables. The model obtained an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.907 and gave prediction accuracy of 80.8%, sensitivity of 82.7%, specificity of 78.8%, and Matthews correlation coefficient of 61.5%. For the first time, 3 new factors are included in the model: the decreased sodium concentration, the INR value, and the preprocedural glucose level. The newly established model shows excellent predictive ability of CIN development and thereby provides preventative measures for CIN. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

  3. Predictive Value of Whole Blood and Plasma Coagulation Tests for Intra- and Postoperative Bleeding Risk: A Systematic Review

    DEFF Research Database (Denmark)

    Larsen, Julie Brogaard; Hvas, Anne-Mette

    2017-01-01

    value of testing in patients receiving antithrombotic medication. In general, studies reported low positive predictive values for perioperative testing, whereas negative predictive values were high. The studies yielded moderate areas under receiver operator characteristics (ROC) curve (for the majority...

  4. Model complexity control for hydrologic prediction

    Science.gov (United States)

    Schoups, G.; van de Giesen, N. C.; Savenije, H. H. G.

    2008-12-01

    A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore needed. We compare three model complexity control methods for hydrologic prediction, namely, cross validation (CV), Akaike's information criterion (AIC), and structural risk minimization (SRM). Results show that simulation of water flow using non-physically-based models (polynomials in this case) leads to increasingly better calibration fits as the model complexity (polynomial order) increases. However, prediction uncertainty worsens for complex non-physically-based models because of overfitting of noisy data. Incorporation of physically based constraints into the model (e.g., storage-discharge relationship) effectively bounds prediction uncertainty, even as the number of parameters increases. The conclusion is that overparameterization and equifinality do not lead to a continued increase in prediction uncertainty, as long as models are constrained by such physical principles. Complexity control of hydrologic models reduces parameter equifinality and identifies the simplest model that adequately explains the data, thereby providing a means of hydrologic generalization and classification. SRM is a promising technique for this purpose, as it (1) provides analytic upper bounds on prediction uncertainty, hence avoiding the computational burden of CV, and (2) extends the applicability of classic methods such as AIC to finite data. The main hurdle in applying SRM is the need for an a priori estimation of the complexity of the hydrologic model, as measured by its Vapnik-Chernovenkis (VC) dimension. Further research is needed in this area.

  5. Validation of an internal hardwood log defect prediction model

    Science.gov (United States)

    R. Edward. Thomas

    2011-01-01

    The type, size, and location of internal defects dictate the grade and value of lumber sawn from hardwood logs. However, acquiring internal defect knowledge with x-ray/computed-tomography or magnetic-resonance imaging technology can be expensive both in time and cost. An alternative approach uses prediction models based on correlations among external defect indicators...

  6. Model Predictive Control for Dynamic Unreliable Resource Allocation

    National Research Council Canada - National Science Library

    Castanon, David

    2002-01-01

    .... The approximation is used in a model predictive control (MPC) algorithm. For single resource problems, the MPC algorithm completes over 98 percent of the task value completed by an optimal dynamic programming algorithm in over 1,000 randomly generated problems. On average, it achieves 99.5 percent of the optimal performance while requiring over 6 orders of magnitude less comnutation.

  7. Multi-model ensemble schemes for predicting northeast monsoon ...

    Indian Academy of Sciences (India)

    Northeast monsoon; multi-model ensemble; rainfall; prediction; principal component regression; single value decomposition. J. Earth Syst. Sci. 120, No. 5, October 2011, pp. 795–805 c Indian Academy of Sciences. 795 ... Rakecha 1983; Krishnan 1984; Raj and Jamadar. 1990; Sridharan and Muthusamy 1990; Singh and.

  8. A prediction method based on wavelet transform and multiple models fusion for chaotic time series

    International Nuclear Information System (INIS)

    Zhongda, Tian; Shujiang, Li; Yanhong, Wang; Yi, Sha

    2017-01-01

    In order to improve the prediction accuracy of chaotic time series, a prediction method based on wavelet transform and multiple models fusion is proposed. The chaotic time series is decomposed and reconstructed by wavelet transform, and approximate components and detail components are obtained. According to different characteristics of each component, least squares support vector machine (LSSVM) is used as predictive model for approximation components. At the same time, an improved free search algorithm is utilized for predictive model parameters optimization. Auto regressive integrated moving average model (ARIMA) is used as predictive model for detail components. The multiple prediction model predictive values are fusion by Gauss–Markov algorithm, the error variance of predicted results after fusion is less than the single model, the prediction accuracy is improved. The simulation results are compared through two typical chaotic time series include Lorenz time series and Mackey–Glass time series. The simulation results show that the prediction method in this paper has a better prediction.

  9. Measurement Error and Bias in Value-Added Models. Research Report. ETS RR-17-25

    Science.gov (United States)

    Kane, Michael T.

    2017-01-01

    By aggregating residual gain scores (the differences between each student's current score and a predicted score based on prior performance) for a school or a teacher, value-added models (VAMs) can be used to generate estimates of school or teacher effects. It is known that random errors in the prior scores will introduce bias into predictions of…

  10. Quantifying predictive accuracy in survival models.

    Science.gov (United States)

    Lirette, Seth T; Aban, Inmaculada

    2017-12-01

    For time-to-event outcomes in medical research, survival models are the most appropriate to use. Unlike logistic regression models, quantifying the predictive accuracy of these models is not a trivial task. We present the classes of concordance (C) statistics and R 2 statistics often used to assess the predictive ability of these models. The discussion focuses on Harrell's C, Kent and O'Quigley's R 2 , and Royston and Sauerbrei's R 2 . We present similarities and differences between the statistics, discuss the software options from the most widely used statistical analysis packages, and give a practical example using the Worcester Heart Attack Study dataset.

  11. Predictive power of nuclear-mass models

    Directory of Open Access Journals (Sweden)

    Yu. A. Litvinov

    2013-12-01

    Full Text Available Ten different theoretical models are tested for their predictive power in the description of nuclear masses. Two sets of experimental masses are used for the test: the older set of 2003 and the newer one of 2011. The predictive power is studied in two regions of nuclei: the global region (Z, N ≥ 8 and the heavy-nuclei region (Z ≥ 82, N ≥ 126. No clear correlation is found between the predictive power of a model and the accuracy of its description of the masses.

  12. Return Predictability, Model Uncertainty, and Robust Investment

    DEFF Research Database (Denmark)

    Lukas, Manuel

    Stock return predictability is subject to great uncertainty. In this paper we use the model confidence set approach to quantify uncertainty about expected utility from investment, accounting for potential return predictability. For monthly US data and six representative return prediction models, we...... find that confidence sets are very wide, change significantly with the predictor variables, and frequently include expected utilities for which the investor prefers not to invest. The latter motivates a robust investment strategy maximizing the minimal element of the confidence set. The robust investor...... allocates a much lower share of wealth to stocks compared to a standard investor....

  13. Comparing National Water Model Inundation Predictions with Hydrodynamic Modeling

    Science.gov (United States)

    Egbert, R. J.; Shastry, A.; Aristizabal, F.; Luo, C.

    2017-12-01

    The National Water Model (NWM) simulates the hydrologic cycle and produces streamflow forecasts, runoff, and other variables for 2.7 million reaches along the National Hydrography Dataset for the continental United States. NWM applies Muskingum-Cunge channel routing which is based on the continuity equation. However, the momentum equation also needs to be considered to obtain better estimates of streamflow and stage in rivers especially for applications such as flood inundation mapping. Simulation Program for River NeTworks (SPRNT) is a fully dynamic model for large scale river networks that solves the full nonlinear Saint-Venant equations for 1D flow and stage height in river channel networks with non-uniform bathymetry. For the current work, the steady-state version of the SPRNT model was leveraged. An evaluation on SPRNT's and NWM's abilities to predict inundation was conducted for the record flood of Hurricane Matthew in October 2016 along the Neuse River in North Carolina. This event was known to have been influenced by backwater effects from the Hurricane's storm surge. Retrospective NWM discharge predictions were converted to stage using synthetic rating curves. The stages from both models were utilized to produce flood inundation maps using the Height Above Nearest Drainage (HAND) method which uses the local relative heights to provide a spatial representation of inundation depths. In order to validate the inundation produced by the models, Sentinel-1A synthetic aperture radar data in the VV and VH polarizations along with auxiliary data was used to produce a reference inundation map. A preliminary, binary comparison of the inundation maps to the reference, limited to the five HUC-12 areas of Goldsboro, NC, yielded that the flood inundation accuracies for NWM and SPRNT were 74.68% and 78.37%, respectively. The differences for all the relevant test statistics including accuracy, true positive rate, true negative rate, and positive predictive value were found

  14. Spatial Economics Model Predicting Transport Volume

    Directory of Open Access Journals (Sweden)

    Lu Bo

    2016-10-01

    Full Text Available It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Zhuanghe as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Zhuanghe and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.

  15. Dietary information improves cardiovascular disease risk prediction models.

    Science.gov (United States)

    Baik, I; Cho, N H; Kim, S H; Shin, C

    2013-01-01

    Data are limited on cardiovascular disease (CVD) risk prediction models that include dietary predictors. Using known risk factors and dietary information, we constructed and evaluated CVD risk prediction models. Data for modeling were from population-based prospective cohort studies comprised of 9026 men and women aged 40-69 years. At baseline, all were free of known CVD and cancer, and were followed up for CVD incidence during an 8-year period. We used Cox proportional hazard regression analysis to construct a traditional risk factor model, an office-based model, and two diet-containing models and evaluated these models by calculating Akaike information criterion (AIC), C-statistics, integrated discrimination improvement (IDI), net reclassification improvement (NRI) and calibration statistic. We constructed diet-containing models with significant dietary predictors such as poultry, legumes, carbonated soft drinks or green tea consumption. Adding dietary predictors to the traditional model yielded a decrease in AIC (delta AIC=15), a 53% increase in relative IDI (P-value for IDI NRI (category-free NRI=0.14, P NRI (category-free NRI=0.08, P<0.01) compared with the office-based model. The calibration plots for risk prediction demonstrated that the inclusion of dietary predictors contributes to better agreement in persons at high risk for CVD. C-statistics for the four models were acceptable and comparable. We suggest that dietary information may be useful in constructing CVD risk prediction models.

  16. The predictive performance and stability of six species distribution models.

    Science.gov (United States)

    Duan, Ren-Yan; Kong, Xiao-Quan; Huang, Min-Yi; Fan, Wei-Yi; Wang, Zhi-Gao

    2014-01-01

    Predicting species' potential geographical range by species distribution models (SDMs) is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs. We collected the distribution data of five common tree species (Pinus massoniana, Betula platyphylla, Quercus wutaishanica, Quercus mongolica and Quercus variabilis) and simulated their potential distribution area using 13 environmental variables and six widely used SDMs: BIOCLIM, DOMAIN, MAHAL, RF, MAXENT, and SVM. Each model run was repeated 100 times (trials). We compared the predictive performance by testing the consistency between observations and simulated distributions and assessed the stability by the standard deviation, coefficient of variation, and the 99% confidence interval of Kappa and AUC values. The mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (pSDMs (MAHAL, RF, MAXENT, and SVM) had higher prediction accuracy, smaller confidence intervals, and were more stable and less affected by the random variable (randomly selected pseudo-absence points). According to the prediction performance and stability of SDMs, we can divide these six SDMs into two categories: a high performance and stability group including MAHAL, RF, MAXENT, and SVM, and a low performance and stability group consisting of BIOCLIM, and DOMAIN. We highlight that choosing appropriate SDMs to address a specific problem is an important part of the modeling process.

  17. Accuracy assessment of landslide prediction models

    International Nuclear Information System (INIS)

    Othman, A N; Mohd, W M N W; Noraini, S

    2014-01-01

    The increasing population and expansion of settlements over hilly areas has greatly increased the impact of natural disasters such as landslide. Therefore, it is important to developed models which could accurately predict landslide hazard zones. Over the years, various techniques and models have been developed to predict landslide hazard zones. The aim of this paper is to access the accuracy of landslide prediction models developed by the authors. The methodology involved the selection of study area, data acquisition, data processing and model development and also data analysis. The development of these models are based on nine different landslide inducing parameters i.e. slope, land use, lithology, soil properties, geomorphology, flow accumulation, aspect, proximity to river and proximity to road. Rank sum, rating, pairwise comparison and AHP techniques are used to determine the weights for each of the parameters used. Four (4) different models which consider different parameter combinations are developed by the authors. Results obtained are compared to landslide history and accuracies for Model 1, Model 2, Model 3 and Model 4 are 66.7, 66.7%, 60% and 22.9% respectively. From the results, rank sum, rating and pairwise comparison can be useful techniques to predict landslide hazard zones

  18. Predictive value and construct validity of the work functioning screener-healthcare (WFS-H)

    Science.gov (United States)

    Boezeman, Edwin J.; Nieuwenhuijsen, Karen; Sluiter, Judith K.

    2016-01-01

    Objectives: To test the predictive value and convergent construct validity of a 6-item work functioning screener (WFS-H). Methods: Healthcare workers (249 nurses) completed a questionnaire containing the work functioning screener (WFS-H) and a work functioning instrument (NWFQ) measuring the following: cognitive aspects of task execution and general incidents, avoidance behavior, conflicts and irritation with colleagues, impaired contact with patients and their family, and level of energy and motivation. Productivity and mental health were also measured. Negative and positive predictive values, AUC values, and sensitivity and specificity were calculated to examine the predictive value of the screener. Correlation analysis was used to examine the construct validity. Results: The screener had good predictive value, since the results showed that a negative screener score is a strong indicator of work functioning not hindered by mental health problems (negative predictive values: 94%-98%; positive predictive values: 21%-36%; AUC:.64-.82; sensitivity: 42%-76%; and specificity 85%-87%). The screener has good construct validity due to moderate, but significant (pvalue and good construct validity. Its score offers occupational health professionals a helpful preliminary insight into the work functioning of healthcare workers. PMID:27010085

  19. Predictive modeling of coupled multi-physics systems: I. Theory

    International Nuclear Information System (INIS)

    Cacuci, Dan Gabriel

    2014-01-01

    Highlights: • We developed “predictive modeling of coupled multi-physics systems (PMCMPS)”. • PMCMPS reduces predicted uncertainties in predicted model responses and parameters. • PMCMPS treats efficiently very large coupled systems. - Abstract: This work presents an innovative mathematical methodology for “predictive modeling of coupled multi-physics systems (PMCMPS).” This methodology takes into account fully the coupling terms between the systems but requires only the computational resources that would be needed to perform predictive modeling on each system separately. The PMCMPS methodology uses the maximum entropy principle to construct an optimal approximation of the unknown a priori distribution based on a priori known mean values and uncertainties characterizing the parameters and responses for both multi-physics models. This “maximum entropy”-approximate a priori distribution is combined, using Bayes’ theorem, with the “likelihood” provided by the multi-physics simulation models. Subsequently, the posterior distribution thus obtained is evaluated using the saddle-point method to obtain analytical expressions for the optimally predicted values for the multi-physics models parameters and responses along with corresponding reduced uncertainties. Noteworthy, the predictive modeling methodology for the coupled systems is constructed such that the systems can be considered sequentially rather than simultaneously, while preserving exactly the same results as if the systems were treated simultaneously. Consequently, very large coupled systems, which could perhaps exceed available computational resources if treated simultaneously, can be treated with the PMCMPS methodology presented in this work sequentially and without any loss of generality or information, requiring just the resources that would be needed if the systems were treated sequentially

  20. Validation of Water Erosion Prediction Project (WEPP) model for low-volume forest roads

    Science.gov (United States)

    William Elliot; R. B. Foltz; Charlie Luce

    1995-01-01

    Erosion rates of recently graded nongravel forest roads were measured under rainfall simulation on five different soils. The erosion rates observed on 24 forest road erosion plots were compared with values predicted by the Water Erosion Prediction Project (WEPP) Model, Version 93.1. Hydraulic conductivity and soil erodibility values were predicted from methods...

  1. Predictive validation of an influenza spread model.

    Directory of Open Access Journals (Sweden)

    Ayaz Hyder

    Full Text Available BACKGROUND: Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. METHODS AND FINDINGS: We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998-1999. Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type. Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. CONCLUSIONS: Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve

  2. Predictive Validation of an Influenza Spread Model

    Science.gov (United States)

    Hyder, Ayaz; Buckeridge, David L.; Leung, Brian

    2013-01-01

    Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive

  3. The predictive value of bronchial histamine challenge in the diagnosis of bronchial asthma

    DEFF Research Database (Denmark)

    Madsen, F; Holstein-Rathlou, N H; Mosbech, H

    1985-01-01

    was below 0.125 mg/ml the predictive value of a positive test was 1.00, but an increase in PC20 in the range from 4.00 to 16 mg/ml did not increase the predictive value of a negative test. In this study the prevalence of asthma was about 0.6. We therefore conclude that bronchial histamine challenge...... is a valuable test for detection and exclusion of bronchial asthma, when the prevalence of the disease is high. In populations with a lower frequency of bronchial asthma the diagnostic value of a positive bronchial challenge will be negligible.......A prospective survey aiming to study the predictive value of bronchial histamine challenge was performed on 151 patients with a forced expiratory volume1 (FEV1) above 60% of predicted. According to variations in peak expiratory flow rate (PEFR) and medical history the patients were classified...

  4. Prognostic value of tissue Doppler imaging for predicting ventricular arrhythmias and cardiovascular mortality in ischaemic cardiomyopathy

    DEFF Research Database (Denmark)

    Biering-Sørensen, Tor; Olsen, Flemming Javier; Storm, Katrine

    2016-01-01

    AIMS: Only 30% of patients receiving an implantable cardioverter defibrillator (ICD) for primary prevention receive appropriately therapy. We sought to investigate the value of tissue Doppler imaging (TDI) to predict ventricular tachycardia (VT), ventricular fibrillation (VF), and cardiovascular...

  5. Characterization and Predictive Value of Segmental Curve Flexibility in Adolescent Idiopathic Scoliosis Patients

    DEFF Research Database (Denmark)

    Yao, Guanfeng; Cheung, Jason P Y; Shigematsu, Hideki

    2017-01-01

    STUDY DESIGN: A prospective radiographic analysis of adolescent idiopathic scoliosis (AIS) patients managed with alternate-level pedicle screw fixation was performed. OBJECTIVE: The objective of this study was to characterize segmental curve flexibility and to determine its predictive value...

  6. Modeling churn using customer lifetime value

    OpenAIRE

    Glady, Nicolas; Baesens, Bart; Croux, Christophe

    2009-01-01

    The definition and modeling of customer loyalty have been central issues in customer relationship management since many years. Recent papers propose solutions to detect customers that are becoming less loyal, also called churners. The churner status is then defined as a function of the volume of commercial transactions. In the context of a Belgian retail financial service company, our first contribution is to redefine the notion of customer loyalty by considering it from a customer-centric vi...

  7. Risk Prediction Models for Incident Heart Failure: A Systematic Review of Methodology and Model Performance.

    Science.gov (United States)

    Sahle, Berhe W; Owen, Alice J; Chin, Ken Lee; Reid, Christopher M

    2017-09-01

    Numerous models predicting the risk of incident heart failure (HF) have been developed; however, evidence of their methodological rigor and reporting remains unclear. This study critically appraises the methods underpinning incident HF risk prediction models. EMBASE and PubMed were searched for articles published between 1990 and June 2016 that reported at least 1 multivariable model for prediction of HF. Model development information, including study design, variable coding, missing data, and predictor selection, was extracted. Nineteen studies reporting 40 risk prediction models were included. Existing models have acceptable discriminative ability (C-statistics > 0.70), although only 6 models were externally validated. Candidate variable selection was based on statistical significance from a univariate screening in 11 models, whereas it was unclear in 12 models. Continuous predictors were retained in 16 models, whereas it was unclear how continuous variables were handled in 16 models. Missing values were excluded in 19 of 23 models that reported missing data, and the number of events per variable was models. Only 2 models presented recommended regression equations. There was significant heterogeneity in discriminative ability of models with respect to age (P prediction models that had sufficient discriminative ability, although few are externally validated. Methods not recommended for the conduct and reporting of risk prediction modeling were frequently used, and resulting algorithms should be applied with caution. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. Predictive Value of Matrix Metalloproteinases and Their Inhibitors for Mortality in Septic Patients: A Cohort Study.

    Science.gov (United States)

    Serrano-Gomez, Sergio; Burgos-Angulo, Gabriel; Niño-Vargas, Daniela Camila; Niño, María Eugenia; Cárdenas, María Eugenia; Chacón-Valenzuela, Estephania; McCosham, Diana Margarita; Peinado-Acevedo, Juan Sebastián; Lopez, M Marcos; Cunha, Fernando; Pazin-Filho, Antonio; Ilarraza, Ramses; Schulz, Richard; Torres-Dueñas, Diego

    2017-01-01

    Over 170 biomarkers are being investigated regarding their prognostic and diagnostic accuracy in sepsis in order to find new tools to reduce morbidity and mortality. Matrix metalloproteinases (MMPs) and their inhibitors have been recently studied as promising new prognostic biomarkers in patients with sepsis. This study is aimed at determining the utility of several cutoff points of these biomarkers to predict mortality in patients with sepsis. A multicenter, prospective, analytic cohort study was performed in the metropolitan area of Bucaramanga, Colombia. A total of 289 patients with sepsis and septic shock were included. MMP-9, MMP-2, tissue inhibitor of metalloproteinase 1 (TIMP-1), TIMP-2, TIMP-1/MMP-9 ratio, and TIMP-2/MMP-2 ratio were determined in blood samples. Value ranges were correlated with mortality to estimate sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiving operating characteristic curve. Sensitivity ranged from 33.3% (MMP-9/TIMP-1 ratio) to 60.6% (TIMP-1) and specificity varied from 38.8% (MMP-2/TIMP-2 ratio) to 58.5% (TIMP-1). As for predictive values, positive predictive value range was from 17.5% (MMP-9/TIMP-1 ratio) to 70.4% (MMP-2/TIMP-2 ratio), whereas negative predictive values were between 23.2% (MMP-2/TIMP-2 ratio) and 80.9% (TIMP-1). Finally, area under the curve scores ranged from 0.31 (MMP-9/TIMP-1 ratio) to 0.623 (TIMP-1). Although TIMP-1 showed higher sensitivity, specificity, and negative predictive value, with a representative population sample, we conclude that none of the evaluated biomarkers had significant predictive value for mortality.

  9. Value of routine blood tests for prediction of mortality risk in hip fracture patients

    DEFF Research Database (Denmark)

    Mosfeldt, Mathias; Pedersen, Ole Birger Vesterager; Riis, Troels

    2012-01-01

    There is a 5- to 8-fold increased risk of mortality during the first 3 months after a hip fracture. Several risk factors are known. We studied the predictive value (for mortality) of routine blood tests taken on admission.......There is a 5- to 8-fold increased risk of mortality during the first 3 months after a hip fracture. Several risk factors are known. We studied the predictive value (for mortality) of routine blood tests taken on admission....

  10. Atterberg Limits Prediction Comparing SVM with ANFIS Model

    Directory of Open Access Journals (Sweden)

    Mohammad Murtaza Sherzoy

    2017-03-01

    Full Text Available Support Vector Machine (SVM and Adaptive Neuro-Fuzzy inference Systems (ANFIS both analytical methods are used to predict the values of Atterberg limits, such as the liquid limit, plastic limit and plasticity index. The main objective of this study is to make a comparison between both forecasts (SVM & ANFIS methods. All data of 54 soil samples are used and taken from the area of Peninsular Malaysian and tested for different parameters containing liquid limit, plastic limit, plasticity index and grain size distribution and were. The input parameter used in for this case are the fraction of grain size distribution which are the percentage of silt, clay and sand. The actual and predicted values of Atterberg limit which obtained from the SVM and ANFIS models are compared by using the correlation coefficient R2 and root mean squared error (RMSE value.  The outcome of the study show that the ANFIS model shows higher accuracy than SVM model for the liquid limit (R2 = 0.987, plastic limit (R2 = 0.949 and plastic index (R2 = 0966. RMSE value that obtained for both methods have shown that the ANFIS model has represent the best performance than SVM model to predict the Atterberg Limits as a whole.

  11. Online physician ratings fail to predict actual performance on measures of quality, value, and peer review.

    Science.gov (United States)

    Daskivich, Timothy J; Houman, Justin; Fuller, Garth; Black, Jeanne T; Kim, Hyung L; Spiegel, Brennan

    2018-04-01

    Patients use online consumer ratings to identify high-performing physicians, but it is unclear if ratings are valid measures of clinical performance. We sought to determine whether online ratings of specialist physicians from 5 platforms predict quality of care, value of care, and peer-assessed physician performance. We conducted an observational study of 78 physicians representing 8 medical and surgical specialties. We assessed the association of consumer ratings with specialty-specific performance scores (metrics including adherence to Choosing Wisely measures, 30-day readmissions, length of stay, and adjusted cost of care), primary care physician peer-review scores, and administrator peer-review scores. Across ratings platforms, multivariable models showed no significant association between mean consumer ratings and specialty-specific performance scores (β-coefficient range, -0.04, 0.04), primary care physician scores (β-coefficient range, -0.01, 0.3), and administrator scores (β-coefficient range, -0.2, 0.1). There was no association between ratings and score subdomains addressing quality or value-based care. Among physicians in the lowest quartile of specialty-specific performance scores, only 5%-32% had consumer ratings in the lowest quartile across platforms. Ratings were consistent across platforms; a physician's score on one platform significantly predicted his/her score on another in 5 of 10 comparisons. Online ratings of specialist physicians do not predict objective measures of quality of care or peer assessment of clinical performance. Scores are consistent across platforms, suggesting that they jointly measure a latent construct that is unrelated to performance. Online consumer ratings should not be used in isolation to select physicians, given their poor association with clinical performance.

  12. Prediction of suitable amounts of water in fluidized bed granulation of pharmaceutical formulations using corresponding values of components.

    Science.gov (United States)

    Miwa, Akio; Yajima, Toshio; Ikuta, Hiroshi; Makado, Kouji

    2008-03-20

    This study describes application of a newly developed method to the fluidized bed granulation. The method is based on predicting suitable amounts of water to be added to multi-component formulations using the corresponding values of components prior to granulation trials. The range of appropriate amount of water for each component in a model formulation was estimated in our previous studies with a refractive near-infrared (NIR) moisture sensor. Using those values, we calculated the range of suitable amount of water to add for the model pharmaceutical formulation. In this study, we examined the relationship between the amount of water added to the model formulation and the NIR sensor output value. Then, we performed fluidized bed granulation of the model formulation at steady-state moisture content levels under monitoring with NIR sensor, within and beyond the suitable range of added water that was calculated from the corresponding range of each component. For the model formulation, we found that the predicted values for suitable amounts of added water well corresponded to those in the granulation trials, suggesting that this predictive method may be useful in estimating suitable amounts of water to be added to formulations before fluidized bed granulation trials.

  13. Variability of Automated Intraoperative ST Segment Values Predicts Postoperative Troponin Elevation.

    Science.gov (United States)

    Maile, Michael D; Engoren, Milo C; Tremper, Kevin K; Tremper, Theodore T; Jewell, Elizabeth S; Kheterpal, Sachin

    2016-03-01

    Intraoperative electrocardiographic monitoring is considered a standard of care. However, there are no evidence-based algorithms for using intraoperative ST segment data to identify patients at high risk for adverse perioperative cardiac events. Therefore, we performed an exploratory study of statistical measures summarizing intraoperative ST segment values determine whether the variability of these measurements was associated with adverse postoperative events. We hypothesized that elevation, depression, and variability of ST segments captured in an anesthesia information management system are associated with postoperative serum troponin elevation. We conducted a single-institution, retrospective study of intraoperative automated ST segment measurements from leads I, II, and III, which were recorded in the electronic anesthesia record of adult patients undergoing noncardiac surgery. The maximum, minimum, mean, and SD of ST segment values were entered into logistic regression models to find independent associations with myocardial injury, defined as an elevated serum troponin concentration during the 7 days after surgery. Performance of these models was assessed by measuring the area under the receiver operator characteristic curve. The net reclassification improvement was calculated to quantify the amount of information that the ST segment values analysis added regarding the ability to predict postoperative troponin elevation. Of 81,011 subjects, 4504 (5.6%) had postoperative myocardial injury. After adjusting for patient characteristics, the ST segment maximal depression (e.g., lead I: odds ratio [OR], 1.66; 95% confidence interval [CI], 1.26-2.19; P = 0.0004), maximal elevation (e.g., lead I: OR, 1.70; 95% CI, 1.34-2.17; P accounting for the maximal amount of ST segment depression and elevation and for patient characteristics. The ST segment summary statistics model had fair discrimination, with an area under the receiver operator characteristic curve of 0.71 (95

  14. The predictive value of diagnostic sonography for the effectiveness of conservative treatment of tennis elbow

    NARCIS (Netherlands)

    Struijs, P. A. A.; Spruyt, M.; Assendelft, W. J. J.; van Dijk, C. N.

    2005-01-01

    OBJECTIVE. Tennis elbow is a common complaint. Several treatment strategies have been described, but an optimal strategy has not been identified. Sonographic imaging as a predictive,factor has never been studied. The aim of our study was to determine the value of sonographic findings in predicting

  15. The predictive value of bronchial histamine challenge in the diagnosis of bronchial asthma

    DEFF Research Database (Denmark)

    Madsen, F; Holstein-Rathlou, N H; Mosbech, H

    1985-01-01

    A prospective survey aiming to study the predictive value of bronchial histamine challenge was performed on 151 patients with a forced expiratory volume1 (FEV1) above 60% of predicted. According to variations in peak expiratory flow rate (PEFR) and medical history the patients were classified as ...

  16. [Study of the predictive value of detection tests for silent aspirations].

    Science.gov (United States)

    Woisard, V; Réhault, E; Brouard, C; Fichaux-Bourin, P; Puech, M; Grand, S

    2009-01-01

    Screening for aspiration in patients with swallowing disorders is important in preventing complications. The tests used in this regard are insufficient due to silent aspiration relating to abnormal protective reflexes in many patients with swallowing problems. The aim of this study is to determine the predictive values of simple tests in screening for silent aspiration. The reference test used was videofluoroscopic examination on swallowing. In the presence of aspiration (FR+) the presence (ME+) or not (ME-) of a cough of throat clearing was noted. The tests being studied were a nasal test with isotonic saline and swallowing according to a set time. For screening for aspiration the presence of a "wet voice" was considered to be a sign of reduced protective reflexes. 1) During the nasal test, the results are 100% for the positive predictive value (VPp) and 83.3% for the negative predictive value (VPn); 2) These results are respectively 84.6% and 35.9% during the swallowing test. Regarding screening for silent aspiration, 1) during the nasal test, the results are 62.5% for the positive predictive value (VPp) and 36.3% for the negative predictive value (VPn); 2) These results are respectively 54.5% and 26.6% during the swallowing test. This preliminary study points out the lack of predictive value of the nasal test and the swallow test for the silent aspirations. However the results could be useful for other researchers developing other tests in this area.

  17. Predicting Protein Secondary Structure with Markov Models

    DEFF Research Database (Denmark)

    Fischer, Paul; Larsen, Simon; Thomsen, Claus

    2004-01-01

    we are considering here, is to predict the secondary structure from the primary one. To this end we train a Markov model on training data and then use it to classify parts of unknown protein sequences as sheets, helices or coils. We show how to exploit the directional information contained...... in the Markov model for this task. Classifications that are purely based on statistical models might not always be biologically meaningful. We present combinatorial methods to incorporate biological background knowledge to enhance the prediction performance....

  18. [Value of the Geriatric Nutritional Risk Index in predicting mortality of elderly patients undergoing hemodialysis].

    Science.gov (United States)

    Zhang, Yuran; Zhang, Zheng; Yu, Qing; Yuan, Weijie

    2015-12-08

    To explore the reliability of the Geriatric Nutritional Risk Index (GNRI) as a mortality predictor in elderly patients undergoing hemodialysis (HD). A total of 125 maintenance HD patients aged >60 years old who had received dialysis for over 6 months before entry was retrospectively examined. The values of GNRI were calculated, and death was taken as the end point. The patients were divided into 4 groups according to the quartiles of GNRI values. All-cause and cardiovascular mortality were calculated using Kaplan-Meier and cox proportional-hazards analyses, and ROC curve was adopted for analyzing the predicting value of GNRI on mortality. The GNRI of the four groups were ≤92.06, 92.07-96.15, 96.16-101.25, ≥101.26, respectively. Kaplan-Meier survival rate was significantly different among 4 groups for all-cause and cardiovascular mortality. Cox regression model analysis demonstrated that the GNRI was a predictor for all cause (HR=0.940, P=0.001, 95%CI: 0.907-0.974) and cardiovascular mortality (HR=0.906, Pmaintainance HD patients, but more multi-center studies with larger samples are still needed.

  19. [Value of the albumin to globulin ratio in predicting severity and prognosis in myasthenia gravis patients].

    Science.gov (United States)

    Yang, D H; Su, Z Q; Chen, Y; Chen, Z B; Ding, Z N; Weng, Y Y; Li, J; Li, X; Tong, Q L; Han, Y X; Zhang, X

    2016-03-08

    To assess the predictive value of the albumin to globulin ratio (AGR) in evaluation of disease severity and prognosis in myasthenia gravis patients. A total of 135 myasthenia gravis (MG) patients were enrolled between February 2009 and March 2015. The AGR was detected on the first day of hospitalization and ranked from lowest to highest, and the patients were divided into three equal tertiles according to the AGR values, which were T1 (AGR 1.53). The Kaplan-Meier curve was used to evaluate the prognostic value of AGR. Cox model analysis was used to evaluate the relevant factors. Multivariate Logistic regression analysis was used to find the predictors of myasthenia crisis during hospitalization. The median length of hospital stay for each tertile was: for the T1 21 days (15-35.5), T2 18 days (14-27.5), and T3 16 days (12-22.5) (Pgravis. At the multivariate Cox regression analysis, the AGR (Pgravis patients. Respectively, the hazard ratio (HR) were 4.655 (95% CI: 2.355-9.202) and 0.596 (95% CI: 0.492-0.723). Multivariate Logistic regression analysis showed the AGR (Pgravis.

  20. Energy based prediction models for building acoustics

    DEFF Research Database (Denmark)

    Brunskog, Jonas

    2012-01-01

    In order to reach robust and simplified yet accurate prediction models, energy based principle are commonly used in many fields of acoustics, especially in building acoustics. This includes simple energy flow models, the framework of statistical energy analysis (SEA) as well as more elaborated...... principles as, e.g., wave intensity analysis (WIA). The European standards for building acoustic predictions, the EN 12354 series, are based on energy flow and SEA principles. In the present paper, different energy based prediction models are discussed and critically reviewed. Special attention is placed...... on underlying basic assumptions, such as diffuse fields, high modal overlap, resonant field being dominant, etc., and the consequences of these in terms of limitations in the theory and in the practical use of the models....

  1. Comparative Study of Bancruptcy Prediction Models

    Directory of Open Access Journals (Sweden)

    Isye Arieshanti

    2013-09-01

    Full Text Available Early indication of bancruptcy is important for a company. If companies aware of  potency of their bancruptcy, they can take a preventive action to anticipate the bancruptcy. In order to detect the potency of a bancruptcy, a company can utilize a a model of bancruptcy prediction. The prediction model can be built using a machine learning methods. However, the choice of machine learning methods should be performed carefully. Because the suitability of a model depends on the problem specifically. Therefore, in this paper we perform a comparative study of several machine leaning methods for bancruptcy prediction. According to the comparative study, the performance of several models that based on machine learning methods (k-NN, fuzzy k-NN, SVM, Bagging Nearest Neighbour SVM, Multilayer Perceptron(MLP, Hybrid of MLP + Multiple Linear Regression, it can be showed that fuzzy k-NN method achieve the best performance with accuracy 77.5%

  2. Waning predictive value of serum adiponectin for fracture risk in elderly men: MrOS Sweden.

    Science.gov (United States)

    Johansson, H; Odén, A; Karlsson, M K; McCloskey, E; Kanis, J A; Ohlsson, C; Mellström, D

    2014-07-01

    Serum adiponectin is a risk factor for fracture. The predictive value attenuates with time in elderly men so that its use for the risk assessment in the long term is questionable. The study underlines the importance of testing the long-term stability of potential risk factors. High serum adiponectin is associated with an increased risk of fracture in elderly men. The aim of the present study was to determine the impact of adiponectin on the probability of fracture as a function of time. The probability of osteoporotic fracture was computed in 989 elderly men from the MrOS study in Sweden. Baseline data included clinical risk factors for fracture, femoral neck BMD and serum adiponectin. Men were followed for up to 7.4 years with a mean follow up of 5.3 years (range 0.0-7.4 years). Poisson regression was used to model the hazard function for osteoporotic fracture and death to determine the 10 year probability of fracture. During follow up, 124 men sustained one or more osteoporotic fracture. There was a significant interaction between adiponectin and time since baseline (p = 0.026) such that the longer time since baseline, the lower the gradient of fracture risk. When using this interaction in the calculation of 10-year probability of fracture, the probabilities of osteoporotic fracture varied little over the range of adiponectin values. Serum adiponectin is a risk factor for fracture. Nevertheless, the predictive value attenuates with time so that its use for the risk assessment in the long term is questionable. This study underlines the importance of testing the long-term stability of potential risk factors that might be used in fracture risk assessment.

  3. Animal studies on medicinal herbs: predictability, dose conversion and potential value.

    Science.gov (United States)

    Wojcikowski, Ken; Gobe, Glenda

    2014-01-01

    Animal studies testing medicinal herbs are often misinterpreted by both translational researchers and clinicians due to a lack of information regarding their predictability, human dose equivalent and potential value. The most common mistake is to design or translate an animal study on a milligram per kilogram basis. This can lead to underestimation of the toxicity and/or overestimation of the amount needed for human therapy. Instead, allometric scaling, which involves body surface area, should be used. While the differences in the pharmacokinetic and pharmacodynamic phases between species will inevitably lead to some degree of error in extrapolation of results regardless of the conversion method used, correct design and interpretation of animal studies can provide information that is not able to be provided by in vitro studies, computer modeling or even traditional use. Copyright © 2013 John Wiley & Sons, Ltd.

  4. Quantifying Confidence in Model Predictions for Hypersonic Aircraft Structures

    Science.gov (United States)

    2015-03-01

    Falsification Power of Posterior p-Value Approach for Various Sample Sizes (Light Blue = 10, Dark Blue = 20, Green = 50, Red = 100...aerothermal model predictions and Glass and Hunt data........... 36 Table 4.12. Correlations between model error parameters in simultaneous posterior samples ...M1 using Latin Hypercube sampling . For each of those samples , a Markov Chain Monte Carlo ( MCMC ) algorithm called slice sampling is employed using

  5. Prediction Models for Dynamic Demand Response

    Energy Technology Data Exchange (ETDEWEB)

    Aman, Saima; Frincu, Marc; Chelmis, Charalampos; Noor, Muhammad; Simmhan, Yogesh; Prasanna, Viktor K.

    2015-11-02

    As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D2R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D2R, which we address in this paper. Our first contribution is the formal definition of D2R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D2R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D2R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D2R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D2R. Also, prediction models require just few days’ worth of data indicating that small amounts of

  6. Are animal models predictive for humans?

    Directory of Open Access Journals (Sweden)

    Greek Ray

    2009-01-01

    Full Text Available Abstract It is one of the central aims of the philosophy of science to elucidate the meanings of scientific terms and also to think critically about their application. The focus of this essay is the scientific term predict and whether there is credible evidence that animal models, especially in toxicology and pathophysiology, can be used to predict human outcomes. Whether animals can be used to predict human response to drugs and other chemicals is apparently a contentious issue. However, when one empirically analyzes animal models using scientific tools they fall far short of being able to predict human responses. This is not surprising considering what we have learned from fields such evolutionary and developmental biology, gene regulation and expression, epigenetics, complexity theory, and comparative genomics.

  7. Evaluation of CASP8 model quality predictions

    KAUST Repository

    Cozzetto, Domenico

    2009-01-01

    The model quality assessment problem consists in the a priori estimation of the overall and per-residue accuracy of protein structure predictions. Over the past years, a number of methods have been developed to address this issue and CASP established a prediction category to evaluate their performance in 2006. In 2008 the experiment was repeated and its results are reported here. Participants were invited to infer the correctness of the protein models submitted by the registered automatic servers. Estimates could apply to both whole models and individual amino acids. Groups involved in the tertiary structure prediction categories were also asked to assign local error estimates to each predicted residue in their own models and their results are also discussed here. The correlation between the predicted and observed correctness measures was the basis of the assessment of the results. We observe that consensus-based methods still perform significantly better than those accepting single models, similarly to what was concluded in the previous edition of the experiment. © 2009 WILEY-LISS, INC.

  8. Technical note: A linear model for predicting δ13 Cprotein.

    Science.gov (United States)

    Pestle, William J; Hubbe, Mark; Smith, Erin K; Stevenson, Joseph M

    2015-08-01

    Development of a model for the prediction of δ(13) Cprotein from δ(13) Ccollagen and Δ(13) Cap-co . Model-generated values could, in turn, serve as "consumer" inputs for multisource mixture modeling of paleodiet. Linear regression analysis of previously published controlled diet data facilitated the development of a mathematical model for predicting δ(13) Cprotein (and an experimentally generated error term) from isotopic data routinely generated during the analysis of osseous remains (δ(13) Cco and Δ(13) Cap-co ). Regression analysis resulted in a two-term linear model (δ(13) Cprotein (%) = (0.78 × δ(13) Cco ) - (0.58× Δ(13) Cap-co ) - 4.7), possessing a high R-value of 0.93 (r(2)  = 0.86, P analysis of human osseous remains. These predicted values are ideal for use in multisource mixture modeling of dietary protein source contribution. © 2015 Wiley Periodicals, Inc.

  9. Predicting entrepreneurial career intentions: Values and the theory of planned behavior.

    NARCIS (Netherlands)

    M.J. Gorgievski-Duijvesteijn (Marjan); U. Stephan (Ute); M. Laguna (Mariola); J.A. Moriano (Juan)

    2017-01-01

    textabstractIntegrating predictions from the theory of human values with the theory of planned behavior (TPB), our primary goal is to investigate mechanisms through which individual values are related to entrepreneurial career intentions using a sample of 823 students from four European countries.

  10. The predictive value of ovarian reserve tests for spontaneous pregnancy in subfertile ovulatory women

    NARCIS (Netherlands)

    Haadsma, M. L.; Groen, H.; Fidler, V.; Bukman, A.; Roeloffzen, E. M. A.; Groenewoud, E. R.; Broekmans, F. J. M.; Heineman, M. J.; Hoek, A.

    BACKGROUND: The predictive value of ovarian reserve tests (ORTs) for spontaneous pregnancy is unclear. Our study aimed to determine whether ORTs have added value to previously identified prognostic factors for spontaneous pregnancy in subfertile ovulatory couples. METHODS: A prospective cohort study

  11. Model predictive controller design of hydrocracker reactors

    OpenAIRE

    GÖKÇE, Dila

    2014-01-01

    This study summarizes the design of a Model Predictive Controller (MPC) in Tüpraş, İzmit Refinery Hydrocracker Unit Reactors. Hydrocracking process, in which heavy vacuum gasoil is converted into lighter and valuable products at high temperature and pressure is described briefly. Controller design description, identification and modeling studies are examined and the model variables are presented. WABT (Weighted Average Bed Temperature) equalization and conversion increase are simulate...

  12. Multi-Model Ensemble Wake Vortex Prediction

    Science.gov (United States)

    Koerner, Stephan; Holzaepfel, Frank; Ahmad, Nash'at N.

    2015-01-01

    Several multi-model ensemble methods are investigated for predicting wake vortex transport and decay. This study is a joint effort between National Aeronautics and Space Administration and Deutsches Zentrum fuer Luft- und Raumfahrt to develop a multi-model ensemble capability using their wake models. An overview of different multi-model ensemble methods and their feasibility for wake applications is presented. The methods include Reliability Ensemble Averaging, Bayesian Model Averaging, and Monte Carlo Simulations. The methodologies are evaluated using data from wake vortex field experiments.

  13. Clinical value of preoperative serum CA 19-9 and CA 125 levels in predicting the resectability of hilar cholangiocarcinoma.

    Science.gov (United States)

    Hu, Hai-Jie; Mao, Hui; Tan, Yong-Qiong; Shrestha, Anuj; Ma, Wen-Jie; Yang, Qin; Wang, Jun-Ke; Cheng, Nan-Sheng; Li, Fu-Yu

    2016-01-01

    To examine the predictive value of tumor markers for evaluating tumor resectability in patients with hilar cholangiocarcinoma and to explore the prognostic effect of various preoperative factors on resectability in patients with potentially resectable tumors. Patients with potentially resectable tumors judged by radiologic examination were included. The receiver operating characteristic (ROC) analysis was conducted to evaluate serum carbohydrate antigenic determinant 19-9 (CA 19-9), carbohydrate antigen 125 (CA 125) and carcino embryonie antigen levels on tumor resectability. Univariate and multivariate logistic regression models were also conducted to analysis the correlation of preoperative factors with resectability. In patients with normal bilirubin levels, ROC curve analysis calculated the ideal CA 19-9 cut-off value of 203.96 U/ml in prediction of resectability, with a sensitivity of 83.7 %, specificity of 80 %, positive predictive value of 91.1 % and negative predictive value of 66.7 %. Meanwhile, the optimal cut-off value for CA 125 to predict resectability was 25.905 U/ml (sensitivity, 78.6 %; specificity, 67.5 %). In a multivariate logistic regression model, tumor size ≤3 cm (OR 4.149, 95 % CI 1.326-12.981, P = 0.015), preoperative CA 19-9 level ≤200 U/ml (OR 20.324, 95 % CI 6.509-63.467, P CA 125 levels ≤26 U/ml (OR 8.209, 95 % CI 2.624-25.677, P CA 19-9 and CA 125 levels predict resectability in patients with radiological resectable hilar cholangiocarcinoma. Increased preoperative CA 19-9 levels and CA 125 levels are associated with poor resectability rate.

  14. Nonlinear mixed-effects modeling: individualization and prediction.

    Science.gov (United States)

    Olofsen, Erik; Dinges, David F; Van Dongen, Hans P A

    2004-03-01

    The development of biomathematical models for the prediction of fatigue and performance relies on statistical techniques to analyze experimental data and model simulations. Statistical models of empirical data have adjustable parameters with a priori unknown values. Interindividual variability in estimates of those values requires a form of smoothing. This traditionally consists of averaging observations across subjects, or fitting a model to the data of individual subjects first and subsequently averaging the parameter estimates. However, the standard errors of the parameter estimates are assessed inaccurately by such averaging methods. The reason is that intra- and inter-individual variabilities are intertwined. They can be separated by mixed-effects modeling in which model predictions are not only determined by fixed effects (usually constant parameters or functions of time) but also by random effects, describing the sampling of subject-specific parameter values from probability distributions. By estimating the parameters of the distributions of the random effects, mixed-effects models can describe experimental observations involving multiple subjects properly (i.e., yielding correct estimates of the standard errors) and parsimoniously (i.e., estimating no more parameters than necessary). Using a Bayesian approach, mixed-effects models can be "individualized" as observations are acquired that capture the unique characteristics of the individual at hand. Mixed-effects models, therefore, have unique advantages in research on human neurobehavioral functions, which frequently show large inter-individual differences. To illustrate this we analyzed laboratory neurobehavioral performance data acquired during sleep deprivation, using a nonlinear mixed-effects model. The results serve to demonstrate the usefulness of mixed-effects modeling for data-driven development of individualized predictive models of fatigue and performance.

  15. Thermodynamic modeling of activity coefficient and prediction of solubility: Part 1. Predictive models.

    Science.gov (United States)

    Mirmehrabi, Mahmoud; Rohani, Sohrab; Perry, Luisa

    2006-04-01

    A new activity coefficient model was developed from excess Gibbs free energy in the form G(ex) = cA(a) x(1)(b)...x(n)(b). The constants of the proposed model were considered to be function of solute and solvent dielectric constants, Hildebrand solubility parameters and specific volumes of solute and solvent molecules. The proposed model obeys the Gibbs-Duhem condition for activity coefficient models. To generalize the model and make it as a purely predictive model without any adjustable parameters, its constants were found using the experimental activity coefficient and physical properties of 20 vapor-liquid systems. The predictive capability of the proposed model was tested by calculating the activity coefficients of 41 binary vapor-liquid equilibrium systems and showed good agreement with the experimental data in comparison with two other predictive models, the UNIFAC and Hildebrand models. The only data used for the prediction of activity coefficients, were dielectric constants, Hildebrand solubility parameters, and specific volumes of the solute and solvent molecules. Furthermore, the proposed model was used to predict the activity coefficient of an organic compound, stearic acid, whose physical properties were available in methanol and 2-butanone. The predicted activity coefficient along with the thermal properties of the stearic acid were used to calculate the solubility of stearic acid in these two solvents and resulted in a better agreement with the experimental data compared to the UNIFAC and Hildebrand predictive models.

  16. PREDICTIVE CAPACITY OF ARCH FAMILY MODELS

    Directory of Open Access Journals (Sweden)

    Raphael Silveira Amaro

    2016-03-01

    Full Text Available In the last decades, a remarkable number of models, variants from the Autoregressive Conditional Heteroscedastic family, have been developed and empirically tested, making extremely complex the process of choosing a particular model. This research aim to compare the predictive capacity, using the Model Confidence Set procedure, than five conditional heteroskedasticity models, considering eight different statistical probability distributions. The financial series which were used refers to the log-return series of the Bovespa index and the Dow Jones Industrial Index in the period between 27 October 2008 and 30 December 2014. The empirical evidences showed that, in general, competing models have a great homogeneity to make predictions, either for a stock market of a developed country or for a stock market of a developing country. An equivalent result can be inferred for the statistical probability distributions that were used.

  17. A revised prediction model for natural conception.

    Science.gov (United States)

    Bensdorp, Alexandra J; van der Steeg, Jan Willem; Steures, Pieternel; Habbema, J Dik F; Hompes, Peter G A; Bossuyt, Patrick M M; van der Veen, Fulco; Mol, Ben W J; Eijkemans, Marinus J C

    2017-06-01

    One of the aims in reproductive medicine is to differentiate between couples that have favourable chances of conceiving naturally and those that do not. Since the development of the prediction model of Hunault, characteristics of the subfertile population have changed. The objective of this analysis was to assess whether additional predictors can refine the Hunault model and extend its applicability. Consecutive subfertile couples with unexplained and mild male subfertility presenting in fertility clinics were asked to participate in a prospective cohort study. We constructed a multivariable prediction model with the predictors from the Hunault model and new potential predictors. The primary outcome, natural conception leading to an ongoing pregnancy, was observed in 1053 women of the 5184 included couples (20%). All predictors of the Hunault model were selected into the revised model plus an additional seven (woman's body mass index, cycle length, basal FSH levels, tubal status,history of previous pregnancies in the current relationship (ongoing pregnancies after natural conception, fertility treatment or miscarriages), semen volume, and semen morphology. Predictions from the revised model seem to concur better with observed pregnancy rates compared with the Hunault model; c-statistic of 0.71 (95% CI 0.69 to 0.73) compared with 0.59 (95% CI 0.57 to 0.61). Copyright © 2017. Published by Elsevier Ltd.

  18. Predictive value of fever and palmar pallor for P. falciparum parasitaemia in children from an endemic area.

    Science.gov (United States)

    Vinnemeier, Christof David; Schwarz, Norbert Georg; Sarpong, Nimako; Loag, Wibke; Acquah, Samuel; Nkrumah, Bernard; Huenger, Frank; Adu-Sarkodie, Yaw; May, Jürgen

    2012-01-01

    Although the incidence of Plasmodium falciparum malaria in some parts of sub-Saharan Africa is reported to decline and other conditions, causing similar symptoms as clinical malaria are gaining in relevance, presumptive anti-malarial treatment is still common. This study traced for age-dependent signs and symptoms predictive for P. falciparum parasitaemia. In total, 5447 visits of 3641 patients between 2-60 months of age who attended an outpatient department (OPD) of a rural hospital in the Ashanti Region, Ghana, were analysed. All Children were examined by a paediatrician and a full blood count and thick smear were done. A Classification and Regression Tree (CART) model was used to generate a clinical decision tree to predict malarial parasitaemia a7nd predictive values of all symptoms were calculated. Malarial parasitaemia was detected in children between 2-12 months and between 12-60 months of age with a prevalence of 13.8% and 30.6%, respectively. The CART-model revealed age-dependent differences in the ability of the variables to predict parasitaemia. While palmar pallor was the most important symptom in children between 2-12 months, a report of fever and an elevated body temperature of ≥37.5°C gained in relevance in children between 12-60 months. The variable palmar pallor was significantly (p<0.001) associated with lower haemoglobin levels in children of all ages. Compared to the Integrated Management of Childhood Illness (IMCI) algorithm the CART-model had much lower sensitivities, but higher specificities and positive predictive values for a malarial parasitaemia. Use of age-derived algorithms increases the specificity of the prediction for P. falciparum parasitaemia. The predictive value of palmar pallor should be underlined in health worker training. Due to a lack of sensitivity neither the best algorithm nor palmar pallor as a single sign are eligible for decision-making and cannot replace presumptive treatment or laboratory diagnosis.

  19. Embryo quality predictive models based on cumulus cells gene expression

    Directory of Open Access Journals (Sweden)

    Devjak R

    2016-06-01

    Full Text Available Since the introduction of in vitro fertilization (IVF in clinical practice of infertility treatment, the indicators for high quality embryos were investigated. Cumulus cells (CC have a specific gene expression profile according to the developmental potential of the oocyte they are surrounding, and therefore, specific gene expression could be used as a biomarker. The aim of our study was to combine more than one biomarker to observe improvement in prediction value of embryo development. In this study, 58 CC samples from 17 IVF patients were analyzed. This study was approved by the Republic of Slovenia National Medical Ethics Committee. Gene expression analysis [quantitative real time polymerase chain reaction (qPCR] for five genes, analyzed according to embryo quality level, was performed. Two prediction models were tested for embryo quality prediction: a binary logistic and a decision tree model. As the main outcome, gene expression levels for five genes were taken and the area under the curve (AUC for two prediction models were calculated. Among tested genes, AMHR2 and LIF showed significant expression difference between high quality and low quality embryos. These two genes were used for the construction of two prediction models: the binary logistic model yielded an AUC of 0.72 ± 0.08 and the decision tree model yielded an AUC of 0.73 ± 0.03. Two different prediction models yielded similar predictive power to differentiate high and low quality embryos. In terms of eventual clinical decision making, the decision tree model resulted in easy-to-interpret rules that are highly applicable in clinical practice.

  20. Model Predictive Control of Wind Turbines using Uncertain LIDAR Measurements

    DEFF Research Database (Denmark)

    Mirzaei, Mahmood; Soltani, Mohsen; Poulsen, Niels Kjølstad

    2013-01-01

    The problem of Model predictive control (MPC) of wind turbines using uncertain LIDAR (LIght Detection And Ranging) measurements is considered. A nonlinear dynamical model of the wind turbine is obtained. We linearize the obtained nonlinear model for different operating points, which are determined......, we simplify state prediction for the MPC. Consequently, the control problem of the nonlinear system is simplified into a quadratic programming. We consider uncertainty in the wind propagation time, which is the traveling time of wind from the LIDAR measurement point to the rotor. An algorithm based...... by the effective wind speed on the rotor disc. We take the wind speed as a scheduling variable. The wind speed is measurable ahead of the turbine using LIDARs, therefore, the scheduling variable is known for the entire prediction horizon. By taking the advantage of having future values of the scheduling variable...

  1. Adverse pregnancy outcomes in hypertensive patients: predictive value of protein concentration versus total protein.

    Science.gov (United States)

    Lee, Amy M; Briandet, Benjamin M; Caranta, Diane G; Zelig, Craig M

    2014-11-01

    To compare the predictive value of protein concentration in a twenty-four hour urine collection to the conventional total protein in a twenty-four hour urine collection for adverse pregnancy outcomes in hypertensive patients. Retrospective cohort study. Hypertensive patients ≥20 weeks estimated gestational age (EGA) who completed twenty-four hour urine protein collections were identified; antepartum and delivery data were examined. For study patients who met criteria for adverse pregnancy outcome, multi-variable analysis was performed and summary receiver operating characteristic (ROC) curves were generated for each model (total protein compared to protein concentration). The models were compared by analyzing the area under the curve (AUC). A total of 150 patients were analyzed. Mean gestational age at delivery was 36.7 weeks. Analysis of the ROC curves showed no significant difference between the models (AUCs of 0.668 versus 0.656, p = 0.715). Optimal thresholds were 299.2 mg for total protein and 0.1 mg/ml for protein concentration. A protein concentration of 0.1 mg/ml on a twenty-four hour urine collection appears equivalent to the traditional 300 mg total protein. If confirmed by prospective studies, this finding would be clinically important in cases where collections fall short of the 300 mg threshold but exceed the 0.1 mg/ml concentration.

  2. Three-model ensemble wind prediction in southern Italy

    Directory of Open Access Journals (Sweden)

    R. C. Torcasio

    2016-03-01

    Full Text Available Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013 three-model ensemble (TME experiment for wind prediction is considered. The models employed, run operationally at National Research Council – Institute of Atmospheric Sciences and Climate (CNR-ISAC, are RAMS (Regional Atmospheric Modelling System, BOLAM (BOlogna Limited Area Model, and MOLOCH (MOdello LOCale in H coordinates. The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System. Comparison with observations is made every 3 h up to 48 h of forecast lead time. Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30 %, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System of the ECMWF (European Centre for Medium-Range Weather Forecast for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.

  3. Three-model ensemble wind prediction in southern Italy

    Science.gov (United States)

    Torcasio, Rosa Claudia; Federico, Stefano; Calidonna, Claudia Roberta; Avolio, Elenio; Drofa, Oxana; Landi, Tony Christian; Malguzzi, Piero; Buzzi, Andrea; Bonasoni, Paolo

    2016-03-01

    Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013) three-model ensemble (TME) experiment for wind prediction is considered. The models employed, run operationally at National Research Council - Institute of Atmospheric Sciences and Climate (CNR-ISAC), are RAMS (Regional Atmospheric Modelling System), BOLAM (BOlogna Limited Area Model), and MOLOCH (MOdello LOCale in H coordinates). The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System). Comparison with observations is made every 3 h up to 48 h of forecast lead time. Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30 %, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System) of the ECMWF (European Centre for Medium-Range Weather Forecast) for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.

  4. The predictive performance and stability of six species distribution models.

    Directory of Open Access Journals (Sweden)

    Ren-Yan Duan

    Full Text Available Predicting species' potential geographical range by species distribution models (SDMs is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs.We collected the distribution data of five common tree species (Pinus massoniana, Betula platyphylla, Quercus wutaishanica, Quercus mongolica and Quercus variabilis and simulated their potential distribution area using 13 environmental variables and six widely used SDMs: BIOCLIM, DOMAIN, MAHAL, RF, MAXENT, and SVM. Each model run was repeated 100 times (trials. We compared the predictive performance by testing the consistency between observations and simulated distributions and assessed the stability by the standard deviation, coefficient of variation, and the 99% confidence interval of Kappa and AUC values.The mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (p<0.05, while the associated standard deviations and coefficients of variation were larger for BIOCLIM and DOMAIN trials (p<0.05, and the 99% confidence intervals for AUC and Kappa values were narrower for MAHAL, RF, MAXENT, and SVM. Compared to BIOCLIM and DOMAIN, other SDMs (MAHAL, RF, MAXENT, and SVM had higher prediction accuracy, smaller confidence intervals, and were more stable and less affected by the random variable (randomly selected pseudo-absence points.According to the prediction performance and stability of SDMs, we can divide these six SDMs into two categories: a high performance and stability group including MAHAL, RF, MAXENT, and SVM, and a low performance and stability group consisting of BIOCLIM, and DOMAIN. We highlight that choosing appropriate SDMs to address a specific problem is an important part of the modeling process.

  5. Measuring Teacher Quality with Value-Added Modeling

    Science.gov (United States)

    Marder, Michael

    2012-01-01

    Using computers to evaluate teachers based on student test scores is more difficult than it seems. Value-added modeling is a genuinely serious attempt to grapple with the difficulties. Value-added modeling carries the promise of measuring teacher quality automatically and objectively, and improving school systems at minimal cost. The essence of…

  6. Modelling language evolution: Examples and predictions

    Science.gov (United States)

    Gong, Tao; Shuai, Lan; Zhang, Menghan

    2014-06-01

    We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon-syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines.

  7. Prediction of Collision Cross-Section Values for Small Molecules: Application to Pesticide Residue Analysis.

    Science.gov (United States)

    Bijlsma, Lubertus; Bade, Richard; Celma, Alberto; Mullin, Lauren; Cleland, Gareth; Stead, Sara; Hernandez, Felix; Sancho, Juan V

    2017-06-20

    The use of collision cross-section (CCS) values obtained by ion mobility high-resolution mass spectrometry has added a third dimension (alongside retention time and exact mass) to aid in the identification of compounds. However, its utility is limited by the number of experimental CCS values currently available. This work demonstrates the potential of artificial neural networks (ANNs) for the prediction of CCS values of pesticides. The predictor, based on eight software-chosen molecular descriptors, was optimized using CCS values of 205 small molecules and validated using a set of 131 pesticides. The relative error was within 6% for 95% of all CCS values for protonated molecules, resulting in a median relative error less than 2%. In order to demonstrate the potential of CCS prediction, the strategy was applied to spinach samples. It notably improved the confidence in the tentative identification of suspect and nontarget pesticides.

  8. Model Predictive Control of Sewer Networks

    DEFF Research Database (Denmark)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik

    2016-01-01

    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....... and controlled have thus become essential factors for efficient performance of waste water treatment plants. This paper examines methods for simplified modelling and controlling a sewer network. A practical approach to the problem is used by analysing simplified design model, which is based on the Barcelona...

  9. Derivation of groundwater threshold values for analysis of impacts predicted at potential carbon sequestration sites

    Energy Technology Data Exchange (ETDEWEB)

    Last, G. V.; Murray, C. J.; Bott, Y.

    2016-06-01

    The U.S. Department of Energy’s (DOE’s) National Risk Assessment Partnership (NRAP) Project is developing reduced-order models to evaluate potential impacts to groundwater quality due to carbon dioxide (CO2) or brine leakage, should it occur from deep CO2 storage reservoirs. These efforts targeted two classes of aquifer – an unconfined fractured carbonate aquifer based on the Edwards Aquifer in Texas, and a confined alluvium aquifer based on the High Plains Aquifer in Kansas. Hypothetical leakage scenarios focus on wellbores as the most likely conduits from the storage reservoir to an underground source of drinking water (USDW). To facilitate evaluation of potential degradation of the USDWs, threshold values, below which there would be no predicted impacts, were determined for each of these two aquifer systems. These threshold values were calculated using an interwell approach for determining background groundwater concentrations that is an adaptation of methods described in the U.S. Environmental Protection Agency’s Unified Guidance for Statistical Analysis of Groundwater Monitoring Data at RCRA Facilities. Results demonstrate the importance of establishing baseline groundwater quality conditions that capture the spatial and temporal variability of the USDWs prior to CO2 injection and storage.

  10. Bayesian Predictive Models for Rayleigh Wind Speed

    DEFF Research Database (Denmark)

    Shahirinia, Amir; Hajizadeh, Amin; Yu, David C

    2017-01-01

    predictive model of the wind speed aggregates the non-homogeneous distributions into a single continuous distribution. Therefore, the result is able to capture the variation among the probability distributions of the wind speeds at the turbines’ locations in a wind farm. More specifically, instead of using...... a wind speed distribution whose parameters are known or estimated, the parameters are considered as random whose variations are according to probability distributions. The Bayesian predictive model for a Rayleigh which only has a single model scale parameter has been proposed. Also closed-form posterior......One of the major challenges with the increase in wind power generation is the uncertain nature of wind speed. So far the uncertainty about wind speed has been presented through probability distributions. Also the existing models that consider the uncertainty of the wind speed primarily view...

  11. Comparison of two ordinal prediction models

    DEFF Research Database (Denmark)

    Kattan, Michael W; Gerds, Thomas A

    2015-01-01

    system (i.e. old or new), such as the level of evidence for one or more factors included in the system or the general opinions of expert clinicians. However, given the major objective of estimating prognosis on an ordinal scale, we argue that the rival staging system candidates should be compared...... on their ability to predict outcome. We sought to outline an algorithm that would compare two rival ordinal systems on their predictive ability. RESULTS: We devised an algorithm based largely on the concordance index, which is appropriate for comparing two models in their ability to rank observations. We...... demonstrate our algorithm with a prostate cancer staging system example. CONCLUSION: We have provided an algorithm for selecting the preferred staging system based on prognostic accuracy. It appears to be useful for the purpose of selecting between two ordinal prediction models....

  12. Estimation and prediction under local volatility jump-diffusion model

    Science.gov (United States)

    Kim, Namhyoung; Lee, Younhee

    2018-02-01

    Volatility is an important factor in operating a company and managing risk. In the portfolio optimization and risk hedging using the option, the value of the option is evaluated using the volatility model. Various attempts have been made to predict option value. Recent studies have shown that stochastic volatility models and jump-diffusion models reflect stock price movements accurately. However, these models have practical limitations. Combining them with the local volatility model, which is widely used among practitioners, may lead to better performance. In this study, we propose a more effective and efficient method of estimating option prices by combining the local volatility model with the jump-diffusion model and apply it using both artificial and actual market data to evaluate its performance. The calibration process for estimating the jump parameters and local volatility surfaces is divided into three stages. We apply the local volatility model, stochastic volatility model, and local volatility jump-diffusion model estimated by the proposed method to KOSPI 200 index option pricing. The proposed method displays good estimation and prediction performance.

  13. Predictive and Prognostic Value of Preoperative Thrombocytosis in Upper Tract Urothelial Carcinoma.

    Science.gov (United States)

    Foerster, Beat; Moschini, Marco; Abufaraj, Mohammad; Soria, Francesco; Gust, Kilian M; Rouprêt, Morgan; Karakiewicz, Pierre I; Briganti, Alberto; Rink, Michael; Kluth, Luis; Mathieu, Romain; Margulis, Vitaly; Lotan, Yair; Aziz, Atiqullah; John, Hubert; Shariat, Shahrokh F

    2017-12-01

    The purpose of this study was to evaluate the predictive and prognostic role of preoperative thrombocytosis (TC) in upper tract urothelial carcinoma (UTUC) after radical nephroureterectomy (RNU) in a large multi-institutional cohort of patients. Records of 2492 patients undergoing RNU for non-metastatic UTUC between 1990 and 2008 were retrospectively analyzed. Preoperative TC was defined as a platelet count > 400 × 10 9 /L, irrespective of gender type. Logistic regression analyses were performed to evaluate its association with pathologic features. Cox proportional hazards regression was used for estimation of recurrence-free survival, cancer-specific survival, and overall survival. Preoperative TC was found in 309 (12.4%) patients and was associated with advanced tumor stage and grade, lymph node metastasis, lymphovascular invasion, tumor architecture, necrosis, and concomitant carcinoma in situ (P-values ≤ .027). Preoperative TC independently predicted ≥ pT2 (P preoperative model that adjusted for the effects of age, gender, location, multifocality, and tumor architecture. Within a median follow-up of 45 months, recurrence occurred in 663 (26.6%) patients with 545 (21.9%) dying of UTUC. In univariable Cox proportional hazard regression analysis, TC was significantly associated with recurrence-free survival (hazard ratio [HR], 1.32; P = .015) and overall survival (HR, 1.4; P Preoperative TC is associated with adverse clinicopathologic features and predicts worse pathology at RNU. Among other serum biomarkers, TC could benefit preoperative risk stratification and help guide treatment decisions. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. Mean Value Engine Modelling of an SI Engine with EGR

    DEFF Research Database (Denmark)

    Føns, Michael; Müller, Martin; Chevalier, Alain

    1999-01-01

    Mean Value Engine Models (MVEMs) are simplified, dynamic engine models what are physically based. Such models are useful for control studies, for engine control system analysis and for model based engine control systems. Very few published MVEMs have included the effects of Exhaust Gas Recirculat...

  15. Two alternative methods to predict amylose content in rice grain by using tristimulus CIELAB values and developing a specific color board of starch-iodine complex solution

    Science.gov (United States)

    Amylose content was predicted by measuring tridimensional L*a*b* values in starch-iodine solutions and building a regression model. The developed regression model showed a highly significant relationship (R2= 0.99) between the L*a*b values and the amylose content. Apparent amylose content was strong...

  16. Predictive modeling in homogeneous catalysis: a tutorial

    NARCIS (Netherlands)

    Maldonado, A.G.; Rothenberg, G.

    2010-01-01

    Predictive modeling has become a practical research tool in homogeneous catalysis. It can help to pinpoint ‘good regions’ in the catalyst space, narrowing the search for the optimal catalyst for a given reaction. Just like any other new idea, in silico catalyst optimization is accepted by some

  17. Model predictive control of smart microgrids

    DEFF Research Database (Denmark)

    Hu, Jiefeng; Zhu, Jianguo; Guerrero, Josep M.

    2014-01-01

    required to realise high-performance of distributed generations and will realise innovative control techniques utilising model predictive control (MPC) to assist in coordinating the plethora of generation and load combinations, thus enable the effective exploitation of the clean renewable energy sources...

  18. Feedback model predictive control by randomized algorithms

    NARCIS (Netherlands)

    Batina, Ivo; Stoorvogel, Antonie Arij; Weiland, Siep

    2001-01-01

    In this paper we present a further development of an algorithm for stochastic disturbance rejection in model predictive control with input constraints based on randomized algorithms. The algorithm presented in our work can solve the problem of stochastic disturbance rejection approximately but with

  19. A Robustly Stabilizing Model Predictive Control Algorithm

    Science.gov (United States)

    Ackmece, A. Behcet; Carson, John M., III

    2007-01-01

    A model predictive control (MPC) algorithm that differs from prior MPC algorithms has been developed for controlling an uncertain nonlinear system. This algorithm guarantees the resolvability of an associated finite-horizon optimal-control problem in a receding-horizon implementation.

  20. Hierarchical Model Predictive Control for Resource Distribution

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Trangbæk, K; Stoustrup, Jakob

    2010-01-01

    This paper deals with hierarchichal model predictive control (MPC) of distributed systems. A three level hierachical approach is proposed, consisting of a high level MPC controller, a second level of so-called aggregators, controlled by an online MPC-like algorithm, and a lower level of autonomous...

  1. Model Predictive Control based on Finite Impulse Response Models

    DEFF Research Database (Denmark)

    Prasath, Guru; Jørgensen, John Bagterp

    2008-01-01

    We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations ...

  2. Development and validation of PRE-DELIRIC (PREdiction of DELIRium in ICu patients) delirium prediction model for intensive care patients: observational multicentre study.

    NARCIS (Netherlands)

    Boogaard, M.W. van den; Pickkers, P.; Slooter, A.J.; Kuiper, M.A.; Spronk, P.E.; Voort, P.H. van der; Hoeven, J.G. van der; Donders, R.; Achterberg, T. van; Schoonhoven, L.

    2012-01-01

    OBJECTIVES: To develop and validate a delirium prediction model for adult intensive care patients and determine its additional value compared with prediction by caregivers. DESIGN: Observational multicentre study. SETTING: Five intensive care units in the Netherlands (two university hospitals and

  3. Disease prediction models and operational readiness.

    Directory of Open Access Journals (Sweden)

    Courtney D Corley

    Full Text Available The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011. We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4, spatial (26, ecological niche (28, diagnostic or clinical (6, spread or response (9, and reviews (3. The model parameters (e.g., etiology, climatic, spatial, cultural and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological were recorded and reviewed. A component of this review is the identification of verification and validation (V&V methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology

  4. Caries risk assessment models in caries prediction

    Directory of Open Access Journals (Sweden)

    Amila Zukanović

    2013-11-01

    Full Text Available Objective. The aim of this research was to assess the efficiency of different multifactor models in caries prediction. Material and methods. Data from the questionnaire and objective examination of 109 examinees was entered into the Cariogram, Previser and Caries-Risk Assessment Tool (CAT multifactor risk assessment models. Caries risk was assessed with the help of all three models for each patient, classifying them as low, medium or high-risk patients. The development of new caries lesions over a period of three years [Decay Missing Filled Tooth (DMFT increment = difference between Decay Missing Filled Tooth Surface (DMFTS index at baseline and follow up], provided for examination of the predictive capacity concerning different multifactor models. Results. The data gathered showed that different multifactor risk assessment models give significantly different results (Friedman test: Chi square = 100.073, p=0.000. Cariogram is the model which identified the majority of examinees as medium risk patients (70%. The other two models were more radical in risk assessment, giving more unfavorable risk –profiles for patients. In only 12% of the patients did the three multifactor models assess the risk in the same way. Previser and CAT gave the same results in 63% of cases – the Wilcoxon test showed that there is no statistically significant difference in caries risk assessment between these two models (Z = -1.805, p=0.071. Conclusions. Evaluation of three different multifactor caries risk assessment models (Cariogram, PreViser and CAT showed that only the Cariogram can successfully predict new caries development in 12-year-old Bosnian children.

  5. Modelling personality, plasticity and predictability in shelter dogs

    Science.gov (United States)

    2017-01-01

    Behavioural assessments of shelter dogs (Canis lupus familiaris) typically comprise standardized test batteries conducted at one time point, but test batteries have shown inconsistent predictive validity. Longitudinal behavioural assessments offer an alternative. We modelled longitudinal observational data on shelter dog behaviour using the framework of behavioural reaction norms, partitioning variance into personality (i.e. inter-individual differences in behaviour), plasticity (i.e. inter-individual differences in average behaviour) and predictability (i.e. individual differences in residual intra-individual variation). We analysed data on interactions of 3263 dogs (n = 19 281) with unfamiliar people during their first month after arrival at the shelter. Accounting for personality, plasticity (linear and quadratic trends) and predictability improved the predictive accuracy of the analyses compared to models quantifying personality and/or plasticity only. While dogs were, on average, highly sociable with unfamiliar people and sociability increased over days since arrival, group averages were unrepresentative of all dogs and predictions made at the individual level entailed considerable uncertainty. Effects of demographic variables (e.g. age) on personality, plasticity and predictability were observed. Behavioural repeatability was higher one week after arrival compared to arrival day. Our results highlight the value of longitudinal assessments on shelter dogs and identify measures that could improve the predictive validity of behavioural assessments in shelters. PMID:28989764

  6. Advective transport in heterogeneous aquifers: Are proxy models predictive?

    Science.gov (United States)

    Fiori, A.; Zarlenga, A.; Gotovac, H.; Jankovic, I.; Volpi, E.; Cvetkovic, V.; Dagan, G.

    2015-12-01

    We examine the prediction capability of two approximate models (Multi-Rate Mass Transfer (MRMT) and Continuous Time Random Walk (CTRW)) of non-Fickian transport, by comparison with accurate 2-D and 3-D numerical simulations. Both nonlocal in time approaches circumvent the need to solve the flow and transport equations by using proxy models to advection, providing the breakthrough curves (BTC) at control planes at any x, depending on a vector of five unknown parameters. Although underlain by different mechanisms, the two models have an identical structure in the Laplace Transform domain and have the Markovian property of independent transitions. We show that also the numerical BTCs enjoy the Markovian property. Following the procedure recommended in the literature, along a practitioner perspective, we first calibrate the parameters values by a best fit with the numerical BTC at a control plane at x1, close to the injection plane, and subsequently use it for prediction at further control planes for a few values of σY2≤8. Due to a similar structure and Markovian property, the two methods perform equally well in matching the numerical BTC. The identified parameters are generally not unique, making their identification somewhat arbitrary. The inverse Gaussian model and the recently developed Multi-Indicator Model (MIM), which does not require any fitting as it relates the BTC to the permeability structure, are also discussed. The application of the proxy models for prediction requires carrying out transport field tests of large plumes for a long duration.

  7. Predictive value of Tokuhashi scoring systems in spinal metastases, focusing on various primary tumor groups

    DEFF Research Database (Denmark)

    Wang, Miao; Bünger, Cody Eric; Li, Haisheng

    2012-01-01

    and accuracy rate of the 2 scoring systems were compared in each cancer group. RESULTS: Both the T12 and T15 scoring systems showed statistically significant predictive value when the 448 patients were analyzed in total (T12, P rate was significantly higher in T15 (P...... predictive value in patients with spinal metastases. T15 has a statistically higher accuracy rate than T12. Among the various cancer groups, the 2 scoring systems are especially reliable in prostate and breast metastases groups. T15 is recommended as superior to T12 because of its higher accuracy rate.......STUDY DESIGN: We conducted a prospective cohort study of 448 patients with spinal metastases from a variety of cancer groups. OBJECTIVE: To determine the specific predictive value of the Tokuhashi scoring system (T12) and its revised version (T15) in spinal metastases of various primary tumors...

  8. A model for predicting Inactivity in the European Banking Sector

    Directory of Open Access Journals (Sweden)

    Themistokles Lazarides

    2015-08-01

    Full Text Available Purpose – The paper will addresses the issue of inactivity and will try to detect its causes using econometric models. The Banking sector of Europe has been under transformation or restructuring for almost half a century. Design/methodology/approach – Probit models and descriptive statistics have been used to create a system that predicts inactivity. The data was collected from Bankscope. Findings – The results of the econometric models show that from the six groups of indicators, four have been found to be statistically important (performance, size, ownership, corporate governance. These findings are consistent with the theory. Research limitations/implications – The limitation is that Bankscope does not provide any longitudinal data regarding ownership, management structure and there are some many missing values before 2007 for some of the financial ratios and data. Originality/value – The paper's value and innovation is that it has given a systemic approach to find indicators of inactivity.

  9. Multi-Valued Modal Fixed Point Logics for Model Checking

    Science.gov (United States)

    Nishizawa, Koki

    In this paper, I will show how multi-valued logics are used for model checking. Model checking is an automatic technique to analyze correctness of hardware and software systems. A model checker is based on a temporal logic or a modal fixed point logic. That is to say, a system to be checked is formalized as a Kripke model, a property to be satisfied by the system is formalized as a temporal formula or a modal formula, and the model checker checks that the Kripke model satisfies the formula. Although most existing model checkers are based on 2-valued logics, recently new attempts have been made to extend the underlying logics of model checkers to multi-valued logics. I will summarize these new results.

  10. Implicit Theories, Expectancies, and Values Predict Mathematics Motivation and Behavior across High School and College.

    Science.gov (United States)

    Priess-Groben, Heather A; Hyde, Janet Shibley

    2017-06-01

    Mathematics motivation declines for many adolescents, which limits future educational and career options. The present study sought to identify predictors of this decline by examining whether implicit theories assessed in ninth grade (incremental/entity) predicted course-taking behaviors and utility value in college. The study integrated implicit theory with variables from expectancy-value theory to examine potential moderators and mediators of the association of implicit theories with college mathematics outcomes. Implicit theories and expectancy-value variables were assessed in 165 American high school students (47 % female; 92 % White), who were then followed into their college years, at which time mathematics courses taken, course-taking intentions, and utility value were assessed. Implicit theories predicted course-taking intentions and utility value, but only self-concept of ability predicted courses taken, course-taking intentions, and utility value after controlling for prior mathematics achievement and baseline values. Expectancy for success in mathematics mediated associations between self-concept of ability and college outcomes. This research identifies self-concept of ability as a stronger predictor than implicit theories of mathematics motivation and behavior across several years: math self-concept is critical to sustained engagement in mathematics.

  11. Electrostatic ion thrusters - towards predictive modeling

    Energy Technology Data Exchange (ETDEWEB)

    Kalentev, O.; Matyash, K.; Duras, J.; Lueskow, K.F.; Schneider, R. [Ernst-Moritz-Arndt Universitaet Greifswald, D-17489 (Germany); Koch, N. [Technische Hochschule Nuernberg Georg Simon Ohm, Kesslerplatz 12, D-90489 Nuernberg (Germany); Schirra, M. [Thales Electronic Systems GmbH, Soeflinger Strasse 100, D-89077 Ulm (Germany)

    2014-02-15

    The development of electrostatic ion thrusters so far has mainly been based on empirical and qualitative know-how, and on evolutionary iteration steps. This resulted in considerable effort regarding prototype design, construction and testing and therefore in significant development and qualification costs and high time demands. For future developments it is anticipated to implement simulation tools which allow for quantitative prediction of ion thruster performance, long-term behavior and space craft interaction prior to hardware design and construction. Based on integrated numerical models combining self-consistent kinetic plasma models with plasma-wall interaction modules a new quality in the description of electrostatic thrusters can be reached. These open the perspective for predictive modeling in this field. This paper reviews the application of a set of predictive numerical modeling tools on an ion thruster model of the HEMP-T (High Efficiency Multi-stage Plasma Thruster) type patented by Thales Electron Devices GmbH. (copyright 2014 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

  12. Predictive value of brain perfusion SPECT for rTMS response in pharmacoresistant depression

    International Nuclear Information System (INIS)

    Richieri, Raphaelle; Lancon, Christophe; Boyer, Laurent; Farisse, Jean; Colavolpe, Cecile; Mundler, Olivier; Guedj, Eric

    2011-01-01

    The aim of this study was to determine the predictive value of whole-brain voxel-based regional cerebral blood flow (rCBF) for repetitive transcranial magnetic stimulation (rTMS) response in patients with pharmacoresistant depression. Thirty-three right-handed patients who met DSM-IV criteria for major depressive disorder (unipolar or bipolar depression) were included before rTMS. rTMS response was defined as at least 50% reduction in the baseline Beck Depression Inventory scores. The predictive value of 99m Tc-ethyl cysteinate dimer (ECD) single photon emission computed tomography (SPECT) for rTMS response was studied before treatment by comparing rTMS responders to non-responders at voxel level using Statistical Parametric Mapping (SPM) (p 0.10). In comparison to responders, non-responders showed significant hypoperfusions (p < 0.001, uncorrected) in the left medial and bilateral superior frontal cortices (BA10), the left uncus/parahippocampal cortex (BA20/BA35) and the right thalamus. The area under the curve for the combination of SPECT clusters to predict rTMS response was 0.89 (p < 0.001). Sensitivity, specificity, positive predictive value and negative predictive value for the combination of clusters were: 94, 73, 81 and 92%, respectively. This study shows that, in pharmacoresistant depression, pretreatment rCBF of specific brain regions is a strong predictor for response to rTMS in patients with homogeneous demographic/clinical features. (orig.)

  13. Characterizing Attention with Predictive Network Models.

    Science.gov (United States)

    Rosenberg, M D; Finn, E S; Scheinost, D; Constable, R T; Chun, M M

    2017-04-01

    Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals' attentional abilities. While being some of the first generalizable neuromarkers of cognitive function, these models also inform our basic understanding of attention, providing empirical evidence that: (i) attention is a network property of brain computation; (ii) the functional architecture that underlies attention can be measured while people are not engaged in any explicit task; and (iii) this architecture supports a general attentional ability that is common to several laboratory-based tasks and is impaired in attention deficit hyperactivity disorder (ADHD). Looking ahead, connectivity-based predictive models of attention and other cognitive abilities and behaviors may potentially improve the assessment, diagnosis, and treatment of clinical dysfunction. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Genetic models of homosexuality: generating testable predictions

    Science.gov (United States)

    Gavrilets, Sergey; Rice, William R

    2006-01-01

    Homosexuality is a common occurrence in humans and other species, yet its genetic and evolutionary basis is poorly understood. Here, we formulate and study a series of simple mathematical models for the purpose of predicting empirical patterns that can be used to determine the form of selection that leads to polymorphism of genes influencing homosexuality. Specifically, we develop theory to make contrasting predictions about the genetic characteristics of genes influencing homosexuality including: (i) chromosomal location, (ii) dominance among segregating alleles and (iii) effect sizes that distinguish between the two major models for their polymorphism: the overdominance and sexual antagonism models. We conclude that the measurement of the genetic characteristics of quantitative trait loci (QTLs) found in genomic screens for genes influencing homosexuality can be highly informative in resolving the form of natural selection maintaining their polymorphism. PMID:17015344

  15. A two process model of burnout and work engagement: distinct implications of demands and values.

    Science.gov (United States)

    Leiter, M P

    2008-01-01

    A model of job burnout proposes two distinct processes. The first process concerns balance of demands to resources. A poor balance leads to chronic exhaustion, an integral aspect of the burnout syndrome. The second process concerns the congruence of individual and organizational values. The model proposes that value conflicts have implications for all three aspects of burnout. It also proposes that the impact of value conflicts has only minor implications for the exhaustion aspect of burnout; they are more relevant for the cynicism and inefficacy aspects of the syndrome. The model considers distinct processes at work that concern employees' perception of organizational justice and their trust in leadership. With a sample of 725 nurses, the analysis tested one component of the theory: the extent to which value congruence enhances the prediction of burnout beyond the prediction provided by demands and resources. Future directions are discussed.

  16. Self-Service Banking: Value Creation Models and Information Exchange

    Directory of Open Access Journals (Sweden)

    Ragnvald Sannes

    2001-01-01

    Full Text Available This paper argues that most banks have failed to exploit the potential of self-service banking because they base their service design on an incomplete business model for self-service. A framework for evaluation of self-service banking concepts is developed on the basis of Stabell and Fjeldstad's three value configurations. The value network and the value shop are consistent with self-service banking while the value chain is inappropriate. The impact of the value configurations on information exchange and self-service functionality is discussed, and a framework for design of such services proposed. Current self-service banking practices are compared to the framework, and it is concluded that current practice matches the concept of a value network and not the value shop. However, current practices are only a partial implementation of a value network-based self-service banking concept.

  17. A statistical model for predicting muscle performance

    Science.gov (United States)

    Byerly, Diane Leslie De Caix

    The objective of these studies was to develop a capability for predicting muscle performance and fatigue to be utilized for both space- and ground-based applications. To develop this predictive model, healthy test subjects performed a defined, repetitive dynamic exercise to failure using a Lordex spinal machine. Throughout the exercise, surface electromyography (SEMG) data were collected from the erector spinae using a Mega Electronics ME3000 muscle tester and surface electrodes placed on both sides of the back muscle. These data were analyzed using a 5th order Autoregressive (AR) model and statistical regression analysis. It was determined that an AR derived parameter, the mean average magnitude of AR poles, significantly correlated with the maximum number of repetitions (designated Rmax) that a test subject was able to perform. Using the mean average magnitude of AR poles, a test subject's performance to failure could be predicted as early as the sixth repetition of the exercise. This predictive model has the potential to provide a basis for improving post-space flight recovery, monitoring muscle atrophy in astronauts and assessing the effectiveness of countermeasures, monitoring astronaut performance and fatigue during Extravehicular Activity (EVA) operations, providing pre-flight assessment of the ability of an EVA crewmember to perform a given task, improving the design of training protocols and simulations for strenuous International Space Station assembly EVA, and enabling EVA work task sequences to be planned enhancing astronaut performance and safety. Potential ground-based, medical applications of the predictive model include monitoring muscle deterioration and performance resulting from illness, establishing safety guidelines in the industry for repetitive tasks, monitoring the stages of rehabilitation for muscle-related injuries sustained in sports and accidents, and enhancing athletic performance through improved training protocols while reducing

  18. Extreme value prediction of the wave-induced vertical bending moment in large container ships

    DEFF Research Database (Denmark)

    Andersen, Ingrid Marie Vincent; Jensen, Jørgen Juncher

    2015-01-01

    increase the extreme hull girder response significantly. Focus in the present paper is on the influence of the hull girder flexibility on the extreme response amidships, namely the wave-induced vertical bending moment (VBM) in hogging, and the prediction of the extreme value of the same. The analysis...... in the present paper is based on time series of full scale measurements from three large container ships of 8600, 9400 and 14000 TEU. When carrying out the extreme value estimation the peak-over-threshold (POT) method combined with an appropriate extreme value distribution is applied. The choice of a proper...... threshold level as well as the statistical correlation between clustered peaks influence the extreme value prediction and are taken into consideration in the present paper....

  19. Prediction models : the right tool for the right problem

    NARCIS (Netherlands)

    Kappen, Teus H.; Peelen, Linda M.

    2016-01-01

    PURPOSE OF REVIEW: Perioperative prediction models can help to improve personalized patient care by providing individual risk predictions to both patients and providers. However, the scientific literature on prediction model development and validation can be quite technical and challenging to

  20. Predictive value of MSH2 gene expression in colorectal cancer treated with capecitabine

    DEFF Research Database (Denmark)

    Jensen, Lars H; Danenberg, Kathleen D; Danenberg, Peter V

    2007-01-01

    was associated with a hazard ratio of 0.5 (95% confidence interval, 0.23-1.11; P = 0.083) in survival analysis. CONCLUSION: The higher gene expression of MSH2 in responders and the trend for predicting overall survival indicates a predictive value of this marker in the treatment of advanced CRC with capecitabine.......PURPOSE: The objective of the present study was to evaluate the gene expression of the DNA mismatch repair gene MSH2 as a predictive marker in advanced colorectal cancer (CRC) treated with first-line capecitabine. PATIENTS AND METHODS: Microdissection of paraffin-embedded tumor tissue, RNA...

  1. Missing Value Imputation Improves Mortality Risk Prediction Following Cardiac Surgery: An Investigation of an Australian Patient Cohort.

    Science.gov (United States)

    Karim, Md Nazmul; Reid, Christopher M; Tran, Lavinia; Cochrane, Andrew; Billah, Baki

    2017-03-01

    The aim of this study was to evaluate the impact of missing values on the prediction performance of the model predicting 30-day mortality following cardiac surgery as an example. Information from 83,309 eligible patients, who underwent cardiac surgery, recorded in the Australia and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) database registry between 2001 and 2014, was used. An existing 30-day mortality risk prediction model developed from ANZSCTS database was re-estimated using the complete cases (CC) analysis and using multiple imputation (MI) analysis. Agreement between the risks generated by the CC and MI analysis approaches was assessed by the Bland-Altman method. Performances of the two models were compared. One or more missing predictor variables were present in 15.8% of the patients in the dataset. The Bland-Altman plot demonstrated significant disagreement between the risk scores (prisk of mortality. Compared to CC analysis, MI analysis resulted in an average of 8.5% decrease in standard error, a measure of uncertainty. The MI model provided better prediction of mortality risk (observed: 2.69%; MI: 2.63% versus CC: 2.37%, Pvalues improved the 30-day mortality risk prediction following cardiac surgery. Copyright © 2016 Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) and the Cardiac Society of Australia and New Zealand (CSANZ). Published by Elsevier B.V. All rights reserved.

  2. Environment Modeling Using Runtime Values for JPF-Android

    Science.gov (United States)

    van der Merwe, Heila; Tkachuk, Oksana; Nel, Seal; van der Merwe, Brink; Visser, Willem

    2015-01-01

    Software applications are developed to be executed in a specific environment. This environment includes external native libraries to add functionality to the application and drivers to fire the application execution. For testing and verification, the environment of an application is simplified abstracted using models or stubs. Empty stubs, returning default values, are simple to generate automatically, but they do not perform well when the application expects specific return values. Symbolic execution is used to find input parameters for drivers and return values for library stubs, but it struggles to detect the values of complex objects. In this work-in-progress paper, we explore an approach to generate drivers and stubs based on values collected during runtime instead of using default values. Entry-points and methods that need to be modeled are instrumented to log their parameters and return values. The instrumented applications are then executed using a driver and instrumented libraries. The values collected during runtime are used to generate driver and stub values on- the-fly that improve coverage during verification by enabling the execution of code that previously crashed or was missed. We are implementing this approach to improve the environment model of JPF-Android, our model checking and analysis tool for Android applications.

  3. Positive Predictive Value of BI-RADS Categorization in an Asian Population

    OpenAIRE

    Yah-Yuen Tan; Siew-Bock Wee; Mona P.C. Tan

    2004-01-01

    The Breast Imaging Reporting And Data System (BI-RADS) categorization of mammograms is useful in estimating the risk of malignancy, thereby guiding management decisions. However, in Asian women, in whom breast density is increased, the sensitivity of mammography is correspondingly lower. We sought to determine the positive predictive value of BI-RADS categorization for malignancy in our Asian population and, hence, its value in helping us to choose between the various modalities for breast bi...

  4. Neuro-fuzzy modeling in bankruptcy prediction

    Directory of Open Access Journals (Sweden)

    Vlachos D.

    2003-01-01

    Full Text Available For the past 30 years the problem of bankruptcy prediction had been thoroughly studied. From the paper of Altman in 1968 to the recent papers in the '90s, the progress of prediction accuracy was not satisfactory. This paper investigates an alternative modeling of the system (firm, combining neural networks and fuzzy controllers, i.e. using neuro-fuzzy models. Classical modeling is based on mathematical models that describe the behavior of the firm under consideration. The main idea of fuzzy control, on the other hand, is to build a model of a human control expert who is capable of controlling the process without thinking in a mathematical model. This control expert specifies his control action in the form of linguistic rules. These control rules are translated into the framework of fuzzy set theory providing a calculus, which can stimulate the behavior of the control expert and enhance its performance. The accuracy of the model is studied using datasets from previous research papers.

  5. Low positive predictive value of midnight salivary cortisol measurement to detect hypercortisolism in type 2 diabetes

    DEFF Research Database (Denmark)

    Steffensen, Maria Charlotte; H Thomsen, Henrik; Dekkers, Olaf M

    2016-01-01

    , gender, body mass index (BMI) and haemoglobin A1c levels. We used the following cut-off values: LNSC ≤ 3·6 nmol/l and DST ≤ 50 nmol/l. RESULTS: The median (range) levels of LNSC and DST were 6·1 (0·3-46·2) nmol/l and 34 (11-547) nmol/l, respectively. Hypercortisolism was present in 86% based on LNSC...... values and 22% based on DST. LNSC, as compared to DST, had the following test characteristics: sensitivity: 85% (95% CI: 7-92%), specificity: 14% (95% CI: 10-19%), positive predictive value: 22% (95% CI: 17-27%), negative predictive value: 76% (95% CI: 63-87%), and overall accuracy: 30% (95% CI: 25......-34%). LNSC and DST values were not associated with haemoglobin A1c, BMI and age in this cohort of patients with T2D. CONCLUSION: The LNSC is characterized by very low specificity and poor positive predictive value as compared to the DST, resulting in an overall low accuracy. Further methodological...

  6. In silico modeling to predict drug-induced phospholipidosis

    International Nuclear Information System (INIS)

    Choi, Sydney S.; Kim, Jae S.; Valerio, Luis G.; Sadrieh, Nakissa

    2013-01-01

    Drug-induced phospholipidosis (DIPL) is a preclinical finding during pharmaceutical drug development that has implications on the course of drug development and regulatory safety review. A principal characteristic of drugs inducing DIPL is known to be a cationic amphiphilic structure. This provides evidence for a structure-based explanation and opportunity to analyze properties and structures of drugs with the histopathologic findings for DIPL. In previous work from the FDA, in silico quantitative structure–activity relationship (QSAR) modeling using machine learning approaches has shown promise with a large dataset of drugs but included unconfirmed data as well. In this study, we report the construction and validation of a battery of complementary in silico QSAR models using the FDA's updated database on phospholipidosis, new algorithms and predictive technologies, and in particular, we address high performance with a high-confidence dataset. The results of our modeling for DIPL include rigorous external validation tests showing 80–81% concordance. Furthermore, the predictive performance characteristics include models with high sensitivity and specificity, in most cases above ≥ 80% leading to desired high negative and positive predictivity. These models are intended to be utilized for regulatory toxicology applied science needs in screening new drugs for DIPL. - Highlights: • New in silico models for predicting drug-induced phospholipidosis (DIPL) are described. • The training set data in the models is derived from the FDA's phospholipidosis database. • We find excellent predictivity values of the models based on external validation. • The models can support drug screening and regulatory decision-making on DIPL

  7. Prediction of pKa Values for Druglike Molecules Using Semiempirical Quantum Chemical Methods.

    Science.gov (United States)

    Jensen, Jan H; Swain, Christopher J; Olsen, Lars

    2017-01-26

    Rapid yet accurate pK a prediction for druglike molecules is a key challenge in computational chemistry. This study uses PM6-DH+/COSMO, PM6/COSMO, PM7/COSMO, PM3/COSMO, AM1/COSMO, PM3/SMD, AM1/SMD, and DFTB3/SMD to predict the pK a values of 53 amine groups in 48 druglike compounds. The approach uses an isodesmic reaction where the pK a value is computed relative to a chemically related reference compound for which the pK a value has been measured experimentally or estimated using a standard empirical approach. The AM1- and PM3-based methods perform best with RMSE values of 1.4-1.6 pH units that have uncertainties of ±0.2-0.3 pH units, which make them statistically equivalent. However, for all but PM3/SMD and AM1/SMD the RMSEs are dominated by a single outlier, cefadroxil, caused by proton transfer in the zwitterionic protonation state. If this outlier is removed, the RMSE values for PM3/COSMO and AM1/COSMO drop to 1.0 ± 0.2 and 1.1 ± 0.3, whereas PM3/SMD and AM1/SMD remain at 1.5 ± 0.3 and 1.6 ± 0.3/0.4 pH units, making the COSMO-based predictions statistically better than the SMD-based predictions. For pK a calculations where a zwitterionic state is not involved or proton transfer in a zwitterionic state is not observed, PM3/COSMO or AM1/COSMO is the best pK a prediction method; otherwise PM3/SMD or AM1/SMD should be used. Thus, fast and relatively accurate pK a prediction for 100-1000s of druglike amines is feasible with the current setup and relatively modest computational resources.

  8. Predictive value of IgE/IgG4 antibody ratio in children with egg allergy

    Directory of Open Access Journals (Sweden)

    Okamoto Shindou

    2012-06-01

    Full Text Available Abstract Background The aim of this study was to investigate the role of specific IgG4 antibodies to hen’s egg white and determine their utility as a marker for the outcome of oral challenge test in children sensitized to hen’s egg Methods The hen’s egg oral food challenge test was performed in 105 sensitized children without atopic dermatitis, and the titers of egg white-specific immunoglobulin G4 (IgG4 and immunoglobulin E (IgE antibodies were measured. To set the cut-off values of IgG4, IgE, and the IgE/IgG4 ratio for predicting positive results in oral challenges, receiver operating characteristic curves were plotted and the area under the curves (AUC were calculated. Results Sixty-four of 105 oral challenges with whole eggs were assessed as positive. The AUC for IgE, IgG4, and IgE/IgG4 for the prediction of positive results were 0.609, 0.724, and 0.847, respectively. Thus, the IgE/IgG4 ratio generated significantly higher specificity, sensitivity, positive predictive value (%, and negative predictive value (% than the individual IgE and IgG4. The negative predictive value of the IgE/IgG4 ratio was 90% at a value of 1. Conclusions We have demonstrated that the egg white-specific serum IgE/IgG4 ratio is important for predicting reactivity to egg during food challenges.

  9. Predictive Models for Carcinogenicity and Mutagenicity ...

    Science.gov (United States)

    Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendous cost (in time, money, animals) of rodent carcinogenicity bioassays. Both mutagenicity and carcinogenicity involve complex, cellular processes that are only partially understood. Advances in technologies and generation of new data will permit a much deeper understanding. In silico methods for predicting mutagenicity and rodent carcinogenicity based on chemical structural features, along with current mutagenicity and carcinogenicity data sets, have performed well for local prediction (i.e., within specific chemical classes), but are less successful for global prediction (i.e., for a broad range of chemicals). The predictivity of in silico methods can be improved by improving the quality of the data base and endpoints used for modelling. In particular, in vitro assays for clastogenicity need to be improved to reduce false positives (relative to rodent carcinogenicity) and to detect compounds that do not interact directly with DNA or have epigenetic activities. New assays emerging to complement or replace some of the standard assays include VitotoxTM, GreenScreenGC, and RadarScreen. The needs of industry and regulators to assess thousands of compounds necessitate the development of high-t

  10. The Residual Value Models: A Framework for Business Administration

    OpenAIRE

    Konstantinos J. Liapis

    2010-01-01

    This article investigates the relationship between a firm’s performance and Residual Value Models (RVM) which serve as decision making tools in corporate management. The main measures are the Economic Value Added (EVA®) and Cash Value Added (CVA®), with key components the Residual Income (RI), Free Cash Flow (FCF) and Weighted Average Cost of Capital (WACC). These measures have attracted considerable interest among scientists, practitioners and organizations in recent years. This work focuses...

  11. The value of Doppler ultrasound in predicting delayed graft function occurrence after kidney transplantation.

    Science.gov (United States)

    Mocny, Grzegorz; Bachul, Piotr; Chang, Ea-Sle; Kulig, Piotr

    The aim of this study was to assess the predictive value of blood flow velocity and vascular resistance measured by Doppler ultrasound in terms of pulsatility index (PI) and resistive index (RI) respectively, in the occurrence of delayed graft function (DGF) after kidney transplantation. This prospective study enrolled kidney transplant recipients operated from January 2005 to April 2009 in the 1st Department of General, Oncological and Gastroenterological Surgery, Jagiellonian University Medical College, Kraków, Poland. The medical records of 53 kidney transplant recipients from deceased donors were reviewed. PI and RI values of the graft arcuate artery were calculated immediately after blood flow restoration and on the 1st, 2nd, 4th and 8th post-operative day. DGF was observed in 20 patients (37.7%), while 33 patients (62.3%) had immediate restoration of the kidney function. The mean intraoperative values of RI and PI from patients with DGF were significantly higher in comparison to patients without DGF (0.9 vs. 0.74, p PI values remained stable and significantly higher in DGF group. The highest sensitivity of RI to predict DGF occurrence was observed intraoperatively and on the first postoperative day, with values of 77.8% and 72.2%, respectively. The risk of DGF occurrence with intraoperative RI value ≥0.9 increased by 13-fold, and with intraoperative PI value ≥1.9 by 12-fold. This increase was even more prominent during the first post-operative day with RI value ≥0.9 or PI value ≥1.9 with 19-fold increase in the risk of DGF occurrence. According to our study, the utilization of Doppler ultrasound with measurement of hemodynamic parameters (PI, RI), play a crucial role in predicting the outcomes of kidney transplantation.

  12. Brownian gas models for extreme-value laws

    International Nuclear Information System (INIS)

    Eliazar, Iddo

    2013-01-01

    In this paper we establish one-dimensional Brownian gas models for the extreme-value laws of Gumbel, Weibull, and Fréchet. A gas model is a countable collection of independent particles governed by common diffusion dynamics. The extreme-value laws are the universal probability distributions governing the affine scaling limits of the maxima and minima of ensembles of independent and identically distributed one-dimensional random variables. Using the recently introduced concept of stationary Poissonian intensities, we construct two gas models whose global statistical structures are stationary, and yield the extreme-value laws: a linear Brownian motion gas model for the Gumbel law, and a geometric Brownian motion gas model for the Weibull and Fréchet laws. The stochastic dynamics of these gas models are studied in detail, and closed-form analytical descriptions of their temporal correlation structures, their topological phase transitions, and their intrinsic first-passage-time fluxes are presented. (paper)

  13. Disease Prediction Models and Operational Readiness

    Energy Technology Data Exchange (ETDEWEB)

    Corley, Courtney D.; Pullum, Laura L.; Hartley, David M.; Benedum, Corey M.; Noonan, Christine F.; Rabinowitz, Peter M.; Lancaster, Mary J.

    2014-03-19

    INTRODUCTION: The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. One of the primary goals of this research was to characterize the viability of biosurveillance models to provide operationally relevant information for decision makers to identify areas for future research. Two critical characteristics differentiate this work from other infectious disease modeling reviews. First, we reviewed models that attempted to predict the disease event, not merely its transmission dynamics. Second, we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). Methods: We searched dozens of commercial and government databases and harvested Google search results for eligible models utilizing terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche-modeling, The publication date of search results returned are bound by the dates of coverage of each database and the date in which the search was performed, however all searching was completed by December 31, 2010. This returned 13,767 webpages and 12,152 citations. After de-duplication and removal of extraneous material, a core collection of 6,503 items was established and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. Next, PNNL’s IN-SPIRE visual analytics software was used to cross-correlate these publications with the definition for a biosurveillance model resulting in the selection of 54 documents that matched the criteria resulting Ten of these documents, However, dealt purely with disease spread models, inactivation of bacteria, or the modeling of human immune system responses to pathogens rather than predicting disease events. As a result, we systematically reviewed 44 papers and the

  14. Nonlinear model predictive control theory and algorithms

    CERN Document Server

    Grüne, Lars

    2017-01-01

    This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...

  15. Stakeholder Theory and Value Creation Models in Brazilian Firms

    Directory of Open Access Journals (Sweden)

    Natalia Giugni Vidal

    2015-09-01

    Full Text Available Objective – The purpose of this study is to understand how top Brazilian firms think about and communicate value creation to their stakeholders. Design/methodology/approach – We use qualitative content analysis methodology to analyze the sustainability or annual integrated reports of the top 25 Brazilian firms by sales revenue. Findings – Based on our analysis, these firms were classified into three main types of stakeholder value creation models: narrow, broad, or transitioning from narrow to broad. We find that many of the firms in our sample are in a transition state between narrow and broad stakeholder value creation models. We also identify seven areas of concentration discussed by firms in creating value for stakeholders: better stakeholder relationships, better work environment, environmental preservation, increased customer base, local development, reputation, and stakeholder dialogue. Practical implications – This study shows a trend towards broader stakeholder value creation models in Brazilian firms. The findings of this study may inform practitioners interested in broadening their value creation models. Originality/value – This study adds to the discussion of stakeholder theory in the Brazilian context by understanding variations in value creation orientation in Brazil.

  16. Predictive value of brain perfusion SPECT for ketamine response in hyperalgesic fibromyalgia

    International Nuclear Information System (INIS)

    Guedj, Eric; Cammilleri, Serge; Colavolpe, Cecile; Taieb, David; Laforte, Catherine de; Mundler, Olivier; Niboyet, Jean

    2007-01-01

    Ketamine has been used successfully in various proportions of fibromyalgia (FM) patients. However, the response to this specific treatment remains largely unpredictable. We evaluated brain SPECT perfusion before treatment with ketamine, using voxel-based analysis. The objective was to determine the predictive value of brain SPECT for ketamine response. Seventeen women with FM (48 ± 11 years; ACR criteria) were enrolled in the study. Brain SPECT was performed before any change was made in therapy in the pain care unit. We considered that a patient was a good responder to ketamine if the VAS score for pain decreased by at least 50% after treatment. A voxel-by-voxel group analysis was performed using SPM2, in comparison to a group of ten healthy women matched for age. The VAS score for pain was 81.8 ± 4.2 before ketamine and 31.8 ± 27.1 after ketamine. Eleven patients were considered ''good responders'' to ketamine. Responder and non-responder subgroups were similar in terms of pain intensity before ketamine. In comparison to responding patients and healthy subjects, non-responding patients exhibited a significant reduction in bilateral perfusion of the medial frontal gyrus. This cluster of hypoperfusion was highly predictive of non-response to ketamine (positive predictive value 100%, negative predictive value 91%). Brain perfusion SPECT may predict response to ketamine in hyperalgesic FM patients. (orig.)

  17. Predictive value of brain perfusion SPECT for ketamine response in hyperalgesic fibromyalgia

    Energy Technology Data Exchange (ETDEWEB)

    Guedj, Eric; Cammilleri, Serge; Colavolpe, Cecile; Taieb, David; Laforte, Catherine de; Mundler, Olivier [Centre Hospitalo-Universitaire de la Timone, Service Central de Biophysique et de Medecine Nucleaire, Assistance Publique des Hopitaux de Marseille, Marseille Cedex 5 (France); Niboyet, Jean [Clinique La Phoceanne, Unite d' Etude et de Traitement de la Douleur, Marseille (France)

    2007-08-15

    Ketamine has been used successfully in various proportions of fibromyalgia (FM) patients. However, the response to this specific treatment remains largely unpredictable. We evaluated brain SPECT perfusion before treatment with ketamine, using voxel-based analysis. The objective was to determine the predictive value of brain SPECT for ketamine response. Seventeen women with FM (48 {+-} 11 years; ACR criteria) were enrolled in the study. Brain SPECT was performed before any change was made in therapy in the pain care unit. We considered that a patient was a good responder to ketamine if the VAS score for pain decreased by at least 50% after treatment. A voxel-by-voxel group analysis was performed using SPM2, in comparison to a group of ten healthy women matched for age. The VAS score for pain was 81.8 {+-} 4.2 before ketamine and 31.8 {+-} 27.1 after ketamine. Eleven patients were considered ''good responders'' to ketamine. Responder and non-responder subgroups were similar in terms of pain intensity before ketamine. In comparison to responding patients and healthy subjects, non-responding patients exhibited a significant reduction in bilateral perfusion of the medial frontal gyrus. This cluster of hypoperfusion was highly predictive of non-response to ketamine (positive predictive value 100%, negative predictive value 91%). Brain perfusion SPECT may predict response to ketamine in hyperalgesic FM patients. (orig.)

  18. HESS Opinions: Hydrologic predictions in a changing environment: behavioral modeling

    Directory of Open Access Journals (Sweden)

    S. J. Schymanski

    2011-02-01

    Full Text Available Most hydrological models are valid at most only in a few places and cannot be reasonably transferred to other places or to far distant time periods. Transfer in space is difficult because the models are conditioned on past observations at particular places to define parameter values and unobservable processes that are needed to fully characterize the structure and functioning of the landscape. Transfer in time has to deal with the likely temporal changes to both parameters and processes under future changed conditions. This remains an important obstacle to addressing some of the most urgent prediction questions in hydrology, such as prediction in ungauged basins and prediction under global change. In this paper, we propose a new approach to catchment hydrological modeling, based on universal principles that do not change in time and that remain valid across many places. The key to this framework, which we call behavioral modeling, is to assume that there are universal and time-invariant organizing principles that can be used to identify the most appropriate model structure (including parameter values and responses for a given ecosystem at a given moment in time. These organizing principles may be derived from fundamental physical or biological laws, or from empirical laws that have been demonstrated to be time-invariant and to hold at many places and scales. Much fundamental research remains to be undertaken to help discover these organizing principles on the basis of exploration of observed patterns of landscape structure and hydrological behavior and their interpretation as legacy effects of past co-evolution of climate, soils, topography, vegetation and humans. Our hope is that the new behavioral modeling framework will be a step forward towards a new vision for hydrology where models are capable of more confidently predicting the behavior of catchments beyond what has been observed or experienced before.

  19. Preterm birth and cerebral palsy. Predictive value of pregnancy complications, mode of delivery, and Apgar scores

    DEFF Research Database (Denmark)

    Topp, Monica Wedell; Langhoff-Roos, J; Uldall, P

    1997-01-01

    .01), and low Apgar scores at 1 minute (45% vs. 36%, p or = 3 (adjusted OR = 1.53 (95% CI 1.00-2.34), p ... complications preceding preterm birth did not imply a higher risk of cerebral palsy. Delivery by Cesarean section was a prognostic factor for developing cerebral palsy, and the predictive value of Apgar scores was highly limited....

  20. The predictive value of childhood subthreshold manic symptoms for adolescent and adult psychiatric outcomes

    NARCIS (Netherlands)

    Papachristou, Efstathios; Oldehinkel, Albertine J.; Ormel, Johan; Raven, Dennis; Hartman, Catharina A.; Frangou, Sophia; Reichenberg, Abraham

    2017-01-01

    Background: Childhood subthreshold manic symptoms may represent a state of developmental vulnerability to Bipolar Disorder (BD) and may also be associated with other adverse psychiatric outcomes. To test this hypothesis we examined the structure and predictive value of childhood subthreshold manic

  1. Assessment of dual tasking has no clinical value for fall prediction in Parkinson's disease

    NARCIS (Netherlands)

    Smulders, K.; Esselink, R.A.J.; Weiss, A; Kessels, R.P.C.; Geurts, A.C.H.; Bloem, B.R.

    2012-01-01

    The objective of this study is to investigate the value of dual-task performance for the prediction of falls inpatients with Parkinson's disease (PD). Two hundred sixty three patients with PD (H&Y 1-3, 65.2 +/- 7.9 years)walked two times along a 10-m trajectory, both under single-task and dual-task

  2. Interests, Work Values, and Occupations: Predicting Work Outcomes with the WorkKeys Fit Assessment

    Science.gov (United States)

    Swaney, Kyle B.; Allen, Jeff; Casillas, Alex; Hanson, Mary Ann; Robbins, Steven B.

    2012-01-01

    This study examined whether a measure of person-environment (P-E) fit predicted worker ratings of work attitudes and supervisor ratings of performance. After combining extant data elements and expert ratings of interest and work value characteristics for each occupation in the O*NET system, the authors generated a "Fit Index"--involving profile…

  3. Predictive value of plasma human chorionic gonadotropin measured 14 days after Day-2 single embryo transfer

    DEFF Research Database (Denmark)

    Løssl, Kristine; Oldenburg, Anna; Toftager, Mette

    2017-01-01

    -2 embryos. The aim of the present study was to investigate the predictive value of p-hCG measured exactly 14 days after the most commonly used Day-2 SET on pregnancy, delivery, and perinatal outcome. Material and methods: A retrospective analysis of prospectively collected data on 466 women who had...

  4. Predictive value of the official cancer alarm symptoms in general practice

    DEFF Research Database (Denmark)

    Krasnik Huggenberger, Ivan; Andersen, John Sahl

    2015-01-01

    Introduction: The objective of this study was to investigate the evidence for positive predictive value (PPV) of alarm symptoms and combinations of symptoms for colorectal cancer, breast cancer, prostate cancer and lung cancer in general practice. Methods: This study is based on a literature search...

  5. Significance of a Behavioral Economic Index of Reward Value in Predicting Drinking Problem Resolution

    Science.gov (United States)

    Tucker, Jalie A.; Vuchinich, Rudy E.; Black, Bethany C.; Rippens, Paula D.

    2006-01-01

    This study investigated whether a behavioral economic index of the value of rewards available over different time horizons improved prediction of drinking outcomes beyond established biopsychosocial predictors. Preferences for immediate drinking versus more delayed rewards made possible by saving money were determined from expenditures prior to…

  6. Predictive value of acute coronary syndrome discharge diagnoses in the Danish national patioent registry

    DEFF Research Database (Denmark)

    Joensen, Albert Marni; Jensen, Majken K.; Overvad, Kim

    Background: Updated data on the predictive value of acute coronary syndrome (ACS) diagnoses, including unstable angina pectoris, myocardial infarction and cardiac arrest, in hospital discharge registries are sparse. Design: Validation study. Methods: All first-time ACS diagnoses in the Danish...

  7. The predictive value of the 70-gene signature for adjuvant chemotherapy in early breast cancer

    NARCIS (Netherlands)

    Knauer, Michael; Mook, Stella; Rutgers, Emiel J. T.; Bender, Richard A.; Hauptmann, Michael; van de Vijver, Marc J.; Koornstra, Rutger H. T.; Bueno-de-Mesquita, Jolien M.; Linn, Sabine C.; van 't Veer, Laura J.

    2010-01-01

    Multigene assays have been developed and validated to determine the prognosis of breast cancer. In this study, we assessed the additional predictive value of the 70-gene MammaPrint signature for chemotherapy (CT) benefit in addition to endocrine therapy (ET) from pooled study series. For 541

  8. Neutrophil CD64 has a high negative predictive value for exclusion ...

    African Journals Online (AJOL)

    Neutrophil CD64 has a high negative predictive value for exclusion of neonatal sepsis. M B Dhlamini, M S Suchard, T M Wiggill, O O Fadahun, D E Ballot. Department of Molecular Medicine and Haematology, National Health Laboratory Service and Faculty of Health Sciences, University of the. Witwatersrand, Johannesburg.

  9. Does measurement of preoperative anxiety have added value for predicting postoperative nausea and vomiting?

    NARCIS (Netherlands)

    van den Bosch, Jolanda E.; Moons, Karel G.; Bonsel, Gouke J.; Kalkman, Cor J.

    2005-01-01

    Preoperative anxiety has been suggested as a predictor of postoperative nausea and vomiting (PONV), but supporting data are lacking. We quantified the added predictive value of preoperative anxiety to established predictors of PONV in 1389 surgical inpatients undergoing various procedures, by using

  10. Impact of the endoscopist's experience on the negative predictive value of capsule endoscopy.

    Science.gov (United States)

    Velayos Jiménez, Benito; Alcaide Suárez, Noelia; González Redondo, Guillermo; Fernández Salazar, Luis; Aller de la Fuente, Rocío; Del Olmo Martínez, Lourdes; Ruiz Rebollo, Lourdes; González Hernández, José Manuel

    2017-01-01

    The impact of the accumulated experience of the capsule endoscopy (CE) reader on the accuracy of this test is discussed. To determine whether the negative predictive value of CE findings changes along the learning curve. We reviewed the first 900 CE read by 3 gastroenterologists experienced in endoscopy over 8 years. These 900 CE were divided into 3 groups (300 CE each): group 1 consisted of the sum of the first 100 CE read by each of the 3 endoscopists; group 2, the sum of the second 100 and groups 3, the sum of the third 100. Patients with normal CE were monitored for at least 28 months to estimate the negative predictive value. A total of 54 (18%) CE in group 1, 58 (19.3%) in group 2 and 47 (15.6%) in group 3 were normal, although only 34 patients in group 1, 38 in group 2 and 36 in group 3 with normal CE completed follow up and were eventually studied. The negative predictive value was 88.2% in group 1, 89.5% in group 2 and 97% in group 3 (P>.05). The negative predictive value tended to increase, but remained high and did not change significantly after the first 100 when readers are experienced in conventional endoscopy and have preliminary specific training. Copyright © 2016 Elsevier España, S.L.U., AEEH y AEG. All rights reserved.

  11. The Predictive Value of Selection Criteria in an Urban Magnet School

    Science.gov (United States)

    Lohmeier, Jill Hendrickson; Raad, Jennifer

    2012-01-01

    The predictive value of selection criteria on outcome data from two cohorts of students (Total N = 525) accepted to an urban magnet high school were evaluated. Regression analyses of typical screening variables (suspensions, absences, metropolitan achievement tests, middle school grade point averages [GPAs], Matrix Analogies test scores, and…

  12. Predictive value of serum gelsolin in hepatitis B virus (HBV)-related ...

    African Journals Online (AJOL)

    Yomi

    2012-03-08

    Mar 8, 2012 ... 2Health Statistics Teaching and Research Section, College of Medicine, Xi'an Jiaotong University, Xi'an 710068, China. 3Beijing Cotimes ... Key words: Gelsolin, hepatitis B virus (HBV)-related chronic liver disease, predictive value. INTRODUCTION ... marker of the degree of liver injury. At present, the.

  13. Predictive value of clinical scoring and simplified gait analysis for acetabulum fractures.

    Science.gov (United States)

    Braun, Benedikt J; Wrona, Julian; Veith, Nils T; Rollman, Mika; Orth, Marcel; Herath, Steven C; Holstein, Jörg H; Pohlemann, Tim

    2016-12-01

    Fractures of the acetabulum show a high, long-term complication rate. The aim of the present study was to determine the predictive value of clinical scoring and standardized, simplified gait analysis on the outcome after these fractures. Forty-one patients with acetabular fractures treated between 2008 and 2013 and available, standardized video recorded aftercare were identified from a prospective database. A visual gait score was used to determine the patients walking abilities 6-m postoperatively. Clinical (Merle d'Aubigne and Postel score, visual analogue scale pain, EQ5d) and radiological scoring (Kellgren-Lawrence score, postoperative computed tomography, and Matta classification) were used to perform correlation and multivariate regression analysis. The average patient age was 48 y (range, 15-82 y), six female patients were included in the study. Mean follow-up was 1.6 y (range, 1-2 y). Moderate correlation between the gait score and outcome (versus EQ5d: r s  = 0.477; versus Merle d'Aubigne: r s  = 0.444; versus Kellgren-Lawrence: r s  = -0.533), as well as high correlation between the Merle d'Aubigne score and outcome were seen (versus EQ5d: r s  = 0.575; versus Merle d'Aubigne: r s  = 0.776; versus Kellgren-Lawrence: r s  = -0.419). Using a multivariate regression model, the 6 m gait score (B = -0.299; P gait score/Merle d'Aubigne) can predict short-term radiological and functional outcome after acetabular fractures with sufficient accuracy. Decisions on further treatment and interventions could be based on simplified gait analysis. Copyright © 2016 Elsevier Inc. All rights reserved.

  14. Predictive Modeling in Actinide Chemistry and Catalysis

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Ping [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-05-16

    These are slides from a presentation on predictive modeling in actinide chemistry and catalysis. The following topics are covered in these slides: Structures, bonding, and reactivity (bonding can be quantified by optical probes and theory, and electronic structures and reaction mechanisms of actinide complexes); Magnetic resonance properties (transition metal catalysts with multi-nuclear centers, and NMR/EPR parameters); Moving to more complex systems (surface chemistry of nanomaterials, and interactions of ligands with nanoparticles); Path forward and conclusions.

  15. Predictive modelling of evidence informed teaching

    OpenAIRE

    Zhang, Dell; Brown, C.

    2017-01-01

    In this paper, we analyse the questionnaire survey data collected from 79 English primary schools about the situation of evidence informed teaching, where the evidences could come from research journals or conferences. Specifically, we build a predictive model to see what external factors could help to close the gap between teachers’ belief and behaviour in evidence informed teaching, which is the first of its kind to our knowledge. The major challenge, from the data mining perspective, is th...

  16. A Predictive Model for Cognitive Radio

    Science.gov (United States)

    2006-09-14

    response in a given situation. Vadde et al. interest and produce a model for prediction of the response. have applied response surface methodology and...34 2000. [3] K. K. Vadde and V. R. Syrotiuk, "Factor interaction on service configurations to those that best meet our communication delivery in mobile ad...resulting set of configurations randomly or apply additional 2004. screening criteria. [4] K. K. Vadde , M.-V. R. Syrotiuk, and D. C. Montgomery

  17. Tectonic predictions with mantle convection models

    Science.gov (United States)

    Coltice, Nicolas; Shephard, Grace E.

    2018-04-01

    Over the past 15 yr, numerical models of convection in Earth's mantle have made a leap forward: they can now produce self-consistent plate-like behaviour at the surface together with deep mantle circulation. These digital tools provide a new window into the intimate connections between plate tectonics and mantle dynamics, and can therefore be used for tectonic predictions, in principle. This contribution explores this assumption. First, initial conditions at 30, 20, 10 and 0 Ma are generated by driving a convective flow with imposed plate velocities at the surface. We then compute instantaneous mantle flows in response to the guessed temperature fields without imposing any boundary conditions. Plate boundaries self-consistently emerge at correct locations with respect to reconstructions, except for small plates close to subduction zones. As already observed for other types of instantaneous flow calculations, the structure of the top boundary layer and upper-mantle slab is the dominant character that leads to accurate predictions of surface velocities. Perturbations of the rheological parameters have little impact on the resulting surface velocities. We then compute fully dynamic model evolution from 30 and 10 to 0 Ma, without imposing plate boundaries or plate velocities. Contrary to instantaneous calculations, errors in kinematic predictions are substantial, although the plate layout and kinematics in several areas remain consistent with the expectations for the Earth. For these calculations, varying the rheological parameters makes a difference for plate boundary evolution. Also, identified errors in initial conditions contribute to first-order kinematic errors. This experiment shows that the tectonic predictions of dynamic models over 10 My are highly sensitive to uncertainties of rheological parameters and initial temperature field in comparison to instantaneous flow calculations. Indeed, the initial conditions and the rheological parameters can be good enough

  18. Value Creation Challenges in Multichannel Retail Business Models

    Directory of Open Access Journals (Sweden)

    Mika Yrjölä

    2014-08-01

    Full Text Available Purpose: The purpose of the paper is to identify and analyze the challenges of value creation in multichannel retail business models. Design/methodology/approach: With the help of semi-structured interviews with top executives from different retailing environments, this study introduces a model of value creation challenges in the context of multichannel retailing. The challenges are analyzed in terms of three retail business model elements, i.e., format, activities, and governance. Findings: Adopting a multichannel retail business model requires critical rethinking of the basic building blocks of value creation. First of all, as customers effortlessly move between multiple channels, multichannel formats can lead to a mismatch between customer and firm value. Secondly, retailers face pressures to use their activities to form integrated total offerings to customers. Thirdly, multiple channels might lead to organizational silos with conflicting goals. A careful orchestration of value creation is needed to determine the roles and incentives of the channel parties involved. Research limitations/implications: In contrast to previous business model literature, this study did not adopt a network-centric view. By embracing the boundary-spanning nature of the business model, other challenges and elements might have been discovered (e.g., challenges in managing relationships with suppliers. Practical implications: As a practical contribution, this paper has analyzed the challenges retailers face in adopting multichannel business models. Customer tendencies for showrooming behavior highlight the need for generating efficient lock-in strategies. Customized, personal offers and information are ways to increase customer value, differentiate from competition, and achieve lock-in. Originality/value: As a theoretical contribution, this paper empirically investigates value creation challenges in a specific context, lowering the level of abstraction in the mostly

  19. Integral-Value Models for Outcomes over Continuous Time

    DEFF Research Database (Denmark)

    Harvey, Charles M.; Østerdal, Lars Peter

    Models of preferences between outcomes over continuous time are important for individual, corporate, and social decision making, e.g., medical treatment, infrastructure development, and environmental regulation. This paper presents a foundation for such models. It shows that conditions on prefere...... on preferences between real- or vector-valued outcomes over continuous time are satisfied if and only if the preferences are represented by a value function having an integral form......Models of preferences between outcomes over continuous time are important for individual, corporate, and social decision making, e.g., medical treatment, infrastructure development, and environmental regulation. This paper presents a foundation for such models. It shows that conditions...

  20. Treatment of hepatitis C virus infection in patients with cirrhosis and predictive value of model for end-stage liver disease: Analysis of data from the Hepa-C registry.

    Science.gov (United States)

    Fernández Carrillo, Carlos; Lens, Sabela; Llop, Elba; Pascasio, Juan Manuel; Crespo, Javier; Arenas, Juan; Fernández, Inmaculada; Baliellas, Carme; Carrión, José Antonio; de la Mata, Manuel; Buti, Maria; Castells, Lluís; Albillos, Agustín; Romero, Manuel; Turnes, Juan; Pons, Clara; Moreno-Planas, José María; Moreno-Palomares, José Javier; Fernández-Rodriguez, Conrado; García-Samaniego, Javier; Prieto, Martín; Fernández Bermejo, Miguel; Salmerón, Javier; Badia, Ester; Salcedo, Magdalena; Herrero, José Ignacio; Granados, Rafael; Blé, Michel; Mariño, Zoe; Calleja, José Luis

    2017-06-01

    Direct-acting antiviral agents (DAAs) are highly effective and well tolerated in patients with chronic hepatitis C virus infection, including those with compensated cirrhosis. However, fewer data are available in patients with more advanced liver disease. Our retrospective, noninterventional, national, multicenter study in patients from the Spanish Hepa-C registry investigated the effectiveness and safety of interferon-free DAA regimens in patients with advanced liver disease, including those with decompensated cirrhosis, in routine practice (all currently approved regimens were registered). Patients transplanted during treatment or within 12 weeks of completing treatment were excluded. Among 843 patients with cirrhosis (Child-Turcotte-Pugh [CTP] class A, n = 564; CTP class B/C, n = 175), 90% achieved sustained virologic response 12 weeks after treatment (SVR12). Significant differences in SVR12 and relapse rates were observed between CTP class A and CTP class B/C patients (94% versus 78%, and 4% versus 14%, respectively; both P < 0.001). Serious adverse events (SAEs) were more common in CTP class B/C versus CTP class A patients (50% versus 12%, respectively; P < 0.001). Incident decompensation was the most common serious adverse event (7% overall). Death rate during the study period was 16/843 (2%), significantly higher among CTP class B/C versus CTP class A patients (6.4% versus 0.9%; P < 0.001). Baseline Model for End-Stage Liver Disease (MELD) score alone (cut-off 18) was the best predictor of survival. Patients with decompensated cirrhosis receiving DAAs present lower response rates and experience more SAEs. In this setting, a MELD score ≥18 may help clinicians to identify those patients with a higher risk of complications and to individualize treatment decisions. (Hepatology 2017;65:1810-1822). © 2017 by the American Association for the Study of Liver Diseases.

  1. Predictive Modeling of the CDRA 4BMS

    Science.gov (United States)

    Coker, Robert F.; Knox, James C.

    2016-01-01

    As part of NASA's Advanced Exploration Systems (AES) program and the Life Support Systems Project (LSSP), fully predictive models of the Four Bed Molecular Sieve (4BMS) of the Carbon Dioxide Removal Assembly (CDRA) on the International Space Station (ISS) are being developed. This virtual laboratory will be used to help reduce mass, power, and volume requirements for future missions. In this paper we describe current and planned modeling developments in the area of carbon dioxide removal to support future crewed Mars missions as well as the resolution of anomalies observed in the ISS CDRA.

  2. Immunity against infectious diseases: predictive value of self-reported history of vaccination and disease.

    Science.gov (United States)

    Trevisan, Andrea; Frasson, Clara; Morandin, Marta; Beggio, Michela; Bruno, Alberto; Davanzo, Elisabetta; Di Marco, Livio; Simioni, Livio; Amato, Guglielmo

    2007-05-01

    To determine whether self-reported history of disease and/or vaccination is predictive of immunity against hepatitis B, varicella, rubella, mumps, and measles. The seroprevalence of viral antibodies and the predictive value of a self-report questionnaire were determined for 616 paramedical students who matriculated into Padua Medical School (Padua, Italy) during 2003-2005. The majority of subjects (86.9%) remembered being vaccinated against hepatitis B but had no recollection of disease. Among vaccinees, 1.5% showed markers of previous infection, 6.7% tested negative for anti-hepatitis B virus surface antigen (anti-HBsAg) antibodies, and 91.8% tested positive for anti-HBsAg. Self-reported vaccination history had a positive predictive value of 93.2% for test results positive for immunity against hepatitis B. Immunity against varicella (93.7% of subjects) and rubella (95.5%) was high, compared with immunity against mumps (79.9%) and measles (83.1%). In addition, results of tests for detection of immunity against mumps and measles were equivocal for more than 7% of subjects, probably because their vaccination regimen was not completed. Self-reported histories of varicella disease and rubella disease and vaccination had high positive predictive values (greater than 98% each) for testing positive for antiviral antibodies, compared with self-reported histories of mumps disease and vaccination and measles disease and vaccination; however, high positive predictive values were observed for self-reported histories of mumps only (92.0%) and measles only (94.7%). The self-report questionnaire used in this study did not accurately predict immunity against 5 transmittable but vaccine-preventable diseases. A complete serological evaluation of healthcare workers, followed by vaccination of those with negative or equivocal results of serological tests, is an appropriate measure to decrease the risk of infection in this population.

  3. Influence of food intake on the predictive value of the gestational diabetes mellitus screening test.

    Science.gov (United States)

    Wang, Panchalli; Lu, Mei-Chun; Yu, Cheng-Wei; Wang, Li-Chu; Yan, Yuan-Horng

    2013-04-01

    To investigate the influence of prior food ingestion on the predictive value of a screening test for gestational diabetes mellitus. This prospective, nonrandomized study enrolled 1,387 pregnant women who underwent the 50-g glucose challenge test. Gestational diabetes mellitus was diagnosed according to the National Diabetes Data Group criteria. A nutritional survey of dietary information before the glucose challenge test was conducted. The patients were stratified into three groups based on the time of last food ingestion (fasting interval): 1 hour or less, 1-2 hours, and more than 2 hours. The more than 2-hours fasting interval group was defined as the "fasting" group, and the combined 1 hour or less and 1-2 hours fasting interval groups were defined as the "fed" group. We calculated the positivity rate and the positive predictive value to detect the predictive value. Among women who fasted 1 hour or less, 1-2 hours, and more than 2 hours before a glucose challenge test, 2.5%, 3.1%, and 6.9% were diagnosed with gestational diabetes mellitus, respectively. The positive predictive value of the glucose challenge test was greater in the fasting group than in the fed group (27.1% compared with 13.7%, P=.003). A multinomial logistic analysis showed that gestational diabetes mellitus was more prevalent in the fasting group than in the fed group (adjusted odds ratio 2.86, 95% confidence interval 1.65-4.95). Our findings suggest that food intake influences the predictive value of the gestational diabetes screening test. II.

  4. A prospective analysis of physical examination findings in the diagnosis of facial fractures: Determining predictive value.

    Science.gov (United States)

    Timashpolsky, Alisa; Dagum, Alexander B; Sayeed, Syed M; Romeiser, Jamie L; Rosenfeld, Elisheva A; Conkling, Nicole

    2016-01-01

    There are >150,000 patient visits per year to emergency rooms for facial trauma. The reliability of a computed tomography (CT) scan has made it the primary modality for diagnosing facial skeletal injury, with the physical examination playing more a cursory role. Knowing the predictive value of physical findings in facial skeletal injuries may enable more appropriate use of imaging and health care resources. A blinded prospective study was undertaken to assess the predictive value of physical examination findings in detecting maxillofacial fracture in trauma patients, and in determining whether a patient will require surgical intervention. Over a four-month period, the authors' team examined patients admitted with facial trauma to the emergency department of their hospital. The evaluating physician completed a standardized physical examination evaluation form indicating the physical findings. Corresponding CT scans and surgical records were then reviewed, and the results recorded by a plastic surgeon who was blinded to the results of the physical examination. A total of 57 patients met the inclusion criteria; there were 44 male and 13 female patients. The sensitivity, specificity, positive predictive value and negative predictive value of grouped physical examination findings were determined in major areas. In further analysis, specific examination findings with n≥9 (15%) were also reported. The data demonstrated a high negative predictive value of at least 90% for orbital floor, zygomatic, mandibular and nasal bone fractures compared with CT scan. Furthermore, none of the patients who did not have a physical examination finding for a particular facial fracture required surgery for that fracture. Thus, the instrument performed well at ruling out fractures in these areas when there were none. Ultimately, these results may help reduce unnecessary radiation and costly imaging in patients with facial trauma without facial fractures.

  5. Predictive Models for Photovoltaic Electricity Production in Hot Weather Conditions

    Directory of Open Access Journals (Sweden)

    Jabar H. Yousif

    2017-07-01

    Full Text Available The process of finding a correct forecast equation for photovoltaic electricity production from renewable sources is an important matter, since knowing the factors affecting the increase in the proportion of renewable energy production and reducing the cost of the product has economic and scientific benefits. This paper proposes a mathematical model for forecasting energy production in photovoltaic (PV panels based on a self-organizing feature map (SOFM model. The proposed model is compared with other models, including the multi-layer perceptron (MLP and support vector machine (SVM models. Moreover, a mathematical model based on a polynomial function for fitting the desired output is proposed. Different practical measurement methods are used to validate the findings of the proposed neural and mathematical models such as mean square error (MSE, mean absolute error (MAE, correlation (R, and coefficient of determination (R2. The proposed SOFM model achieved a final MSE of 0.0007 in the training phase and 0.0005 in the cross-validation phase. In contrast, the SVM model resulted in a small MSE value equal to 0.0058, while the MLP model achieved a final MSE of 0.026 with a correlation coefficient of 0.9989, which indicates a strong relationship between input and output variables. The proposed SOFM model closely fits the desired results based on the R2 value, which is equal to 0.9555. Finally, the comparison results of MAE for the three models show that the SOFM model achieved a best result of 0.36156, whereas the SVM and MLP models yielded 4.53761 and 3.63927, respectively. A small MAE value indicates that the output of the SOFM model closely fits the actual results and predicts the desired output.

  6. Diagnostic value of hyperfibrinogenemia as a predictive factor for appendiceal perforation in acute appendicitis.

    Science.gov (United States)

    Zhao, Lingling; Feng, Shaoguang; Huang, Songsong; Tong, Yulong; Chen, Zhongliang; Wu, Peng; Lai, Xin-He; Chen, Xiaoming

    2017-05-01

    Acute appendicitis is one of the most common emergency requiring operation. As the first discovered coagulation factor, plasma fibrinogen frequently increases with inflammation due to the activation of coagulation. The aim of this retrospective study was to investigate the diagnostic value of hyperfibrinogenemia as a preoperative laboratory marker for appendiceal perforation in patients with acute appendicitis. We identified 455 patients (202 females, 253 males; mean age, 31.7 years) with histologically confirmed acute appendicitis who underwent laparoscopic or open appendectomy. Results of preoperative laboratory values and post-operative histologic results were analysed retrospectively. A multivariate logistic regression model was performed to determine patient's age and laboratory tests associated with perforated appendicitis. Mean plasma fibrinogen level of all patients was 3.99 g/L (1.41 SD; range, 1.73-10.6 g/L; median, 3.69 g/L). Patients with appendiceal perforation had a mean fibrinogen level of 5.72 g/L (1.52 SD; range, 3.38-10.04 g/L; median, 5.28 g/L), which was significantly higher than those with nonperforated groups (P = 0.001). Multivariate analysis showed fibrinogen and D-dimer were associated with perforation (P = 0.001, P = 0.014, respectively). Areas under the receiver operating characteristic curve of fibrinogen for discriminating acute perforated appendicitis from non-perforated groups were larger than white blood cell and D-dimer. Hyperfibrinogenemia was common in patients with acute appendicitis and fibrinogen may be useful as a predictive factor for appendiceal perforation. © 2015 Royal Australasian College of Surgeons.

  7. Regional differences in prediction models of lung function in Germany

    Directory of Open Access Journals (Sweden)

    Schäper Christoph

    2010-04-01

    Full Text Available Abstract Background Little is known about the influencing potential of specific characteristics on lung function in different populations. The aim of this analysis was to determine whether lung function determinants differ between subpopulations within Germany and whether prediction equations developed for one subpopulation are also adequate for another subpopulation. Methods Within three studies (KORA C, SHIP-I, ECRHS-I in different areas of Germany 4059 adults performed lung function tests. The available data consisted of forced expiratory volume in one second, forced vital capacity and peak expiratory flow rate. For each study multivariate regression models were developed to predict lung function and Bland-Altman plots were established to evaluate the agreement between predicted and measured values. Results The final regression equations for FEV1 and FVC showed adjusted r-square values between 0.65 and 0.75, and for PEF they were between 0.46 and 0.61. In all studies gender, age, height and pack-years were significant determinants, each with a similar effect size. Regarding other predictors there were some, although not statistically significant, differences between the studies. Bland-Altman plots indicated that the regression models for each individual study adequately predict medium (i.e. normal but not extremely high or low lung function values in the whole study population. Conclusions Simple models with gender, age and height explain a substantial part of lung function variance whereas further determinants add less than 5% to the total explained r-squared, at least for FEV1 and FVC. Thus, for different adult subpopulations of Germany one simple model for each lung function measures is still sufficient.

  8. Models of consumer value cocreation in health care.

    Science.gov (United States)

    Nambisan, Priya; Nambisan, Satish

    2009-01-01

    In recent years, consumer participation in health care has gained critical importance as health care organizations (HCOs) seek varied avenues to enhance the quality and the value of their offerings. Many large HCOs have established online health communities where health care consumers (patients) can interact with one another to share knowledge and offer emotional support in disease management and care. Importantly, the focus of consumer participation in health care has moved beyond such personal health care management as the potential for consumers to participate in innovation and value creation in varied areas of the health care industry becomes increasingly evident. Realizing such potential, however, will require HCOs to develop a better understanding of the varied types of consumer value cocreation that are enabled by new information and communication technologies such as online health communities and Web 2.0 (social media) technologies. This article seeks to contribute toward such an understanding by offering a concise and coherent theoretical framework to analyze consumer value cocreation in health care. We identify four alternate models of consumer value cocreation-the partnership model, the open-source model, the support-group model, and the diffusion model-and discuss their implications for HCOs. We develop our theoretical framework by drawing on theories and concepts in knowledge creation, innovation management, and online communities. A set of propositions are developed by combining theoretical insights from these areas with real-world examples of consumer value cocreation in health care. The theoretical framework offered here informs on the potential impact of the different models of consumer value cocreation on important organizational variables such as innovation cost and time, service quality, and consumer perceptions of HCO. An understanding of the four models of consumer value cocreation can help HCOs adopt appropriate strategies and practices to

  9. Modeling and prediction of Turkey's electricity consumption using Support Vector Regression

    International Nuclear Information System (INIS)

    Kavaklioglu, Kadir

    2011-01-01

    Support Vector Regression (SVR) methodology is used to model and predict Turkey's electricity consumption. Among various SVR formalisms, ε-SVR method was used since the training pattern set was relatively small. Electricity consumption is modeled as a function of socio-economic indicators such as population, Gross National Product, imports and exports. In order to facilitate future predictions of electricity consumption, a separate SVR model was created for each of the input variables using their current and past values; and these models were combined to yield consumption prediction values. A grid search for the model parameters was performed to find the best ε-SVR model for each variable based on Root Mean Square Error. Electricity consumption of Turkey is predicted until 2026 using data from 1975 to 2006. The results show that electricity consumption can be modeled using Support Vector Regression and the models can be used to predict future electricity consumption. (author)

  10. The predictive value of the NICE "red traffic lights" in acutely ill children.

    Directory of Open Access Journals (Sweden)

    Evelien Kerkhof

    Full Text Available OBJECTIVE: Early recognition and treatment of febrile children with serious infections (SI improves prognosis, however, early detection can be difficult. We aimed to validate the predictive rule-in value of the National Institute for Health and Clinical Excellence (NICE most severe alarming signs or symptoms to identify SI in children. DESIGN, SETTING AND PARTICIPANTS: The 16 most severe ("red" features of the NICE traffic light system were validated in seven different primary care and emergency department settings, including 6,260 children presenting with acute illness. MAIN OUTCOME MEASURES: We focussed on the individual predictive value of single red features for SI and their combinations. Results were presented as positive likelihood ratios, sensitivities and specificities. We categorised "general" and "disease-specific" red features. Changes in pre-test probability versus post-test probability for SI were visualised in Fagan nomograms. RESULTS: Almost all red features had rule-in value for SI, but only four individual red features substantially raised the probability of SI in more than one dataset: "does not wake/stay awake", "reduced skin turgor", "non-blanching rash", and "focal neurological signs". The presence of ≥ 3 red features improved prediction of SI but still lacked strong rule-in value as likelihood ratios were below 5. CONCLUSIONS: The rule-in value of the most severe alarming signs or symptoms of the NICE traffic light system for identifying children with SI was limited, even when multiple red features were present. Our study highlights the importance of assessing the predictive value of alarming signs in clinical guidelines prior to widespread implementation in routine practice.

  11. Defining the cutoff value of MGMT gene promoter methylation and its predictive capacity in glioblastoma.

    Science.gov (United States)

    Brigliadori, Giovanni; Foca, Flavia; Dall'Agata, Monia; Rengucci, Claudia; Melegari, Elisabetta; Cerasoli, Serenella; Amadori, Dino; Calistri, Daniele; Faedi, Marina

    2016-06-01

    Despite advances in the treatment of glioblastoma (GBM), median survival is 12-15 months. O6-methylguanine-DNA methyltransferase (MGMT) gene promoter methylation status is acknowledged as a predictive marker for temozolomide (TMZ) treatment. When MGMT promoter values fall into a "methylated" range, a better response to chemotherapy is expected. However, a cutoff that discriminates between "methylated" and "unmethylated" status has yet to be defined. We aimed to identify the best cutoff value and to find out whether variability in methylation profiles influences the predictive capacity of MGMT promoter methylation. Data from 105 GBM patients treated between 2008 and 2013 were analyzed. MGMT promoter methylation status was determined by analyzing 10 CpG islands by pyrosequencing. Patients were treated with radiotherapy followed by TMZ. MGMT promoter methylation status was classified into unmethylated 0-9 %, methylated 10-29 % and methylated 30-100 %. Statistical analysis showed that an assumed methylation cutoff of 9 % led to an overestimation of responders. All patients in the 10-29 % methylation group relapsed before the 18-month evaluation. Patients with a methylation status ≥30 % showed a median overall survival of 25.2 months compared to 15.2 months in all other patients, confirming this value as the best methylation cutoff. Despite wide variability among individual profiles, single CpG island analysis did not reveal any correlation between single CpG island methylation values and relapse or death. Specific CpG island methylation status did not influence the predictive value of MGMT. The predictive role of MGMT promoter methylation was maintained only with a cutoff value ≥30 %.

  12. Risk reclassification analysis investigating the added value of fatigue to sickness absence predictions

    NARCIS (Netherlands)

    Roelen, C.A.M.; Bultmann, U.; Groothoff, J.W.; Twisk, J.W.R.; Heymans, M.W.

    2015-01-01

    Background: Prognostic models including age, self-rated health and prior sickness absence (SA) have been found to predict high (≥30) SA days and high (≥3) SA episodes during 1-year follow-up. More predictors of high SA are needed to improve these SA prognostic models. The purpose of this study was

  13. A two step Bayesian approach for genomic prediction of breeding values

    DEFF Research Database (Denmark)

    Mahdi Shariati, Mohammad; Sørensen, Peter; Janss, Luc

    2012-01-01

    -MAS workshop were analyzed such that SNP markers were ranked based on their effects and markers with similar estimated effects were grouped together. In step 1, all markers with minor allele frequency more than 0.01 were included in a SNP-BLUP prediction model. In step 2, markers were ranked based...... on their estimated variance on the trait in step 1 and each 150 markers were assigned to one group with a common variance. In further analyses, subsets of 1500 and 450 markers with largest effects in step 2 were kept in the prediction model. Results: Grouping markers outperformed SNP-BLUP model in terms of accuracy...

  14. In Silico Modeling of Gastrointestinal Drug Absorption: Predictive Performance of Three Physiologically Based Absorption Models.

    Science.gov (United States)

    Sjögren, Erik; Thörn, Helena; Tannergren, Christer

    2016-06-06

    Gastrointestinal (GI) drug absorption is a complex process determined by formulation, physicochemical and biopharmaceutical factors, and GI physiology. Physiologically based in silico absorption models have emerged as a widely used and promising supplement to traditional in vitro assays and preclinical in vivo studies. However, there remains a lack of comparative studies between different models. The aim of this study was to explore the strengths and limitations of the in silico absorption models Simcyp 13.1, GastroPlus 8.0, and GI-Sim 4.1, with respect to their performance in predicting human intestinal drug absorption. This was achieved by adopting an a priori modeling approach and using well-defined input data for 12 drugs associated with incomplete GI absorption and related challenges in predicting the extent of absorption. This approach better mimics the real situation during formulation development where predictive in silico models would be beneficial. Plasma concentration-time profiles for 44 oral drug administrations were calculated by convolution of model-predicted absorption-time profiles and reported pharmacokinetic parameters. Model performance was evaluated by comparing the predicted plasma concentration-time profiles, Cmax, tmax, and exposure (AUC) with observations from clinical studies. The overall prediction accuracies for AUC, given as the absolute average fold error (AAFE) values, were 2.2, 1.6, and 1.3 for Simcyp, GastroPlus, and GI-Sim, respectively. The corresponding AAFE values for Cmax were 2.2, 1.6, and 1.3, respectively, and those for tmax were 1.7, 1.5, and 1.4, respectively. Simcyp was associated with underprediction of AUC and Cmax; the accuracy decreased with decreasing predicted fabs. A tendency for underprediction was also observed for GastroPlus, but there was no correlation with predicted fabs. There were no obvious trends for over- or underprediction for GI-Sim. The models performed similarly in capturing dependencies on dose and

  15. Added value of anti-Müllerian hormone in prediction of menopause: results from a large prospective cohort study.

    Science.gov (United States)

    Dólleman, Madeleine; Verschuren, W M Monique; Eijkemans, Marinus J C; Broekmans, Frank J M; van der Schouw, Yvonne T

    2015-08-01

    What is the added value of anti-Müllerian hormone (AMH) on top of patient characteristics for predicting the risk to enter menopause within 10 years? For women who did enter menopause, the risk of entering menopause within 10 years assigned by the model with AMH was on average 3% higher than that assigned by the model without AMH, and in the subgroup of young women with regular cycles, this increase was 14%. Prediction of age at menopause may be useful in predicting the end of female fertility. AMH may be useful for this, but the current evidence is based on small studies or specific subgroups, and does not take into account predictors other than age. This was a retrospective cohort study among 1163 premenopausal women participating in the second follow-up round of the Doetinchem Cohort Study with follow-up assessments of menopausal status and age after 5 and 10 years of follow-up. This study included premenopausal women from the general population with a mean age of 41 (SD 7) years. A Cox proportional hazards' model without AMH was fitted using variables selected based on Akaike's information criterion. Performance of the prediction rule was assessed with C-statistics and compared with a model additionally including AMH and to a model with age only. The added value of AMH was assessed with Net Reclassification Index and change in absolute predicted risk. Performance of these three models was compared in subgroups based on age and reproductive characteristics. The final model included age, BMI, packyears of smoking and menstrual cycle status (regular, irregular, pregnant or taking oral contraceptives). This model had a C-statistic of 0.89 (0.01 SD), compared with 0.88 (0.01 SD) for the model including age only. Addition of AMH increased it to 0.91 (0.03 SD). In a subgroup of 25-43 year olds with regular menstrual cycles, the model with age only had a C-statistic of 0.79 (0.04 SD) and for the models without and with AMH the C-stastic was 0.79 (0.04 SD) and 0.87 (0

  16. Can pretreatment ADC values predict recurrence of bladder cancer after transurethral resection?

    Energy Technology Data Exchange (ETDEWEB)

    Funatsu, Hiroyuki, E-mail: hirofunatsu999@hotmail.com [Division of Diagnostic Imaging, Chiba Cancer Center, 666-2 Nitona-cho, Chuo-ku, Chiba 260-8717 (Japan); Imamura, Akihiro; Takano, Hideyuki [Division of Diagnostic Imaging, Chiba Cancer Center, 666-2 Nitona-cho, Chuo-ku, Chiba 260-8717 (Japan); Ueda, Takeshi [Division of Urology, Chiba Cancer Center, 666-2 Nitona-cho, Chuo-ku, Chiba 260-8717 (Japan); Uno, Takashi [Department of Radiology, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuou-ku, Chiba 260-8670 (Japan)

    2012-11-15

    Objective: The aim of this retrospective study was to investigate the association between the pretreatment apparent diffusion coefficient (ADC) value and recurrence of bladder cancer after transurethral resection. Methods: Patients with superficial bladder cancer were identified. Mean ADC values of the tumors were compared between patients with and without recurrence following trans-urethral resection. A receiver-operator characteristic curve was used for determining the optimal cutoff ADC value. Univariate and multivariate analyses were used to determine the effect of ADC values and other factors. Results: With a mean follow-up period of 25 months, bladder cancer recurred in 14 of 44 patients (32%). The mean ADC value of tumors in patients with recurrence was lower than in those without recurrence (1.08 mm{sup 2}/s vs. 1.28 Multiplication-Sign 10{sup -3} mm{sup 2}/s; p = 0.003). The optimal cutoff ADC value for predicting recurrence was determined to be 1.12 Multiplication-Sign 10{sup -3} mm{sup 2}/s. A modest and significant negative correlation was observed between the ADC values and tumor size (r = -0.436, p = 0.008). After adjustment for size and risk groups, an ADC value equal to or less than the optimal cutoff remained a significant predictor of recurrence (odds ratio 6.3, 95% CI 1.23-32.2, p = 0.027). Conclusion: Pretreatment ADC values may be an independent predictor of bladder cancer recurrence.

  17. Can pretreatment ADC values predict recurrence of bladder cancer after transurethral resection?

    International Nuclear Information System (INIS)

    Funatsu, Hiroyuki; Imamura, Akihiro; Takano, Hideyuki; Ueda, Takeshi; Uno, Takashi

    2012-01-01

    Objective: The aim of this retrospective study was to investigate the association between the pretreatment apparent diffusion coefficient (ADC) value and recurrence of bladder cancer after transurethral resection. Methods: Patients with superficial bladder cancer were identified. Mean ADC values of the tumors were compared between patients with and without recurrence following trans-urethral resection. A receiver–operator characteristic curve was used for determining the optimal cutoff ADC value. Univariate and multivariate analyses were used to determine the effect of ADC values and other factors. Results: With a mean follow-up period of 25 months, bladder cancer recurred in 14 of 44 patients (32%). The mean ADC value of tumors in patients with recurrence was lower than in those without recurrence (1.08 mm 2 /s vs. 1.28 × 10 −3 mm 2 /s; p = 0.003). The optimal cutoff ADC value for predicting recurrence was determined to be 1.12 × 10 −3 mm 2 /s. A modest and significant negative correlation was observed between the ADC values and tumor size (r = −0.436, p = 0.008). After adjustment for size and risk groups, an ADC value equal to or less than the optimal cutoff remained a significant predictor of recurrence (odds ratio 6.3, 95% CI 1.23–32.2, p = 0.027). Conclusion: Pretreatment ADC values may be an independent predictor of bladder cancer recurrence.

  18. Predictive Modeling by the Cerebellum Improves Proprioception

    Science.gov (United States)

    Bhanpuri, Nasir H.; Okamura, Allison M.

    2013-01-01

    Because sensation is delayed, real-time movement control requires not just sensing, but also predicting limb position, a function hypothesized for the cerebellum. Such cerebellar predictions could contribute to perception of limb position (i.e., proprioception), particularly when a person actively moves the limb. Here we show that human cerebellar patients have proprioceptive deficits compared with controls during active movement, but not when the arm is moved passively. Furthermore, when healthy subjects move in a force field with unpredictable dynamics, they have active proprioceptive deficits similar to cerebellar patients. Therefore, muscle activity alone is likely insufficient to enhance proprioception and predictability (i.e., an internal model of the body and environment) is important for active movement to benefit proprioception. We conclude that cerebellar patients have an active proprioceptive deficit consistent with disrupted movement prediction rather than an inability to generally enhance peripheral proprioceptive signals during action and suggest that active proprioceptive deficits should be considered a fundamental cerebellar impairment of clinical importance. PMID:24005283

  19. [Healthcare value chain: a model for the Brazilian healthcare system].

    Science.gov (United States)

    Pedroso, Marcelo Caldeira; Malik, Ana Maria

    2012-10-01

    This article presents a model of the healthcare value chain which consists of a schematic representation of the Brazilian healthcare system. The proposed model is adapted for the Brazilian reality and has the scope and flexibility for use in academic activities and analysis of the healthcare sector in Brazil. It places emphasis on three components: the main activities of the value chain, grouped in vertical and horizontal links; the mission of each link and the main value chain flows. The proposed model consists of six vertical and three horizontal links, amounting to nine. These are: knowledge development; supply of products and technologies; healthcare services; financial intermediation; healthcare financing; healthcare consumption; regulation; distribution of healthcare products; and complementary and support services. Four flows can be used to analyze the value chain: knowledge and innovation; products and services; financial; and information.

  20. Nonlinear Model Predictive Control for Oil Reservoirs Management

    DEFF Research Database (Denmark)

    Capolei, Andrea

    . The controller consists of -A model based optimizer for maximizing some predicted financial measure of the reservoir (e.g. the net present value). -A parameter and state estimator. -Use of the moving horizon principle for data assimilation and implementation of the computed control input. The optimizer uses...... Optimization has been suggested to compensate for inherent geological uncertainties in an oil field. In robust optimization of an oil reservoir, the water injection and production borehole pressures are computed such that the predicted net present value of an ensemble of permeability field realizations...... equivalent strategy is not justified for the particular case studied in this paper. The third contribution of this thesis is a mean-variance method for risk mitigation in production optimization of oil reservoirs. We introduce a return-risk bicriterion objective function for the profit-risk tradeoff...

  1. Integer Valued Autoregressive Models for Tipping Bucket Rainfall Measurements

    DEFF Research Database (Denmark)

    Thyregod, Peter; Carstensen, Niels Jacob; Madsen, Henrik

    1999-01-01

    A new method for modelling the dynamics of rain sampled by a tipping bucket rain gauge is proposed. The considered models belong to the class of integer valued autoregressive processes. The models take the autocorelation and discrete nature of the data into account. A first order, a second order...... and a threshold model are presented together with methods to estimate the parameters of each model. The models are demonstrated to provide a good description of dt from actual rain events requiring only two to four parameters....

  2. Sensitivity, specificity, predictive value and accuracy of ultrasonography in pregnancy rate prediction in Sahelian goats after progesterone impregnated sponge synchronization

    Directory of Open Access Journals (Sweden)

    Justin Kouamo

    2014-09-01

    Full Text Available Aim: This study was aimed to evaluate the sensitivity, specificity, predictive value and accuracy of ultrasonography in pregnancy rate (PR prediction in Sahelian goats after progesterone impregnated sponge synchronization within the framework of caprine artificial insemination (AI program in Fatick (Senegal. Materials and Methods: Of 193 candidate goats in AI program, 167 were selected (day 50 in six villages. Estrus was synchronized by progesterone impregnated sponges installed for 11 days. Two days before the time of sponge removal (day 4, each goat was treated with 500 IU of equine chorionic gonadotropin and 50 μg of dcloprostenol. All goats were inseminated (day 0 with alpine goat semen from France at 45±3 h after sponge removal (day 2. Real-time B-mode ultrasonography was performed at day 50, day 13, day 0, day 40 and day 60 post-AI. Results: Selection rate, estrus response rate, AI rate, PR at days 40 and days 60 were 86.53%; 71.85%; 83.34%; 51% and 68% (p<0.05 respectively. Value of sensitivity, specificity, positive and negative predictive value, accuracy, total conformity, conformity of correct positive, conformity of correct negative and discordance of pregnancy diagnosis by trans-abdominal ultrasonography (TU were 98.03%; 63.26%; 73.52%; 3.12%; 81%; 81%; 50%; 31% and 19%, respectively. Conclusion: These results indicate that the TU can be performed in goats under traditional condition and emphasized the importance of re-examination of goats with negative or doubtful TU diagnoses performed at day 40 post-AI.

  3. Prediction of Chemical Function: Model Development and ...

    Science.gov (United States)

    The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (HT) screening-level exposures developed under ExpoCast can be combined with HT screening (HTS) bioactivity data for the risk-based prioritization of chemicals for further evaluation. The functional role (e.g. solvent, plasticizer, fragrance) that a chemical performs can drive both the types of products in which it is found and the concentration in which it is present and therefore impacting exposure potential. However, critical chemical use information (including functional role) is lacking for the majority of commercial chemicals for which exposure estimates are needed. A suite of machine-learning based models for classifying chemicals in terms of their likely functional roles in products based on structure were developed. This effort required collection, curation, and harmonization of publically-available data sources of chemical functional use information from government and industry bodies. Physicochemical and structure descriptor data were generated for chemicals with function data. Machine-learning classifier models for function were then built in a cross-validated manner from the descriptor/function data using the method of random forests. The models were applied to: 1) predict chemi

  4. Gamma-Ray Pulsars Models and Predictions

    CERN Document Server

    Harding, A K

    2001-01-01

    Pulsed emission from gamma-ray pulsars originates inside the magnetosphere, from radiation by charged particles accelerated near the magnetic poles or in the outer gaps. In polar cap models, the high energy spectrum is cut off by magnetic pair production above an energy that is dependent on the local magnetic field strength. While most young pulsars with surface fields in the range B = 10^{12} - 10^{13} G are expected to have high energy cutoffs around several GeV, the gamma-ray spectra of old pulsars having lower surface fields may extend to 50 GeV. Although the gamma-ray emission of older pulsars is weaker, detecting pulsed emission at high energies from nearby sources would be an important confirmation of polar cap models. Outer gap models predict more gradual high-energy turnovers at around 10 GeV, but also predict an inverse Compton component extending to TeV energies. Detection of pulsed TeV emission, which would not survive attenuation at the polar caps, is thus an important test of outer gap models. N...

  5. A prediction model for Clostridium difficile recurrence

    Directory of Open Access Journals (Sweden)

    Francis D. LaBarbera

    2015-02-01

    Full Text Available Background: Clostridium difficile infection (CDI is a growing problem in the community and hospital setting. Its incidence has been on the rise over the past two decades, and it is quickly becoming a major concern for the health care system. High rate of recurrence is one of the major hurdles in the successful treatment of C. difficile infection. There have been few studies that have looked at patterns of recurrence. The studies currently available have shown a number of risk factors associated with C. difficile recurrence (CDR; however, there is little consensus on the impact of most of the identified risk factors. Methods: Our study was a retrospective chart review of 198 patients diagnosed with CDI via Polymerase Chain Reaction (PCR from February 2009 to Jun 2013. In our study, we decided to use a machine learning algorithm called the Random Forest (RF to analyze all of the factors proposed to be associated with CDR. This model is capable of making predictions based on a large number of variables, and has outperformed numerous other models and statistical methods. Results: We came up with a model that was able to accurately predict the CDR with a sensitivity of 83.3%, specificity of 63.1%, and area under curve of 82.6%. Like other similar studies that have used the RF model, we also had very impressive results. Conclusions: We hope that in the future, machine learning algorithms, such as the RF, will see a wider application.

  6. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif

    2013-05-01

    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  7. Evaluating predictive models of software quality

    International Nuclear Information System (INIS)

    Ciaschini, V; Canaparo, M; Ronchieri, E; Salomoni, D

    2014-01-01

    Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.

  8. A generative model for predicting terrorist incidents

    Science.gov (United States)

    Verma, Dinesh C.; Verma, Archit; Felmlee, Diane; Pearson, Gavin; Whitaker, Roger

    2017-05-01

    A major concern in coalition peace-support operations is the incidence of terrorist activity. In this paper, we propose a generative model for the occurrence of the terrorist incidents, and illustrate that an increase in diversity, as measured by the number of different social groups to which that an individual belongs, is inversely correlated with the likelihood of a terrorist incident in the society. A generative model is one that can predict the likelihood of events in new contexts, as opposed to statistical models which are used to predict the future incidents based on the history of the incidents in an existing context. Generative models can be useful in planning for persistent Information Surveillance and Reconnaissance (ISR) since they allow an estimation of regions in the theater of operation where terrorist incidents may arise, and thus can be used to better allocate the assignment and deployment of ISR assets. In this paper, we present a taxonomy of terrorist incidents, identify factors related to occurrence of terrorist incidents, and provide a mathematical analysis calculating the likelihood of occurrence of terrorist incidents in three common real-life scenarios arising in peace-keeping operations

  9. PREDICTION MODELS OF GRAIN YIELD AND CHARACTERIZATION

    Directory of Open Access Journals (Sweden)

    Narciso Ysac Avila Serrano

    2009-06-01

    Full Text Available With the objective to characterize the grain yield of five cowpea cultivars and to find linear regression models to predict it, a study was developed in La Paz, Baja California Sur, Mexico. A complete randomized blocks design was used. Simple and multivariate analyses of variance were carried out using the canonical variables to characterize the cultivars. The variables cluster per plant, pods per plant, pods per cluster, seeds weight per plant, seeds hectoliter weight, 100-seed weight, seeds length, seeds wide, seeds thickness, pods length, pods wide, pods weight, seeds per pods, and seeds weight per pods, showed significant differences (P≤ 0.05 among cultivars. Paceño and IT90K-277-2 cultivars showed the higher seeds weight per plant. The linear regression models showed correlation coefficients ≥0.92. In these models, the seeds weight per plant, pods per cluster, pods per plant, cluster per plant and pods length showed significant correlations (P≤ 0.05. In conclusion, the results showed that grain yield differ among cultivars and for its estimation, the prediction models showed determination coefficients highly dependable.

  10. The Predictive Value of Adrenomedullin for Development of Severe Sepsis and Septic Shock in Emergency Department

    Directory of Open Access Journals (Sweden)

    Yun-Xia Chen

    2013-01-01

    Full Text Available Objective. The aim of the study was to assess adrenomedullin (AM as a predictor for development of severe sepsis and septic shock in emergency department (ED. Method. From December 2011 to October 2012, 372 consecutive septic patients admitted to ED were enrolled. AM was examined in every patient. All patients were followed up for 3 days. The outcome variable was development of severe sepsis or septic shock. The predictive ability of AM was evaluated by binary logistic regression analysis and receiver operating characteristic (ROC curve. Result. On admission, the differences of AM among patients with different comorbidities, infections, and culture results were not significant. AM level was higher in patients who progressed than in who did not (41.63 ± 6.55 versus 31.31 ± 7.71 ng/L, . AM was the only independent predictor of outcome. The area under ROC curve of AM was 0.847. With a cutoff value of 41.24 ng/L, the sensitivity was 67.6%, the specificity was 90.0%, the positive predictive value was 61.5%, the negative predictive value was 92.2%, the positive likelihood ratio was 6.78, and the negative likelihood ratio was 0.36. Conclusion. Adrenomedullin is valuable for predicting development of severe sepsis and septic shock in ED.

  11. Creating Value Through the Freemium Business Model: A Consumer Perspective

    NARCIS (Netherlands)

    G.J. Rietveld (Joost)

    2016-01-01

    textabstractThis paper develops a consumer-centric framework for creating value through the freemium business model. Goods that are commercialized through the freemium business model offer basic functionality for free and monetize users for extended use or complementary features. Compared to premium

  12. The Meaning and Predictive Value of Self-rated Mental Health among Persons with a Mental Health Problem.

    Science.gov (United States)

    McAlpine, Donna D; McCreedy, Ellen; Alang, Sirry

    2018-02-01

    Self-rated health is a valid measure of health that predicts quality of life, morbidity, and mortality. Its predictive value reflects a conceptualization of health that goes beyond a traditional medical model. However, less is known about self-rated mental health (SRMH). Using data from the Medical Expenditure Panel Survey ( N = 2,547), we examine how rating your mental health as good-despite meeting criteria for a mental health problem-predicts outcomes. We found that 62% of people with a mental health problem rated their mental health positively. Persons who rated their mental health as good (compared to poor) had 30% lower odds of having a mental health problem at follow-up. Even without treatment, persons with a mental health problem did better if they perceived their mental health positively. SRMH might comprise information beyond the experience of symptoms. Understanding the unobserved information individuals incorporate into SRMH will help us improve screening and treatment interventions.

  13. Angiogenic Markers Predict Pregnancy Complications and Prolongation in Preeclampsia: Continuous Versus Cutoff Values.

    Science.gov (United States)

    Saleh, Langeza; Vergouwe, Yvonne; van den Meiracker, Anton H; Verdonk, Koen; Russcher, Henk; Bremer, Henk A; Versendaal, Hans J; Steegers, Eric A P; Danser, A H Jan; Visser, Willy

    2017-11-01

    To assess the incremental value of a single determination of the serum levels of sFlt-1 (soluble Fms-like tyrosine kinase 1) and PlGF (placental growth factor) or their ratio, without using cutoff values, for the prediction of maternal and fetal/neonatal complications and pregnancy prolongation, 620 women with suspected/confirmed preeclampsia, aged 18 to 48 years, were included in a prospective, multicenter, observational cohort study. Women had singleton pregnancies and a median pregnancy duration of 34 (range, 20-41) weeks. Complications occurred in 118 women and 248 fetuses. The median duration between admission and delivery was 12 days. To predict prolongation, PlGF showed the highest incremental value ( R 2 =0.72) on top of traditional predictors (gestational age at inclusion, diastolic blood pressure, proteinuria, creatinine, uric acid, alanine transaminase, lactate dehydrogenase, and platelets) compared with R 2 =0.53 for the traditional predictors only. sFlt-1 showed the highest value to discriminate women with and without maternal complications (C-index=0.83 versus 0.72 for the traditional predictors only), and the sFlt-1/PlGF ratio showed the highest value to discriminate fetal/neonatal complications (C-index=0.86 versus 0.78 for the traditional predictors only). Applying previously suggested cutoff values for the sFlt-1/PlGF ratio yielded lower incremental values than applying continuous values. In conclusion, sFlt-1 and PlGF are strong and independent predictors for days until delivery along with maternal and fetal/neonatal complications on top of the traditional criteria. Their use as continuous variables (instead of applying cutoff values for different gestational ages) should now be tested in a prospective manner, making use of an algorithm calculating the risk of an individual woman with suspected/confirmed preeclampsia to develop complications. © 2017 American Heart Association, Inc.

  14. Effects of model schematisation, geometry and parameter values on urban flood modelling.

    Science.gov (United States)

    Vojinovic, Z; Seyoum, S D; Mwalwaka, J M; Price, R K

    2011-01-01

    One-dimensional (1D) hydrodynamic models have been used as a standard industry practice for urban flood modelling work for many years. More recently, however, model formulations have included a 1D representation of the main channels and a 2D representation of the floodplains. Since the physical process of describing exchanges of flows with the floodplains can be represented in different ways, the predictive capability of different modelling approaches can also vary. The present paper explores effects of some of the issues that concern urban flood modelling work. Impacts from applying different model schematisation, geometry and parameter values were investigated. The study has mainly focussed on exploring how different Digital Terrain Model (DTM) resolution, presence of different features on DTM such as roads and building structures and different friction coefficients affect the simulation results. Practical implications of these issues are analysed and illustrated in a case study from St Maarten, N.A. The results from this study aim to provide users of numerical models with information that can be used in the analyses of flooding processes in urban areas.

  15. Predictive value of brain perfusion SPECT for rTMS response in pharmacoresistant depression

    Energy Technology Data Exchange (ETDEWEB)

    Richieri, Raphaelle; Lancon, Christophe [Sainte-Marguerite University Hospital, Department of Psychiatry, Marseille (France); La Timone University, EA 3279 - Self-perceived Health Assessment Research Unit, School of Medicine, Marseille (France); Boyer, Laurent [La Timone University, EA 3279 - Self-perceived Health Assessment Research Unit, School of Medicine, Marseille (France); La Timone University Hospital, Assistance Publique - Hopitaux de Marseille, Department of Public Health, Marseille (France); Farisse, Jean [Sainte-Marguerite University Hospital, Department of Psychiatry, Marseille (France); Colavolpe, Cecile; Mundler, Olivier [La Timone University Hospital, Assistance Publique - Hopitaux de Marseille, Service Central de Biophysique et Medecine Nucleaire, Marseille (France); Universite de la Mediterranee, Centre Europeen de Recherche en Imagerie Medicale (CERIMED), Marseille (France); Guedj, Eric [La Timone University Hospital, Assistance Publique - Hopitaux de Marseille, Service Central de Biophysique et Medecine Nucleaire, Marseille (France); Universite de la Mediterranee, Centre Europeen de Recherche en Imagerie Medicale (CERIMED), Marseille (France); Hopital de la Timone, Service Central de Biophysique et de Medecine Nucleaire, Marseille Cedex 5 (France)

    2011-09-15

    The aim of this study was to determine the predictive value of whole-brain voxel-based regional cerebral blood flow (rCBF) for repetitive transcranial magnetic stimulation (rTMS) response in patients with pharmacoresistant depression. Thirty-three right-handed patients who met DSM-IV criteria for major depressive disorder (unipolar or bipolar depression) were included before rTMS. rTMS response was defined as at least 50% reduction in the baseline Beck Depression Inventory scores. The predictive value of {sup 99m}Tc-ethyl cysteinate dimer (ECD) single photon emission computed tomography (SPECT) for rTMS response was studied before treatment by comparing rTMS responders to non-responders at voxel level using Statistical Parametric Mapping (SPM) (p < 0.001, uncorrected). Of the patients, 18 (54.5%) were responders to rTMS and 15 were non-responders (45.5%). There were no statistically significant differences in demographic and clinical characteristics (p > 0.10). In comparison to responders, non-responders showed significant hypoperfusions (p < 0.001, uncorrected) in the left medial and bilateral superior frontal cortices (BA10), the left uncus/parahippocampal cortex (BA20/BA35) and the right thalamus. The area under the curve for the combination of SPECT clusters to predict rTMS response was 0.89 (p < 0.001). Sensitivity, specificity, positive predictive value and negative predictive value for the combination of clusters were: 94, 73, 81 and 92%, respectively. This study shows that, in pharmacoresistant depression, pretreatment rCBF of specific brain regions is a strong predictor for response to rTMS in patients with homogeneous demographic/clinical features. (orig.)

  16. Hemostatic system changes predictive value in patients with ischemic brain disorders

    Directory of Open Access Journals (Sweden)

    Raičević Ranko

    2002-01-01

    Full Text Available The aim of this research was to determine the importance of tracking the dynamics of changes of the hemostatic system factors (aggregation of thrombocytes, D-dimer, PAI-1, antithrombin III, protein C and protein S, factor VII and factor VIII, fibrin degradation products, euglobulin test and the activated partial thromboplastin time – aPTPV in relation to the level of the severity of ischemic brain disorders (IBD and the level of neurological and functional deficiency in the beginning of IBD manifestation from 7 to 10 days, 19 to 21 day, and after 3 to 6 months. The research results confirmed significant predictive value of changes of hemostatic system with the predomination of procoagulant factors, together with the insufficiency of fibrinolysis. Concerning the IBD severity and it's outcome, the significant predictive value was shown in the higher levels of PAI-1 and the lower level of antithrombin III, and borderline significant value was shown in the accelerated aggregation of thrombocytes and the increased concentration of D-dimer. It could be concluded that the tracking of the dynamics of changes in parameters of hemostatic system proved to be an easily accessible method with the significant predictive value regarding the development of more severe. IBD cases and the outcome of the disease itself.

  17. Oculomotor capture is influenced by expected reward value but (maybe) not predictiveness.

    Science.gov (United States)

    Le Pelley, Mike E; Pearson, Daniel; Porter, Alexis; Yee, Hannah; Luque, David

    2017-04-04

    A large body of research has shown that learning about relationships between neutral stimuli and events of significance-rewards or punishments-influences the extent to which people attend to those stimuli in future. However, different accounts of this influence differ in terms of the critical variable that is proposed to determine learned changes in attention. We describe two experiments using eye-tracking with a rewarded visual search procedure to investigate whether attentional capture is influenced by the predictiveness of stimuli (i.e., the extent to which they provide information about upcoming events) or by their absolute associative value (that is, the expected incentive value of the outcome that a stimulus predicts). Results demonstrated a clear influence of associative value on the likelihood that stimuli will capture eye-movements, but the evidence for a distinct influence of predictiveness was less compelling. The results of these experiments can be reconciled within a simple account under which attentional prioritization is a monotonic function of the expected, subjective value of the reward that is signalled by a stimulus.

  18. Extreme value modelling of Ghana stock exchange index.

    Science.gov (United States)

    Nortey, Ezekiel N N; Asare, Kwabena; Mettle, Felix Okoe

    2015-01-01

    Modelling of extreme events has always been of interest in fields such as hydrology and meteorology. However, after the recent global financial crises, appropriate models for modelling of such rare events leading to these crises have become quite essential in the finance and risk management fields. This paper models the extreme values of the Ghana stock exchange all-shares index (2000-2010) by applying the extreme value theory (EVT) to fit a model to the tails of the daily stock returns data. A conditional approach of the EVT was preferred and hence an ARMA-GARCH model was fitted to the data to correct for the effects of autocorrelation and conditional heteroscedastic terms present in the returns series, before the EVT method was applied. The Peak Over Threshold approach of the EVT, which fits a Generalized Pareto Distribution (GPD) model to excesses above a certain selected threshold, was employed. Maximum likelihood estimates of the model parameters were obtained and the model's goodness of fit was assessed graphically using Q-Q, P-P and density plots. The findings indicate that the GPD provides an adequate fit to the data of excesses. The size of the extreme daily Ghanaian stock market movements were then computed using the value at risk and expected shortfall risk measures at some high quantiles, based on the fitted GPD model.

  19. Tailoring the implementation of new biomarkers based on their added predictive value in subgroups of individuals.

    Directory of Open Access Journals (Sweden)

    A van Giessen

    Full Text Available The value of new biomarkers or imaging tests, when added to a prediction model, is currently evaluated using reclassification measures, such as the net reclassification improvement (NRI. However, these measures only provide an estimate of improved reclassification at population level. We present a straightforward approach to characterize subgroups of reclassified individuals in order to tailor implementation of a new prediction model to individuals expected to benefit from it.In a large Dutch population cohort (n = 21,992 we classified individuals to low (< 5% and high (≥ 5% fatal cardiovascular disease risk by the Framingham risk score (FRS and reclassified them based on the systematic coronary risk evaluation (SCORE. Subsequently, we characterized the reclassified individuals and, in case of heterogeneity, applied cluster analysis to identify and characterize subgroups. These characterizations were used to select individuals expected to benefit from implementation of SCORE.Reclassification after applying SCORE in all individuals resulted in an NRI of 5.00% (95% CI [-0.53%; 11.50%] within the events, 0.06% (95% CI [-0.08%; 0.22%] within the nonevents, and a total NRI of 0.051 (95% CI [-0.004; 0.116]. Among the correctly downward reclassified individuals cluster analysis identified three subgroups. Using the characterizations of the typically correctly reclassified individuals, implementing SCORE only in individuals expected to benefit (n = 2,707,12.3% improved the NRI to 5.32% (95% CI [-0.13%; 12.06%] within the events, 0.24% (95% CI [0.10%; 0.36%] within the nonevents, and a total NRI of 0.055 (95% CI [0.001; 0.123]. Overall, the risk levels for individuals reclassified by tailored implementation of SCORE were more accurate.In our empirical example the presented approach successfully characterized subgroups of reclassified individuals that could be used to improve reclassification and reduce implementation burden. In particular when newly

  20. An analytical model for climatic predictions

    International Nuclear Information System (INIS)

    Njau, E.C.

    1990-12-01

    A climatic model based upon analytical expressions is presented. This model is capable of making long-range predictions of heat energy variations on regional or global scales. These variations can then be transformed into corresponding variations of some other key climatic parameters since weather and climatic changes are basically driven by differential heating and cooling around the earth. On the basis of the mathematical expressions upon which the model is based, it is shown that the global heat energy structure (and hence the associated climatic system) are characterized by zonally as well as latitudinally propagating fluctuations at frequencies downward of 0.5 day -1 . We have calculated the propagation speeds for those particular frequencies that are well documented in the literature. The calculated speeds are in excellent agreement with the measured speeds. (author). 13 refs

  1. An Anisotropic Hardening Model for Springback Prediction

    International Nuclear Information System (INIS)

    Zeng, Danielle; Xia, Z. Cedric

    2005-01-01

    As more Advanced High-Strength Steels (AHSS) are heavily used for automotive body structures and closures panels, accurate springback prediction for these components becomes more challenging because of their rapid hardening characteristics and ability to sustain even higher stresses. In this paper, a modified Mroz hardening model is proposed to capture realistic Bauschinger effect at reverse loading, such as when material passes through die radii or drawbead during sheet metal forming process. This model accounts for material anisotropic yield surface and nonlinear isotropic/kinematic hardening behavior. Material tension/compression test data are used to accurately represent Bauschinger effect. The effectiveness of the model is demonstrated by comparison of numerical and experimental springback results for a DP600 straight U-channel test

  2. Predictive Models in Differentiating Vertebral Lesions Using Multiparametric MRI.

    Science.gov (United States)

    Rathore, R; Parihar, A; Dwivedi, D K; Dwivedi, A K; Kohli, N; Garg, R K; Chandra, A

    2017-12-01

    Conventional MR imaging has high sensitivity but limited specificity in differentiating various vertebral lesions. We aimed to assess the ability of multiparametric MR imaging in differentiating spinal vertebral lesions and to develop statistical models for predicting the probability of malignant vertebral lesions. One hundred twenty-six consecutive patients underwent multiparametric MRI (conventional MR imaging, diffusion-weighted MR imaging, and in-phase/opposed-phase imaging) for vertebral lesions. Vertebral lesions were divided into 3 subgroups: infectious, noninfectious benign, and malignant. The cutoffs for apparent diffusion coefficient (expressed as 10 -3 mm 2 /s) and signal intensity ratio values were calculated, and 3 predictive models were established for differentiating these subgroups. Of the lesions of the 126 patients, 62 were infectious, 22 were noninfectious benign, and 42 were malignant. The mean ADC was 1.23 ± 0.16 for infectious, 1.41 ± 0.31 for noninfectious benign, and 1.01 ± 0.22 mm 2 /s for malignant lesions. The mean signal intensity ratio was 0.80 ± 0.13 for infectious, 0.75 ± 0.19 for noninfectious benign, and 0.98 ± 0.11 for the malignant group. The combination of ADC and signal intensity ratio showed strong discriminatory ability to differentiate lesion type. We found an area under the curve of 0.92 for the predictive model in differentiating infectious from malignant lesions and an area under the curve of 0.91 for the predictive model in differentiating noninfectious benign from malignant lesions. On the basis of the mean ADC and signal intensity ratio, we established automated statistical models that would be helpful in differentiating vertebral lesions. Our study shows that multiparametric MRI differentiates various vertebral lesions, and we established prediction models for the same. © 2017 by American Journal of Neuroradiology.

  3. Predictive Modelling of Contagious Deforestation in the Brazilian Amazon

    Science.gov (United States)

    Rosa, Isabel M. D.; Purves, Drew; Souza, Carlos; Ewers, Robert M.

    2013-01-01

    Tropical forests are diminishing in extent due primarily to the rapid expansion of agriculture, but the future magnitude and geographical distribution of future tropical deforestation is uncertain. Here, we introduce a dynamic and spatially-explicit model of deforestation that predicts the potential magnitude and spatial pattern of Amazon deforestation. Our model differs from previous models in three ways: (1) it is probabilistic and quantifies uncertainty around predictions and parameters; (2) the overall deforestation rate emerges “bottom up”, as the sum of local-scale deforestation driven by local processes; and (3) deforestation is contagious, such that local deforestation rate increases through time if adjacent locations are deforested. For the scenarios evaluated–pre- and post-PPCDAM (“Plano de Ação para Proteção e Controle do Desmatamento na Amazônia”)–the parameter estimates confirmed that forests near roads and already deforested areas are significantly more likely to be deforested in the near future and less likely in protected areas. Validation tests showed that our model correctly predicted the magnitude and spatial pattern of deforestation that accumulates over time, but that there is very high uncertainty surrounding the exact sequence in which pixels are deforested. The model predicts that under pre-PPCDAM (assuming no change in parameter values due to, for example, changes in government policy), annual deforestation rates would halve between 2050 compared to 2002, although this partly reflects reliance on a static map of the road network. Consistent with other models, under the pre-PPCDAM scenario, states in the south and east of the Brazilian Amazon have a high predicted probability of losing nearly all forest outside of protected areas by 2050. This pattern is less strong in the post-PPCDAM scenario. Contagious spread along roads and through areas lacking formal protection could allow deforestation to reach the core, which is

  4. Predictive modelling of contagious deforestation in the Brazilian Amazon.

    Science.gov (United States)

    Rosa, Isabel M D; Purves, Drew; Souza, Carlos; Ewers, Robert M

    2013-01-01

    Tropical forests are diminishing in extent due primarily to the rapid expansion of agriculture, but the future magnitude and geographical distribution of future tropical deforestation is uncertain. Here, we introduce a dynamic and spatially-explicit model of deforestation that predicts the potential magnitude and spatial pattern of Amazon deforestation. Our model differs from previous models in three ways: (1) it is probabilistic and quantifies uncertainty around predictions and parameters; (2) the overall deforestation rate emerges "bottom up", as the sum of local-scale deforestation driven by local processes; and (3) deforestation is contagious, such that local deforestation rate increases through time if adjacent locations are deforested. For the scenarios evaluated-pre- and post-PPCDAM ("Plano de Ação para Proteção e Controle do Desmatamento na Amazônia")-the parameter estimates confirmed that forests near roads and already deforested areas are significantly more likely to be deforested in the near future and less likely in protected areas. Validation tests showed that our model correctly predicted the magnitude and spatial pattern of deforestation that accumulates over time, but that there is very high uncertainty surrounding the exact sequence in which pixels are deforested. The model predicts that under pre-PPCDAM (assuming no change in parameter values due to, for example, changes in government policy), annual deforestation rates would halve between 2050 compared to 2002, although this partly reflects reliance on a static map of the road network. Consistent with other models, under the pre-PPCDAM scenario, states in the south and east of the Brazilian Amazon have a high predicted probability of losing nearly all forest outside of protected areas by 2050. This pattern is less strong in the post-PPCDAM scenario. Contagious spread along roads and through areas lacking formal protection could allow deforestation to reach the core, which is currently

  5. Inverse modeling with RZWQM2 to predict water quality

    Science.gov (United States)

    Nolan, Bernard T.; Malone, Robert W.; Ma, Liwang; Green, Christopher T.; Fienen, Michael N.; Jaynes, Dan B.

    2011-01-01

    reflect the total information provided by the observations for a parameter, indicated that most of the RZWQM2 parameters at the California study site (CA) and Iowa study site (IA) could be reliably estimated by regression. Correlations obtained in the CA case indicated that all model parameters could be uniquely estimated by inverse modeling. Although water content at field capacity was highly correlated with bulk density (−0.94), the correlation is less than the threshold for nonuniqueness (0.95, absolute value basis). Additionally, we used truncated singular value decomposition (SVD) at CA to mitigate potential problems with highly correlated and insensitive parameters. Singular value decomposition estimates linear combinations (eigenvectors) of the original process-model parameters. Parameter confidence intervals (CIs) at CA indicated that parameters were reliably estimated with the possible exception of an organic pool transfer coefficient (R45), which had a comparatively wide CI. However, the 95% confidence interval for R45 (0.03–0.35) is mostly within the range of values reported for this parameter. Predictive analysis at CA generated confidence intervals that were compared with independently measured annual water flux (groundwater recharge) and median nitrate concentration in a collocated monitoring well as part of model evaluation. Both the observed recharge (42.3 cm yr−1) and nitrate concentration (24.3 mg L−1) were within their respective 90% confidence intervals, indicating that overall model error was within acceptable limits.

  6. Development of a tool for prediction of ovarian cancer in patients with adnexal masses: Value of plasma fibrinogen.

    Directory of Open Access Journals (Sweden)

    Veronika Seebacher

    Full Text Available To develop a tool for individualized risk estimation of presence of cancer in women with adnexal masses, and to assess the added value of plasma fibrinogen.We performed a retrospective analysis of a prospectively maintained database of 906 patients with adnexal masses who underwent cystectomy or oophorectomy. Uni- and multivariate logistic regression analyses including pre-operative plasma fibrinogen levels and established predictors were performed. A nomogram was generated to predict the probability of ovarian cancer. Internal validation with split-sample analysis was performed. Decision curve analysis (DCA was then used to evaluate the clinical net benefit of the prediction model.Ovarian cancer including borderline tumours was found in 241 (26.6% patients. In multivariate analysis, elevated plasma fibrinogen, elevated CA-125, suspicion for malignancy on ultrasound, and postmenopausal status were associated with ovarian cancer and formed the basis for the nomogram. The overall predictive accuracy of the model, as measured by AUC, was 0.91 (95% CI 0.87-0.94. DCA revealed a net benefit for using this model for predicting ovarian cancer presence compared to a strategy of treat all or treat none.We confirmed the value of plasma fibrinogen as a strong predictor for ovarian cancer in a large cohort of patients with adnexal masses. We developed a highly accurate multivariable model to help in the clinical decision-making regarding the presence of ovarian cancer. This model provided net benefit for a wide range of threshold probabilities. External validation is needed before a recommendation for its use in routine practice can be given.

  7. A matrix model for valuing anesthesia service with the resource-based relative value system.

    Science.gov (United States)

    Sinclair, David R; Lubarsky, David A; Vigoda, Michael M; Birnbach, David J; Harris, Eric A; Behrens, Vicente; Bazan, Richard E; Williams, Steve M; Arheart, Kristopher; Candiotti, Keith A

    2014-01-01

    The purpose of this study was to propose a new crosswalk using the resource-based relative value system (RBRVS) that preserves the time unit component of the anesthesia service and disaggregates anesthesia billing into component parts (preoperative evaluation, intraoperative management, and postoperative evaluation). The study was designed as an observational chart and billing data review of current and proposed payments, in the setting of a preoperative holing area, intraoperative suite, and post anesthesia care unit. In total, 1,195 charts of American Society of Anesthesiology (ASA) physical status 1 through 5 patients were reviewed. No direct patient interventions were undertaken. Spearman correlations between the proposed RBRVS billing matrix payments and the current ASA relative value guide methodology payments were strong (r=0.94-0.96, Pbilling matrix yielded payments that were 3.0%±1.34% less than would have been expected from commercial insurers, using standard rates for commercial ASA relative value units and RBRVS relative value units. Compared with current Medicare reimbursement under the ASA relative value guide, reimbursement would almost double when converting to an RBRVS billing model. The greatest increases in Medicare reimbursement between the current system and proposed billing model occurred as anesthetic management complexity increased. The new crosswalk correlates with existing evaluation and management and intensive care medicine codes in an essentially revenue neutral manner when applied to the market-based rates of commercial insurers. The new system more highly values delivery of care to more complex patients undergoing more complex surgery and better represents the true value of anesthetic case management.

  8. A matrix model for valuing anesthesia service with the resource-based relative value system

    Science.gov (United States)

    Sinclair, David R; Lubarsky, David A; Vigoda, Michael M; Birnbach, David J; Harris, Eric A; Behrens, Vicente; Bazan, Richard E; Williams, Steve M; Arheart, Kristopher; Candiotti, Keith A

    2014-01-01

    Background The purpose of this study was to propose a new crosswalk using the resource-based relative value system (RBRVS) that preserves the time unit component of the anesthesia service and disaggregates anesthesia billing into component parts (preoperative evaluation, intraoperative management, and postoperative evaluation). The study was designed as an observational chart and billing data review of current and proposed payments, in the setting of a preoperative holing area, intraoperative suite, and post anesthesia care unit. In total, 1,195 charts of American Society of Anesthesiology (ASA) physical status 1 through 5 patients were reviewed. No direct patient interventions were undertaken. Results Spearman correlations between the proposed RBRVS billing matrix payments and the current ASA relative value guide methodology payments were strong (r=0.94–0.96, P<0.001 for training, test, and overall). The proposed RBRVS-based billing matrix yielded payments that were 3.0%±1.34% less than would have been expected from commercial insurers, using standard rates for commercial ASA relative value units and RBRVS relative value units. Compared with current Medicare reimbursement under the ASA relative value guide, reimbursement would almost double when converting to an RBRVS billing model. The greatest increases in Medicare reimbursement between the current system and proposed billing model occurred as anesthetic management complexity increased. Conclusion The new crosswalk correlates with existing evaluation and management and intensive care medicine codes in an essentially revenue neutral manner when applied to the market-based rates of commercial insurers. The new system more highly values delivery of care to more complex patients undergoing more complex surgery and better represents the true value of anesthetic case management. PMID:25336964

  9. Predictive Value of Upper Limb Muscles and Grasp Patterns on Functional Outcome in Cervical Spinal Cord Injury.

    Science.gov (United States)

    Velstra, Inge-Marie; Bolliger, Marc; Krebs, Jörg; Rietman, Johan S; Curt, Armin

    2016-05-01

    To determine which single or combined upper limb muscles as defined by the International Standards for the Neurological Classification of Spinal Cord Injury (ISNCSCI); upper extremity motor score (UEMS) and the Graded Redefined Assessment of Strength, Sensibility, and Prehension (GRASSP), best predict upper limb function and independence in activities of daily living (ADLs) and to assess the predictive value of qualitative grasp movements (QlG) on upper limb function in individuals with acute tetraplegia. As part of a Europe-wide, prospective, longitudinal, multicenter study ISNCSCI, GRASSP, and Spinal Cord Independence Measure (SCIM III) scores were recorded at 1 and 6 months after SCI. For prediction of upper limb function and ADLs, a logistic regression model and unbiased recursive partitioning conditional inference tree (URP-CTREE) were used. Results: Logistic regression and URP-CTREE revealed that a combination of ISNCSCI and GRASSP muscles (to a maximum of 4) demonstrated the best prediction (specificity and sensitivity ranged from 81.8% to 96.0%) of upper limb function and identified homogenous outcome cohorts at 6 months. The URP-CTREE model with the QlG predictors for upper limb function showed similar results. Prediction of upper limb function can be achieved through a combination of defined, specific upper limb muscles assessed in the ISNCSCI and GRASSP. A combination of a limited number of proximal and distal muscles along with an assessment of grasping movements can be applied for clinical decision making for rehabilitation interventions and clinical trials. © The Author(s) 2015.

  10. Ethnic differences in antepartum glucose values that predict postpartum dysglycemia and neonatal macrosomia.

    Science.gov (United States)

    Ajala, Olubukola; Chik, Constance

    2018-03-31

    Gestational diabetes (GDM) occurs more often in women from certain ethnic groups and is also associated with fetal macrosomia. In this study, we investigated the ability of a gestational diabetes screening test (GDS), the 2 h 75 g-Oral Glucose Tolerance Test (OGTT), and glycated hemoglobin (HbA1c) in predicting postpartum dysglycemia and fetal macrosomia in women of Caucasian, Filipino, Chinese and South-Asian descent. 848 women diagnosed with carbohydrate intolerance in pregnancy who completed a 2 h 75 g- OGTT within 6 months postpartum, were included in the study. Receiver Operating Characteristic curve analysis was used to test the ability of antepartum GDS, HbA1c and OGTT in predicting postpartum hyperglycemia, type 2 diabetes (T2D) and neonatal macrosomia (birth weight >4000 g). 20.2% had postpartum hyperglycemia while 3.8% had T2D. Those with postpartum dysglycemia were more likely to be non-Caucasian (South-Asian > Filipino > Chinese), have higher antepartum glucose values, require insulin during pregnancy and have cesarean births. Of HbA1c and the antepartum glucose values, a fasting glucose of ≥5.25 mmol/L was predictive of fetal macrosomia in Caucasians. 1 h glucose of ≥11.05 mmol/L was predictive of postpartum hyperglycemia, while 2 h glucose of ≥9.75 mmol/L was predictive of T2D; ethnicity influenced the predictive ability of these tests. Ethnicity influences the ability of antepartum glucose and HbA1c to predict the risk of macrosomia and postpartum dysglycemia. This information will help detect those most at risk of T2D. Copyright © 2018 Elsevier B.V. All rights reserved.

  11. The Unfolding of Value Sources During Online Business Model Transformation

    Directory of Open Access Journals (Sweden)

    Nadja Hoßbach

    2016-12-01

    Full Text Available Purpose: In the magazine publishing industry, viable online business models are still rare to absent. To prepare for the ‘digital future’ and safeguard their long-term survival, many publishers are currently in the process of transforming their online business model. Against this backdrop, this study aims to develop a deeper understanding of (1 how the different building blocks of an online business model are transformed over time and (2 how sources of value creation unfold during this transformation process. Methodology: To answer our research question, we conducted a longitudinal case study with a leading German business magazine publisher (called BIZ. Data was triangulated from multiple sources including interviews, internal documents, and direct observations. Findings: Based on our case study, we nd that BIZ used the transformation process to differentiate its online business model from its traditional print business model along several dimensions, and that BIZ’s online business model changed from an efficiency- to a complementarity- to a novelty-based model during this process. Research implications: Our findings suggest that different business model transformation phases relate to different value sources, questioning the appropriateness of value source-based approaches for classifying business models. Practical implications: The results of our case study highlight the need for online-offline business model differentiation and point to the important distinction between service and product differentiation. Originality: Our study contributes to the business model literature by applying a dynamic and holistic perspective on the link between online business model changes and unfolding value sources.

  12. Comparison of joint modeling and landmarking for dynamic prediction under an illness-death model.

    Science.gov (United States)

    Suresh, Krithika; Taylor, Jeremy M G; Spratt, Daniel E; Daignault, Stephanie; Tsodikov, Alexander

    2017-11-01

    Dynamic prediction incorporates time-dependent marker information accrued during follow-up to improve personalized survival prediction probabilities. At any follow-up, or "landmark", time, the residual time distribution for an individual, conditional on their updated marker values, can be used to produce a dynamic prediction. To satisfy a consistency condition that links dynamic predictions at different time points, the residual time distribution must follow from a prediction function that models the joint distribution of the marker process and time to failure, such as a joint model. To circumvent the assumptions and computational burden associated with a joint model, approximate methods for dynamic prediction have been proposed. One such method is landmarking, which fits a Cox model at a sequence of landmark times, and thus is not a comprehensive probability model of the marker process and the event time. Considering an illness-death model, we derive the residual time distribution and demonstrate that the structure of the Cox model baseline hazard and covariate effects under the landmarking approach do not have simple form. We suggest some extensions of the landmark Cox model that should provide a better approximation. We compare the performance of the landmark models with joint models using simulation studies and cognitive aging data from the PAQUID study. We examine the predicted probabilities produced under both methods using data from a prostate cancer study, where metastatic clinical failure is a time-dependent covariate for predicting death following radiation therapy. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  13. Web tools for predictive toxicology model building.

    Science.gov (United States)

    Jeliazkova, Nina

    2012-07-01

    The development and use of web tools in chemistry has accumulated more than 15 years of history already. Powered by the advances in the Internet technologies, the current generation of web systems are starting to expand into areas, traditional for desktop applications. The web platforms integrate data storage, cheminformatics and data analysis tools. The ease of use and the collaborative potential of the web is compelling, despite the challenges. The topic of this review is a set of recently published web tools that facilitate predictive toxicology model building. The focus is on software platforms, offering web access to chemical structure-based methods, although some of the frameworks could also provide bioinformatics or hybrid data analysis functionalities. A number of historical and current developments are cited. In order to provide comparable assessment, the following characteristics are considered: support for workflows, descriptor calculations, visualization, modeling algorithms, data management and data sharing capabilities, availability of GUI or programmatic access and implementation details. The success of the Web is largely due to its highly decentralized, yet sufficiently interoperable model for information access. The expected future convergence between cheminformatics and bioinformatics databases provides new challenges toward management and analysis of large data sets. The web tools in predictive toxicology will likely continue to evolve toward the right mix of flexibility, performance, scalability, interoperability, sets of unique features offered, friendly user interfaces, programmatic access for advanced users, platform independence, results reproducibility, curation and crowdsourcing utilities, collaborative sharing and secure access.

  14. [Endometrial cancer: Predictive models and clinical impact].

    Science.gov (United States)

    Bendifallah, Sofiane; Ballester, Marcos; Daraï, Emile

    2017-12-01

    In France, in 2015, endometrial cancer (CE) is the first gynecological cancer in terms of incidence and the fourth cause of cancer of the woman. About 8151 new cases and nearly 2179 deaths have been reported. Treatments (surgery, external radiotherapy, brachytherapy and chemotherapy) are currently delivered on the basis of an estimation of the recurrence risk, an estimation of lymph node metastasis or an estimate of survival probability. This risk is determined on the basis of prognostic factors (clinical, histological, imaging, biological) taken alone or grouped together in the form of classification systems, which are currently insufficient to account for the evolutionary and prognostic heterogeneity of endometrial cancer. For endometrial cancer, the concept of mathematical modeling and its application to prediction have developed in recent years. These biomathematical tools have opened a new era of care oriented towards the promotion of targeted therapies and personalized treatments. Many predictive models have been published to estimate the risk of recurrence and lymph node metastasis, but a tiny fraction of them is sufficiently relevant and of clinical utility. The optimization tracks are multiple and varied, suggesting the possibility in the near future of a place for these mathematical models. The development of high-throughput genomics is likely to offer a more detailed molecular characterization of the disease and its heterogeneity. Copyright © 2017 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.

  15. Finite element modeling to analyze TEER values across silicon nanomembranes.

    Science.gov (United States)

    Khire, Tejas S; Nehilla, Barrett J; Getpreecharsawas, Jirachai; Gracheva, Maria E; Waugh, Richard E; McGrath, James L

    2018-01-05

    Silicon nanomembranes are ultrathin, highly permeable, optically transparent and biocompatible substrates for the construction of barrier tissue models. Trans-epithelial/endothelial electrical resistance (TEER) is often used as a non-invasive, sensitive and quantitative technique to assess barrier function. The current study characterizes the electrical behavior of devices featuring silicon nanomembranes to facilitate their application in TEER studies. In conventional practice with commercial systems, raw resistance values are multiplied by the area of the membrane supporting cell growth to normalize TEER measurements. We demonstrate that under most circumstances, this multiplication does not 'normalize' TEER values as is assumed, and that the assumption is worse if applied to nanomembrane chips with a limited active area. To compare the TEER values from nanomembrane devices to those obtained from conventional polymer track-etched (TE) membranes, we develop finite element models (FEM) of the electrical behavior of the two membrane systems. Using FEM and parallel cell-culture experiments on both types of membranes, we successfully model the evolution of resistance values during the growth of endothelial monolayers. Further, by exploring the relationship between the models we develop a 'correction' function, which when applied to nanomembrane TEER, maps to experiments on conventional TE membranes. In summary, our work advances the the utility of silicon nanomembranes as substrates for barrier tissue models by developing an interpretation of TEER values compatible with conventional systems.

  16. Classification of customer lifetime value models using Markov chain

    Science.gov (United States)

    Permana, Dony; Pasaribu, Udjianna S.; Indratno, Sapto W.; Suprayogi

    2017-10-01

    A firm’s potential reward in future time from a customer can be determined by customer lifetime value (CLV). There are some mathematic methods to calculate it. One method is using Markov chain stochastic model. Here, a customer is assumed through some states. Transition inter the states follow Markovian properties. If we are given some states for a customer and the relationships inter states, then we can make some Markov models to describe the properties of the customer. As Markov models, CLV is defined as a vector contains CLV for a customer in the first state. In this paper we make a classification of Markov Models to calculate CLV. Start from two states of customer model, we make develop in many states models. The development a model is based on weaknesses in previous model. Some last models can be expected to describe how real characters of customers in a firm.

  17. Sensation and perception of sucrose and fat stimuli predict the reinforcing value of food.

    Science.gov (United States)

    Panek-Scarborough, Leah M; Dewey, Amber M; Temple, Jennifer L

    2012-03-20

    Chronic overeating can lead to weight gain and obesity. Sensory system function may play a role in the types of foods people select and the amount of food people eat. Several studies have shown that the orosensory components of eating play a strong role in driving food intake and food selection. In addition, previous work has shown that motivation to get food, or the reinforcing value of food, is a predictor of energy intake. The purpose of this study was to test the hypothesis that higher detection thresholds and lower suprathreshold intensity ratings of sweet and fat stimuli are associated with greater reinforcing value of food. In addition, we sought to determine if the sensory ratings of the stimuli would differ depending on whether they were expectorated or swallowed. The reinforcing value of food was measured by having participants perform operant responses for food on progressive ratio schedules of reinforcement. Taste detection thresholds and suprathresholds for solutions containing varied concentrations of sucrose and fat were also measured in two different Experiments. In Experiment 1, we found that sucrose, but not fat, detection predicted the reinforcing value of food with the reinforcing value of food increasing as sucrose detection threshold increased (indicating poorer detection). In Experiment 2, we found that lower suprathreshold ratings of expectorated fat and sucrose predicted greater reinforcing value of food. In addition, higher detection thresholds for fat stimuli (indicating poorer detection) were associated with greater reinforcing value of food. When taken together, these studies suggest that there is a relationship between taste detection and perception and reinforcing value of food and that these relationships vary based on whether the stimulus is swallowed or expectorated. Copyright © 2012 Elsevier Inc. All rights reserved.

  18. The role of low CRP values in the prediction of the development of acute diverticulitis.

    Science.gov (United States)

    Mäkelä, Jyrki T; Klintrup, Kai; Rautio, Tero

    2016-01-01

    Computed tomography (CT) is the most appropriate imaging modality for the assessment of acute diverticulitis at the emergency unit. The aim of this study was to determine the clinical outcome of the patients presented first time with symptoms of acute diverticulitis and low CRP values. Two-hundred patients, who presented with the symptoms of acute diverticulitis and had CRP values under 150 mg/L, underwent abdominal CT examination on admission to Oulu University Hospital. The clinical parameters and radiological findings were compared in relation to clinical outcome both by means of univariate and multivariate analyses. Seventy-one (35.5 %) of the 200 patients presented on admission with complicated diverticulitis. CRP values between 100 and 150 mg/L predicted complicated disease, but the mean values of CRP between uncomplicated disease, 89 mg/L ± 39, and complicated disease, 101 mg/L ± 39, did not differ significantly. Free intra-abdominal fluid in CT was the only independent risk factor of the need for interventional therapy and treatment in the intensive care unit. Longevity of the patients and free fluid in CT predicted significantly prolonged hospitalization. Mortality was 1 % and older patients were significantly affected. The recurrence rate of acute diverticulitis was 24 % (43/177) in the whole group and 18 % (23/129) after uncomplicated diverticulitis. Low CRP values do not reliably predict uncomplicated disease in patients presented first time at the emergency unit with acute diverticulitis. We recommend that the need for abdominal CT is carefully evaluated according to the patient's clinical status, always even when the CRP value is under 150 mg/L.

  19. Comparing the values of intact parathormone and 1- 84 PTH to predict hyperparathyroidism in hemodialysis patients.

    Science.gov (United States)

    Jenabi, Aria; Jabbari, Mosadegh; Ziaie, Hossein

    2017-07-01

    Secondary hyperparathyroidism (SHPT) is a common complication of chronic kidney disease (CKD) leading high mortality and even long-term morbidity. SHPT is manifested by elevation of parathyroid hormone (PTH) and accurate determining the level of serum PTH is very essential for early diagnosis of SHPT secondary to CKD. It is very important to match the values obtained for intact parathormone (iPTH) and 1- 84 PTH with the minimized measurement bias. The present study aimed to first determine the agreement value between the iPTH and 1- 84 PTH measures in patients with hyperparathyroidism secondary to endstage renal disease under chronic hemodialysis. Then, we attempted to determine the best cutoff values for these two measurements for detecting SHPT in such patients. This cross-sectional study was conducted on hemodialysis patients. The value of study biomarkers including iPTH and 1- 84 PTH was assessed. A strong positive association was revealed between the two indicators of iPTH and 1-84 PTH (r = 0.800, P PTH was only associated with serum calcium level negatively (r = -0.267, P = 0.027) and alkaline phosphatase positively (r = 0.359, P = 0.003). Considering iPTH as the reference and according to the area under the ROC curve (AUC), 1-84 PTH had high value to predict hyperparathyroidism (AUC = 0.926, P PTH to discriminate hyperparathyroidism from normal condition was 60 yielding a sensitivity of 92.3% and a specificity of 79.1%. Among other baseline laboratory parameters, only alkaline phosphatase had an acceptable value for diagnosing hyperparathyroidism (AUC = 0.731, P = 0.001). The measurement of both iPTH and 1-84 PTH is valuable for predicting hyperparathyroidism secondary to CKD, but according to lower cost and comparableeffectiveness of iPTH measurement, this assay may be comparable to 1-84 PTH to predict this consequence.

  20. Mean Value Modelling of a Turbocharged SI Engine

    DEFF Research Database (Denmark)

    Müller, Martin; Hendricks, Elbert; Sorenson, Spencer C.

    1998-01-01

    but not the cycle-by-cycle behavior. In principle such models are also physically based,are very compact in a mathematical sense but nevertheless can have reasonable prediction accuracy. Presently no MVEMs have been constructed for intercooled turbocharged SI engines because their complexity confounds the simple...... reasonable accuracy for realistic operating scenarios....

  1. Predictive Capability Maturity Model for computational modeling and simulation.

    Energy Technology Data Exchange (ETDEWEB)

    Oberkampf, William Louis; Trucano, Timothy Guy; Pilch, Martin M.

    2007-10-01

    The Predictive Capability Maturity Model (PCMM) is a new model that can be used to assess the level of maturity of computational modeling and simulation (M&S) efforts. The development of the model is based on both the authors experience and their analysis of similar investigations in the past. The perspective taken in this report is one of judging the usefulness of a predictive capability that relies on the numerical solution to partial differential equations to better inform and improve decision making. The review of past investigations, such as the Software Engineering Institute's Capability Maturity Model Integration and the National Aeronautics and Space Administration and Department of Defense Technology Readiness Levels, indicates that a more restricted, more interpretable method is needed to assess the maturity of an M&S effort. The PCMM addresses six contributing elements to M&S: (1) representation and geometric fidelity, (2) physics and material model fidelity, (3) code verification, (4) solution verification, (5) model validation, and (6) uncertainty quantification and sensitivity analysis. For each of these elements, attributes are identified that characterize four increasing levels of maturity. Importantly, the PCMM is a structured method for assessing the maturity of an M&S effort that is directed toward an engineering application of interest. The PCMM does not assess whether the M&S effort, the accuracy of the predictions, or the performance of the engineering system satisfies or does not satisfy specified application requirements.

  2. Predictions of models for environmental radiological assessment

    International Nuclear Information System (INIS)

    Peres, Sueli da Silva; Lauria, Dejanira da Costa; Mahler, Claudio Fernando

    2011-01-01

    In the field of environmental impact assessment, models are used for estimating source term, environmental dispersion and transfer of radionuclides, exposure pathway, radiation dose and the risk for human beings Although it is recognized that the specific information of local data are important to improve the quality of the dose assessment results, in fact obtaining it can be very difficult and expensive. Sources of uncertainties are numerous, among which we can cite: the subjectivity of modelers, exposure scenarios and pathways, used codes and general parameters. The various models available utilize different mathematical approaches with different complexities that can result in different predictions. Thus, for the same inputs different models can produce very different outputs. This paper presents briefly the main advances in the field of environmental radiological assessment that aim to improve the reliability of the models used in the assessment of environmental radiological impact. The intercomparison exercise of model supplied incompatible results for 137 Cs and 60 Co, enhancing the need for developing reference methodologies for environmental radiological assessment that allow to confront dose estimations in a common comparison base. The results of the intercomparison exercise are present briefly. (author)

  3. Ion current prediction model considering columnar recombination in alpha radioactivity measurement using ionized air transportation

    International Nuclear Information System (INIS)

    Naito, Susumu; Hirata, Yosuke; Izumi, Mikio; Sano, Akira; Miyamoto, Yasuaki; Aoyama, Yoshio; Yamaguchi, Hiromi

    2007-01-01

    We present a reinforced ion current prediction model in alpha radioactivity measurement using ionized air transportation. Although our previous model explained the qualitative trend of the measured ion current values, the absolute values of the theoretical curves were about two times as large as the measured values. In order to accurately predict the measured values, we reinforced our model by considering columnar recombination and turbulent diffusion, which affects columnar recombination. Our new model explained the considerable ion loss in the early stage of ion diffusion and narrowed the gap between the theoretical and measured values. The model also predicted suppression of ion loss due to columnar recombination by spraying a high-speed air flow near a contaminated surface. This suppression was experimentally investigated and confirmed. In conclusion, we quantitatively clarified the theoretical relation between alpha radioactivity and ion current in laminar flow and turbulent pipe flow. (author)

  4. The Clinical Added Value of Imaging: A Perspective From Outcome Prediction.

    Science.gov (United States)

    Jollans, Lee; Whelan, Robert

    2016-09-01

    Objective measures of psychiatric health would be of benefit in clinical practice. Despite considerable research in the area of psychiatric neuroimaging outcome prediction, translating putative neuroimaging markers (neuromarkers) of a disorder into clinical practice has proven challenging. We reviewed studies that used neuroimaging measures to predict treatment response and disease outcomes in major depressive disorder, substance use, autism spectrum disorder, psychosis, and dementia. The majority of studies sought to predict psychiatric outcomes rather than develop a specific biological index of future disease trajectory. Studies varied widely with respect to sample size and quantification of out-of-sample prediction model performance. Many studies were able to predict psychiatric outcomes with moderate accuracy, with neuroimaging data often augmenting the prediction compared to clinical or psychometric data alone. We make recommendations for future research with respect to methods that can increase the generalizability and reproducibility of predictions. Large sample sizes in conjunction with machine learning methods, such as feature selection, cross-validation, and random label permutation, provide significant improvement to and quantification of generalizability. Further refinement of neuroimaging protocols and analysis methods will likely facilitate the clinical applicability of predictive imaging markers in psychiatry. Such clinically relevant neuromarkers need not necessarily be grounded in the pathophysiology of the disease, but identifying these neuromarkers may suggest targets for future research into disease mechanisms. The ability of imaging prediction models to augment clinical judgments will ultimately depend on the personal and economic costs and benefits to the patient. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  5. Statistical models for expert judgement and wear prediction

    International Nuclear Information System (INIS)

    Pulkkinen, U.

    1994-01-01

    This thesis studies the statistical analysis of expert judgements and prediction of wear. The point of view adopted is the one of information theory and Bayesian statistics. A general Bayesian framework for analyzing both the expert judgements and wear prediction is presented. Information theoretic interpretations are given for some averaging techniques used in the determination of consensus distributions. Further, information theoretic models are compared with a Bayesian model. The general Bayesian framework is then applied in analyzing expert judgements based on ordinal comparisons. In this context, the value of information lost in the ordinal comparison process is analyzed by applying decision theoretic concepts. As a generalization of the Bayesian framework, stochastic filtering models for wear prediction are formulated. These models utilize the information from condition monitoring measurements in updating the residual life distribution of mechanical components. Finally, the application of stochastic control models in optimizing operational strategies for inspected components are studied. Monte-Carlo simulation methods, such as the Gibbs sampler and the stochastic quasi-gradient method, are applied in the determination of posterior distributions and in the solution of stochastic optimization problems. (orig.) (57 refs., 7 figs., 1 tab.)

  6. Endorsement of Social and Personal Values Predicts the Desirability of Men and Women as Long-Term Partners.

    Science.gov (United States)

    Lopes, Guilherme S; Barbaro, Nicole; Sela, Yael; Jeffery, Austin J; Pham, Michael N; Shackelford, Todd K; Zeigler-Hill, Virgil

    2017-01-01

    A prospective romantic partner's desirability as a long-term partner may be affected by the values that he or she endorses. However, few studies have examined the effects of "values" on a person's desirability as a long-term partner. We hypothesized that individuals who endorse social values (vs. personal values) will be perceived as more desirable long-term partners (Hypothesis 1) and that the endorsement of social values will be especially desirable in a male (vs. female) long-term partner (Hypothesis 2). The current study employed a 2 (sex of prospective partner: male vs. female) × 2 (values of prospective partner: personal vs. social) × 2 (physical attractiveness of prospective partner: unattractive vs. highly attractive) mixed-model design. Participants were 339 undergraduates (174 men, 165 women), with ages varying between 18 and 33 years ( M = 19.9, SD = 3.6), and mostly in a romantic relationship (53.7%). Participants reported interest in a long-term relationship with prospective partners depicted in four scenarios (within subjects), each varying along the dimensions of values (personal vs. social) and physical attractiveness (unattractive vs. highly attractive). Individuals endorsing personal values (vs. social values) and men (vs. women) endorsing personal values were rated as less desirable as long-term partners. The current research adds to the partner preferences literature by demonstrating that an individual's ascribed values influence others' perceptions of desirability as a long-term partner and that these effects are consistently sex differentiated, as predicted by an evolutionary perspective on romantic partner preferences.

  7. Predictive Value of Panoramic Radiography for Injury of Inferior Alveolar Nerve After Mandibular Third Molar Surgery.

    Science.gov (United States)

    Su, Naichuan; van Wijk, Arjen; Berkhout, Erwin; Sanderink, Gerard; De Lange, Jan; Wang, Hang; van der Heijden, Geert J M G

    2017-04-01

    The purpose of the present systematic review was to assess the added value of panoramic radiography in predicting postoperative injury of the inferior alveolar nerve (IAN) in the decision-making before mandibular third molar (MM3) surgery. MEDLINE and EMBASE were searched electronically to identify the diagnostic accuracy of studies that had assessed the predictive value of 7 panoramic radiographic signs, including root-related signs (darkening of the root, deflection of the root, narrowing of the root, and dark and bifid apex of the root) and canal-related signs (interruption of the white line of the canal, diversion of the canal, and narrowing of the canal) for IAN injury after MM3 surgery. A total of 8 studies qualified for the meta-analysis. The pooled sensitivity and specificity of the 7 signs ranged from 0.06 to 0.49 and 0.81 to 0.97, respectively. The area under the summary area under the receiver operating characteristic curve ranged from 0.42 to 0.89. The pooled positive predictive value (PPV) and negative predictive value (NPV) ranged from 7.5 to 26.6% and 95.9 to 97.7%, respectively. The added value of a positive sign for ruling in an IAN injury (PPV minus the prior probability) ranged from 3.4 to 22.2%. The added value of a negative sign for ruling out an IAN injury (NPV minus [1 minus the prior probability]) ranged from 0.1 to 2.2%. For all 7 signs, the added value of panoramic radiography is too low to consider it appropriate for ruling out postoperative IAN in the decision-making before MM3 surgery. The added value of panoramic radiography for determining the presence of diversion of the canal, interruption of the white line of the canal, and darkening of the root can be considered sufficient for ruling in the risk of postoperative IAN injury in the decision-making before MM3 surgery. Copyright © 2016 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

  8. Systematic prediction error correction: a novel strategy for maintaining the predictive abilities of multivariate calibration models.

    Science.gov (United States)

    Chen, Zeng-Ping; Li, Li-Mei; Yu, Ru-Qin; Littlejohn, David; Nordon, Alison; Morris, Julian; Dann, Alison S; Jeffkins, Paul A; Richardson, Mark D; Stimpson, Sarah L

    2011-01-07

    The development of reliable multivariate calibration models for spectroscopic instruments in on-line/in-line monitoring of chemical and bio-chemical processes is generally difficult, time-consuming and costly. Therefore, it is preferable if calibration models can be used for an extended period, without the need to replace them. However, in many process applications, changes in the instrumental response (e.g. owing to a change of spectrometer) or variations in the measurement conditions (e.g. a change in temperature) can cause a multivariate calibration model to become invalid. In this contribution, a new method, systematic prediction error correction (SPEC), has been developed to maintain the predictive abilities of multivariate calibration models when e.g. the spectrometer or measurement conditions are altered. The performance of the method has been tested on two NIR data sets (one with changes in instrumental responses, the other with variations in experimental conditions) and the outcomes compared with those of some popular methods, i.e. global PLS, univariate slope and bias correction (SBC) and piecewise direct standardization (PDS). The results show that SPEC achieves satisfactory analyte predictions with significantly lower RMSEP values than global PLS and SBC for both data sets, even when only a few standardization samples are used. Furthermore, SPEC is simple to implement and requires less information than PDS, which offers advantages for applications with limited data.

  9. Enhanced pid vs model predictive control applied to bldc motor

    Science.gov (United States)

    Gaya, M. S.; Muhammad, Auwal; Aliyu Abdulkadir, Rabiu; Salim, S. N. S.; Madugu, I. S.; Tijjani, Aminu; Aminu Yusuf, Lukman; Dauda Umar, Ibrahim; Khairi, M. T. M.

    2018-01-01

    BrushLess Direct Current (BLDC) motor is a multivariable and highly complex nonlinear system. Variation of internal parameter values with environment or reference signal increases the difficulty in controlling the BLDC effectively. Advanced control strategies (like model predictive control) often have to be integrated to satisfy the control desires. Enhancing or proper tuning of a conventional algorithm results in achieving the desired performance. This paper presents a performance comparison of Enhanced PID and Model Predictive Control (MPC) applied to brushless direct current motor. The simulation results demonstrated that the PSO-PID is slightly better than the PID and MPC in tracking the trajectory of the reference signal. The proposed scheme could be useful algorithms for the system.

  10. Predictive value of fever and palmar pallor for P. falciparum parasitaemia in children from an endemic area.

    Directory of Open Access Journals (Sweden)

    Christof David Vinnemeier

    Full Text Available INTRODUCTION: Although the incidence of Plasmodium falciparum malaria in some parts of sub-Saharan Africa is reported to decline and other conditions, causing similar symptoms as clinical malaria are gaining in relevance, presumptive anti-malarial treatment is still common. This study traced for age-dependent signs and symptoms predictive for P. falciparum parasitaemia. METHODS: In total, 5447 visits of 3641 patients between 2-60 months of age who attended an outpatient department (OPD of a rural hospital in the Ashanti Region, Ghana, were analysed. All Children were examined by a paediatrician and a full blood count and thick smear were done. A Classification and Regression Tree (CART model was used to generate a clinical decision tree to predict malarial parasitaemia a7nd predictive values of all symptoms were calculated. RESULTS: Malarial parasitaemia was detected in children between 2-12 months and between 12-60 months of age with a prevalence of 13.8% and 30.6%, respectively. The CART-model revealed age-dependent differences in the ability of the variables to predict parasitaemia. While palmar pallor was the most important symptom in children between 2-12 months, a report of fever and an elevated body temperature of ≥37.5°C gained in relevance in children between 12-60 months. The variable palmar pallor was significantly (p<0.001 associated with lower haemoglobin levels in children of all ages. Compared to the Integrated Management of Childhood Illness (IMCI algorithm the CART-model had much lower sensitivities, but higher specificities and positive predictive values for a malarial parasitaemia. CONCLUSIONS: Use of age-derived algorithms increases the specificity of the prediction for P. falciparum parasitaemia. The predictive value of palmar pallor should be underlined in health worker training. Due to a lack of sensitivity neither the best algorithm nor palmar pallor as a single sign are eligible for decision-making and cannot replace

  11. Analysis and monitoring of energy security and prediction of indicator values using conventional non-linear mathematical programming

    Directory of Open Access Journals (Sweden)

    Elena Vital'evna Bykova

    2011-09-01

    Full Text Available This paper describes the concept of energy security and a system of indicators for its monitoring. The indicator system includes more than 40 parameters that reflect the structure and state of fuel and energy complex sectors (fuel, electricity and heat & power, as well as takes into account economic, environmental and social aspects. A brief description of the structure of the computer system to monitor and analyze energy security is given. The complex contains informational, analytical and calculation modules, provides applications for forecasting and modeling energy scenarios, modeling threats and determining levels of energy security. Its application to predict the values of the indicators and methods developed for it are described. This paper presents a method developed by conventional nonlinear mathematical programming needed to address several problems of energy and, in particular, the prediction problem of the security. An example of its use and implementation of this method in the application, "Prognosis", is also given.

  12. An Early Model for Value and Sustainability in Health Information Exchanges: Qualitative Study.

    Science.gov (United States)

    Feldman, Sue S

    2018-04-30

    The primary value relative to health information exchange has been seen in terms of cost savings relative to laboratory and radiology testing, emergency department expenditures, and admissions. However, models are needed to statistically quantify value and sustainability and better understand the dependent and mediating factors that contribute to value and sustainability. The purpose of this study was to provide a basis for early model development for health information exchange value and sustainability. A qualitative study was conducted with 21 interviews of eHealth Exchange participants across 10 organizations. Using a grounded theory approach and 3.0 as a relative frequency threshold, 5 main categories and 16 subcategories emerged. This study identifies 3 core current perceived value factors and 5 potential perceived value factors-how interviewees predict health information exchanges may evolve as there are more participants. These value factors were used as the foundation for early model development for sustainability of health information exchange. Using the value factors from the interviews, the study provides the basis for early model development for health information exchange value and sustainability. This basis includes factors from the research: fostering consumer engagement; establishing a provider directory; quantifying use, cost, and clinical outcomes; ensuring data integrity through patient matching; and increasing awareness, usefulness, interoperability, and sustainability of eHealth Exchange. ©Sue S Feldman. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 30.04.2018.

  13. Predictive value of tonometry with Tono-pen XL in primary care.

    Science.gov (United States)

    Beneyto, Pedro; Barajas, Miguel A; Garcia-de-Blas, Francisa; Del Cura, Isabel; Sanz, Teresa; Vello, Rocio; Salvador, Carmela

    2007-08-01

    This is a descriptive study designed to assess the predictive value of intraocular pressure (IOP) measurement in GPs' offices in an urban healthcare site using Tono-pen XL. A total of 2044 patients, aged > or =40 years, were enrolled by consecutive sampling from patients visiting the GP. Those participants who had IOP > or =21 mmHg were referred to the ophthalmologist. Of the 226 then tested, ocular hypertension was confirmed in 100 participants (4.89%, 95% CI [confidence interval] = 3.93 to 5.85%). Predictive value was 44.2%. These results suggest the validity of using Tono-pen XL in the GP's office to detect ocular hypertension.

  14. Sensitivity, specificity and predictive value of blood cultures from cattle clinically suspected of bacterial endocarditis

    DEFF Research Database (Denmark)

    Houe, Hans; Eriksen, L.; Jungersen, Gregers

    1993-01-01

    This study investigated the number of blood culture-positive cattle among 215 animals clinically suspected of having bacterial endocarditis. For animals that were necropsied, the sensitivity, specificity and predictive value of the diagnosis of endocarditis were calculated on the basis...... of the isolation of the causative bacteria from blood. Furthermore, it was investigated whether the glutaraldehyde coagulation time, total leucocyte count, per cent neutrophil granulocytes, pulse rate and duration of disease could help to discriminate endocarditis from other diseases. Among 138 animals necropsied...... the sensitivity, specificity and predictive value of blood cultivation were 70.7 per cent, 93.8 per cent and 89.1 per cent, respectively. None of the other measurements could be used to discriminate between endocarditis and non-endocarditis cases....

  15. Prevalence and predictive value of islet cell antibodies and insulin autoantibodies in women with gestational diabetes

    DEFF Research Database (Denmark)

    Damm, P; Kühl, C; Buschard, K

    1994-01-01

    The objective of the present study was to investigate the predictive value of islet cell antibodies (ICA) and insulin autoantibodies (IAA) for development of diabetes in women with previous gestational diabetes (GDM). Two hundred and forty-one previous diet-treated GDM patients and 57 women without...... previous GDM were examined 2-11 years after the index pregnancy. In subgroups, plasma from the diagnostic OGTT during index pregnancy was analysed for ICA and IAA. Among the previous GDM patients, 3.7% had developed Type 1 diabetes and 13.7% Type 2 diabetes. Four (2.9%) of the 139 GDM patients tested...... for ICA were ICA-positive and three of these had Type 1 diabetes at follow-up, as well as three ICA-negative patients. The sensitivity, specificity, and predictive value of ICA-positivity for later development of diabetes were 50%, 99%, and 75%, respectively. None of the women was IAA-positive during...

  16. The Prognostic and Predictive Value of Soluble Type IV Collagen in Colorectal Cancer

    DEFF Research Database (Denmark)

    Rolff, Hans Christian; Christensen, Ib Jarle; Vainer, Ben

    2016-01-01

    PURPOSE: To investigate the prognostic and predictive biomarker value of type IV collagen in colorectal cancer. EXPERIMENTAL DESIGN: Retrospective evaluation of two independent cohorts of patients with colorectal cancer included prospectively in 2004-2005 (training set) and 2006-2008 (validation...... and validation set, respectively. The prognostic impact was present both in patients with metastatic and nonmetastatic disease. The predictive value of the marker was investigated in stage II and III patients. In the training set, type IV collagen was prognostic both in the subsets of patients receiving...... set). Plasma samples were available from 297 (training set) and 482 (validation set) patients. Type IV collagen determinations were performed using an ELISA. From the training set, 222 tumors were available for IHC. Clinical and follow-up data were retrieved from patient files and national registries...

  17. Predictive value of electrical restitution in hypokalemia-induced ventricular arrhythmogenicity

    DEFF Research Database (Denmark)

    Osadchii, Oleg E; Larsen, Anders Peter; Olesen, Soren Peter

    2009-01-01

    The ventricular action potential (AP) shortens exponentially upon a progressive reduction of the preceding diastolic interval. Steep electrical restitution slopes have been shown to promote wavebreaks, thus contributing to electrical instability. The present study was designed to assess the predi......The ventricular action potential (AP) shortens exponentially upon a progressive reduction of the preceding diastolic interval. Steep electrical restitution slopes have been shown to promote wavebreaks, thus contributing to electrical instability. The present study was designed to assess...... the predictive value of electrical restitution in hypokalemia-induced arrhythmogenicity. We recorded monophasic APs and measured effective refractory periods (ERP) at distinct ventricular epicardial and endocardial sites and monitored volume-conducted ECG at baseline and after hypokalemic perfusion (2.5 mM K...... of predictive value of APD(90) restitution is presumably related to different mode of changes in ventricular repolarization and refractoriness in a hypokalemic setting, whereby APD(90) prolongation may be associated with shortened ERP....

  18. Positive predictive value of diagnosis coding for hemolytic anemias in the Danish National Patient Register

    DEFF Research Database (Denmark)

    Hansen, Dennis Lund; Overgaard, Ulrik Malthe; Pedersen, Lars

    2016-01-01

    PURPOSE: The nationwide public health registers in Denmark provide a unique opportunity for evaluation of disease-associated morbidity if the positive predictive values (PPVs) of the primary diagnosis are known. The aim of this study was to evaluate the predictive values of hemolytic anemias...... registered in the Danish National Patient Register. PATIENTS AND METHODS: All patients with a first-ever diagnosis of hemolytic anemia from either specialist outpatient clinic contact or inpatient admission at Odense University Hospital from January 1994 through December 2011 were considered for inclusion....... Patients with mechanical reason for hemolysis such as an artificial heart valve, and patients with vitamin-B12 or folic acid deficiency were excluded. RESULTS: We identified 412 eligible patients: 249 with a congenital hemolytic anemia diagnosis and 163 with acquired hemolytic anemia diagnosis. In all...

  19. Efficacy and predictive value of clinical stage in non-surgical patients with esophageal cancer

    International Nuclear Information System (INIS)

    Liu Xiao; Wang Guiqi; He Shun

    2014-01-01

    Objective: To investigate the efficacy and predictive value of clinical stage in non-surgical patients with esophageal cancer (EC). Methods: A retrospective study was conducted in 358 EC patients who underwent radical surgery in our hospital from April 2003 to October 2010 and who had preoperative work-up including endoscopic esophageal ultrasound (EUS), esophagoscopy, thoracic CT scans,and contrast esophagography and had detailed information on postoperative pathological stages. The predictive value of preoperative clinical T/N stage based on EUS + CT for postoperative pathological stage was analyzed. The disease free survival (DFS) and overall survival (OS) were analyzed according to the UICC TNM classification (2002/ 2009) and the clinical stage based on imaging findings. Results: The median follow-up was 47 months.A total of 305 (85.2%) of all patients were analyzed by clinical stage based on EUS + CT.Among them, the predictive value of clinical T stage for pathological T stage was 0-88.6%, highest (88.6%) for T1 stage and lowest for T4 stage. The predictive value of clinical N stage (N 0 /N1) was 62.5-100%. The significant differences in OS and DFS rates based on both 2002 and 2009 UICC TNM classifications were noted (P=0.000 and 0.000). There were significant differences in OS between stage groups, except the comparison between two stage Ⅳ patients and other groups, according to 2002 UICC TNM classification. There were usually insignificant differences in OS between stage groups, according to 2009 UICC TNM classification. For the 305 patients staged clinically based on EUS and CT according to 2002 UICC TNM classification, significant differences in OS and DFS rates were noted (P=0.000 and 0.000). Conclusions: Imaging modalities show good predictive value for N stage (N0/N1),even though they cannot accurately provide the number of metastatic lymph nodes. The clinical stage based on EUS + CT can effectively predict the prognosis of non-surgical EC patients

  20. Continuous Spatial Process Models for Spatial Extreme Values

    KAUST Repository

    Sang, Huiyan

    2010-01-28

    We propose a hierarchical modeling approach for explaining a collection of point-referenced extreme values. In particular, annual maxima over space and time are assumed to follow generalized extreme value (GEV) distributions, with parameters μ, σ, and ξ specified in the latent stage to reflect underlying spatio-temporal structure. The novelty here is that we relax the conditionally independence assumption in the first stage of the hierarchial model, an assumption which has been adopted in previous work. This assumption implies that realizations of the the surface of spatial maxima will be everywhere discontinuous. For many phenomena including, e. g., temperature and precipitation, this behavior is inappropriate. Instead, we offer a spatial process model for extreme values that provides mean square continuous realizations, where the behavior of the surface is driven by the spatial dependence which is unexplained under the latent spatio-temporal specification for the GEV parameters. In this sense, the first stage smoothing is viewed as fine scale or short range smoothing while the larger scale smoothing will be captured in the second stage of the modeling. In addition, as would be desired, we are able to implement spatial interpolation for extreme values based on this model. A simulation study and a study on actual annual maximum rainfall for a region in South Africa are used to illustrate the performance of the model. © 2009 International Biometric Society.