[Is the "Berne model" in the performing blood transfusion still justified?].
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Blood Transfusion↗
SEARCH · PubMed Health
Results for “Model performance”
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[Modelling of attributive performances in concept-like classes of objects].
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Classification↗
Proceedings: Enhanced avoidance in poor performers as a model for evaluating antidepressant agents.
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Animals↗
[Experience with use of jet injectors for massive immunization against infectious diseases. I. Comparative assessment of the performance of different models of jet injectors].
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Cholera↗
Mathematical modeling of human performance errors for reliability analysis of systems. AMRL-TR-68-93.
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Ergonomics↗
Factors affecting resident performance: development of a theoretical model and a focused literature review.
PURPOSE: The clinical performances of physicians have come under scrutiny as greater public attention is paid to the quality of health care. However, determinants of physician performance have not been well elucidated. The authors sought to develop a theoretical model of physician performance, and explored the literature about factors affecting resident performance. METHOD: Using expert consensus panel, in 2002-03 the authors developed a hypothesis-generating model of resident performance. The developed model had three input factors (individual resident factors, health care infrastructure, and medical education infrastructure), intermediate process measures (knowledge, skills, attitudes, habits), and final health outcomes (affecting patient, community and population). The authors used factors from the model to focus a PubMed search (1967-2002) for all original articles related to the factors of individual resident performance. RESULTS: The authors found 52 original studies that examined factors of an individual resident's performance. They describe each study's measurement instrument, study design, major findings, and limitations. Studies were categorized into five domains: learning styles/personality, social/financial factors, practice preferences, personal health, and response to job environment. Few studies examined intermediate or final performance outcomes. Most were single-institution, cross-sectional, and survey-based studies. CONCLUSIONS: Attempting to understand resident performance without understanding factors that influence performance is analogous to examining patient adherence to medication regimens without understanding the individual patient and his or her environment. Based on a systematic review of the literature, the authors found few discrete associations between the factors of individual resident and the resident's actual job performance. Additionally, they identify and discuss major gaps in the educational literature.
Adult↗
An improved Peronnet-Thibault mathematical model of human running performance.
Using an improved Peronnet-Thibault model to analyse the maximal power available during exercise, it was found that a 3rd-order relaxation process for the decreasing dynamics of aerobic power can describe accurately the data available for world track records and aerobic-to-total energy ratio (ATER). It was estimated that the time-scales for the decreasing dynamics are around 25 s for anaerobic power output and that they range from 2.12 h to 7.8 days for aerobic power output. In agreement with experimental evidence, the ATER showed a rapid increase during the first 300 s of exercise duration, to achieve an asymptote close to 100% after 1,000 s. In addition, the transition time when the ATER rose above 50% was found to be at a race duration of about 100 s, which would correspond to race distances of about 800 m. The results suggest that the aerobic power output achieves its maximal value at 300-400 s, and reaches a plateau at 26-28 W.kg(-1) that lasts about 5,000 s. After this period, the aerobic power output decreases slowly due to the contribution of long time-scale metabolic processes having smaller energy contributions (about 30% to 40% of the total aerobic power output).
Energy Metabolism↗
Model for predicting work performance among persons with multiple sclerosis.
A causal model was tested on a sample of 201 MS subjects for the purpose of examining the role of selected demographic factors, work impediments, and work enhancers as predictors of activities essential for performing outside employment, home making, and for personal care. The results showed a relatively good fit of the model to the data through a number of indicators. Explained variance included: 9.0% in work impediments from the direct effect of age; 32.3% in work enhancers--A (job and environmental adjustment, social support, healthful practices) through direct effects from MS duration and work impediments; 9.8% in work enhancers--B (personal attributes) through direct effects from MS duration and work impediments; 61.1% in work performance through direct effects from the antecedent variables (age, education), stress-appraisal variable (work impediments), and coping variables (work enhancers-A, work enhancers-B).
Activities of Daily Living↗
Dental data mining: potential pitfalls and practical issues.
Knowledge Discovery and Data Mining (KDD) have become popular buzzwords. But what exactly is data mining? What are its strengths and limitations? Classic regression, artificial neural network (ANN), and classification and regression tree (CART) models are common KDD tools. Some recent reports (e.g., Kattan et al., 1998) show that ANN and CART models can perform better than classic regression models: CART models excel at covariate interactions, while ANN models excel at nonlinear covariates. Model prediction performance is examined with the use of validation procedures and evaluating concordance, sensitivity, specificity, and likelihood ratio. To aid interpretation, various plots of predicted probabilities are utilized, such as lift charts, receiver operating characteristic curves, and cumulative captured-response plots. A dental caries study is used as an illustrative example. This paper compares the performance of logistic regression with KDD methods of CART and ANN in analyzing data from the Rochester caries study. With careful analysis, such as validation with sufficient sample size and the use of proper competitors, problems of naïve KDD analyses (Schwarzer et al., 2000) can be carefully avoided.
Child↗
Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance.
A reliable model for any wastewater treatment plant is essential in order to provide a tool for predicting its performance and to form a basis for controlling the operation of the process. This would minimize the operation costs and assess the stability of environmental balance. This process is complex and attains a high degree of nonlinearity due to the presence of bio-organic constituents that are difficult to model using mechanistic approaches. Predicting the plant operational parameters using conventional experimental techniques is also a time consuming step and is an obstacle in the way of efficient control of such processes. In this work, an artificial neural network (ANN) black-box modeling approach was used to acquire the knowledge base of a real wastewater plant and then used as a process model. The study signifies that the ANNs are capable of capturing the plant operation characteristics with a good degree of accuracy. A computer model is developed that incorporates the trained ANN plant model. The developed program is implemented and validated using plant-scale data obtained from a local wastewater treatment plant, namely the Doha West wastewater treatment plant (WWTP). It is used as a valuable performance assessment tool for plant operators and decision makers. The ANN model provided accurate predictions of the effluent stream, in terms of biological oxygen demand (BOD), chemical oxygen demand (COD) and total suspended solids (TSS) when using COD as an input in the crude supply stream. It can be said that the ANN predictions based on three crude supply inputs together, namely BOD, COD and TSS, resulted in better ANN predictions when using only one crude supply input. Graphical user interface representation of the ANN for the Doha West WWTP data is performed and presented.
Evaluation Studies as Topic↗
Performance of different radiotherapy workload models.
PURPOSE: The purpose of this study was to evaluate the performance of different radiotherapy workload models using a prospectively collected dataset of patient and treatment information from a single center. METHODS AND MATERIALS: Information about all individual radiotherapy treatments was collected for 2 weeks from the three linear accelerators (linacs) in our department. This information included diagnosis code, treatment site, treatment unit, treatment time, fields per fraction, technique, beam type, blocks, wedges, junctions, port films, and Eastern Cooperative Oncology Group (ECOG) performance status. We evaluated the accuracy and precision of the original and revised basic treatment equivalent (BTE) model, the simple and complex Addenbrooke models, the equivalent simple treatment visit (ESTV) model, fields per hour, and two local standards of workload measurement. RESULTS: Data were collected for 2 weeks in June 2001. During this time, 151 patients were treated with 857 fractions. The revised BTE model performed better than the other models with a mean [observed -predicted] of 2.62 (2.44-2.80). It estimated 88.0% of treatment times within 5 min, which is similar to the previously reported accuracy of the model. CONCLUSION: The revised BTE model had similar accuracy and precision for data collected in our center as it did for the original dataset and performed the best of the models assessed. This model would have uses for patient scheduling, and describing workloads and case complexity.
Appointments and Schedules↗
Model-based administration of inhalation anaesthesia. 3. Validating the system model.
BACKGROUND: We quantified the predictive performance of our computer model of the administration of inhalation anaesthesia from a Datex-Ohmeda Modulus CD circle-absorber system. METHODS: In 50 patients, desflurane anaesthesia was maintained with a fresh-gas flow (FGF) of 0.5 litres min(-1) of both nitrous oxide and oxygen, preceded by fast (n=14) or slow (n=36) induction: FGF greater than total ventilation, Group F; FGF equal to 1.0 litres min(-1), Group S. The two versions of the model studied differed in the size of their inter-tissue diffusion, as 0 (version 1) and 3% (version 2) of the cardiac output was shifted from the viscera to adipose tissue. Model performance was judged by comparing measured and predicted gas concentrations in terms of three variables for each gas concentration in each patient: root mean squared error (rmse=total error), bias (mean predicted - measured) (systematic error), and scatter (error around bias). These variables were then averaged over all patients. These measures were calculated overall, and separately for each group and each stage (1 = induction or 2 = maintenance). RESULTS: Model predictions were in reasonable to very good agreement with clinically obtained data. Version 2 performed better than version 1. Differences between groups were not demonstrated. The model performed better for stage 2, but only for desflurane. In group S, results (mean (SD); as percentages of the measured values for nitrous oxide, oxygen and desflurane) in the order rmse, bias, and scatter were for end-tidal concentrations of nitrous oxide: 8 (4), 8 (5), 2 (1)%; oxygen: 11 (4), -10 (6), 2 (1.1)%; nitrogen: 0.9 (0.6), -0.8 (0.6), 0.2 (0.1) vol%; carbon dioxide: 1.8 (0.6), 1.8 (0.6), 0.2 (0.1) vol%; desflurane, stage 2: 8 (4), 4 (7), 4 (2)%, vs 15 (6), -10 (8), 9 (4)% for stage 1. CONCLUSION: Administration of inhalation anaesthesia can be based on version 2 of this model, but must be guided by active monitoring.
Adolescent↗
Stability and performance of a population pharmacokinetic model.
This study aimed to determine the stability (in terms of covariate selection) of a population pharmacokinetic model and evaluate its performance in the absence of a test data set. Data from 88 full-term infants, 11 of whom were human immunodeficiency virus (HIV)-seropositive, taking an antiinfective agent were analyzed using exploratory data analysis methods and the nonlinear mixed-effects modeling (NONMEM) program to obtain the final population pharmacokinetic model. The stability of the population pharmacokinetic model was tested using the nonparametric bootstrap approach in four steps: 1) with the base pharmacokinetic model, 100 bootstrap replicates of the original data were generated by sampling with replacement; 2) ascertainment that each bootstrap data replicate was described by the basic structural model using the NONMEM objective function; 3) generalized additive modeling (GAM) applied to empiric Bayesian estimates for covariate selection at alpha = 0.05 and a frequency (f) cutoff value of 0.50; and 4) NONMEM population model building using covariates selected in the third step with alpha = 0.005. Performance of the population pharmacokinetic model was evaluated using 200 additional bootstrap replicates of the data by fitting the model obtained in step 4 to them. Parameters obtained were compared with those obtained in the model stability step, and improved prediction error, a measure of predictive accuracy as an index of internal validation, was computed. The reciprocal of serum creatinine (RSC; f = 0.73) and HIV (f = 0.70) were selected by GAM as predictors of clearance (Cl). The population pharmacokinetic model obtained without the determination of model stability included RSC as a predictor of Cl, but the final model from the model stability step included both HIV and RSC as predictors of Cl. Final population pharmacokinetic parameters were obtained with this model fitted to the original data; however, the 95% confidence interval on the HIV status regression coefficient included zero, indicating no significance. The mean parameter estimates obtained with the additional 200 bootstrap replicates of data were within 15% of those obtained with the final model at the regression stability step. Bootstrap resampling procedure is useful for evaluating the stability and performance of a population model by repeatedly fitting it to the bootstrap samples when there is no test data set.
Adolescent↗
Identification of Mycobacterium tuberculosis and M. avium complex directly from smear-positive sputum specimens and BACTEC 12B cultures by high-performance liquid chromatography with fluorescence detection and computer-driven pattern recognition models.
A high-performance liquid chromatography method that utilized fluorescence detection (HPLC-FL) of mycolic acid 6,7-dimethoxycoumarin esters was developed to identify Mycobacterium tuberculosis (MTB) and M. avium complex (MAC) directly from fluorochrome stain smear-positive sputum specimens and young BACTEC 12B cultures. HPLC-FL chromatograms from a training set that included 202 smear-positive clinical sputum specimens and 343 mycobacterial cultures were used to construct a calibrated peak-naming table and computer-based pattern recognition models for MTB and MAC. Pattern recognition model performance was measured with an evaluation set of samples that included 251 smear-positive clinical sputum specimens and 167 BACTEC 12B cultures. Evaluation sputum specimens were culture positive for MTB (n = 132) and MAC (n = 48). With evaluation sputa, the MTB and MAC models were 56.8 and 33.3% sensitive, respectively. Evaluation set BACTEC 12B cultures were culture positive for MTB (n = 97) and MAC (n = 53). The sensitivities of the MTB and MAC models for identification of BACTEC 12B cultures were 99.0 and 94.3%, respectively. The specificity of both models was 100% for both types of evaluation samples. The average times from BACTEC 12B inoculation to cell harvest were 10.2 and 7.4 days for MTB and MAC, respectively. HPLC-FL can identify MTB and MAC in 1 day from many smear-positive sputa. Rapid and sensitive identification of MTB and MAC from young BACTEC 12B cultures was achieved.
Bacteriological Techniques↗
Modelling health-related performance indices.
PRIMARY OBJECTIVE: The purpose of the present review is to critically examine the various models used to describe health-related performance indices (e.g. grip-strength, lung function, arterial blood pressure, anaerobic and aerobic power, etc.). MAIN OUTCOMES AND RESULTS: The majority of health-related performance indices are known to increase proportionally with body size and decline with age, although some, for example arterial blood pressure and cholesterol, increase with both age and excessive body weight. Historically, the confounding effects of body size and age have been controlled using multiple linear or polynomial regression models assuming a constant additive error variance. Based on maximum likelihood goodness-of-fit criterion, an alternative class of allometric models with a multiplicative error term arc shown to describe such proportional relationships more adequately in both cross-sectional and longitudinal studies. CONCLUSIONS: When modelling health-related performance indices, multiplicative, allometric models incorporate the concept of a proportional association as an integral part of its model form. Such models guarantee realistic values of health-related response variables both within and beyond the range of observations, ensuring biologically sound estimates of the response variables for subjects of all ages. After a logarithmic transformation, the models can be fitted using ordinary linear regression that will naturally help to overcome the heteroscedasticity observed in such data. However, if the logarithmic transformation fails to provide a constant error variance, an alternative approach is to model the error variance itself using log-linear regression. For longitudinal data, the use of multilevel modelling provides an alternative method of modelling error variation observed in health-related performance indices.
Adolescent↗
Understanding the sources of performance in deep drug response models reveals insights and improvements.
MOTIVATION: Anti-cancer drug response prediction (DRP) using cancer cell lines (CLs) is crucial in stratified medicine and drug discovery. Recently, new deep learning models for DRP have improved performance over their predecessors. However, different models use different input data types and architectures making it hard to find the source of these improvements. Here we consider published DRP models that report state-of-the-art performance predicting continuous response values. These models take chemical structures of drugs and omics profiles of CLs as input. RESULTS: By experimenting with these models and comparing with our simple baselines, we show that no performance comes from drug features, instead, performance is due to the transcriptomics CL profiles. Furthermore, we show that, depending on the testing type, much of the current reported performance is a property of the training target values. We address these limitations by creating BinaryET and BinaryCB that predict binary drug response values, guided by the hypothesis that this reduces the noise in the drug efficacy data. Thus, better aligning them with biochemistry that can be learnt from the input data. BinaryCB leverages a chemical foundation model, while BinaryET is trained from scratch using a transformer-type architecture. We show that these models learn useful chemical drug features, which is the first time this has been demonstrated for multiple testing types to our knowledge. We further show binarizing the drug response values causes the models to learn useful chemical drug features. We also show that BinaryET improves performance over BinaryCB, and the published models that report state-of-the-art performance. AVAILABILITY AND IMPLEMENTATION: Code is available from https://github.com/Nik-BB/Understanding_DRP_models.
Humans↗
Classroom social experiences as predictors of academic performance.
A model linking children's peer acceptance in the classroom to academic performance via academic self-concept and internalizing symptoms was tested in a longitudinal study. A sample of 248 children was followed from 4th to 6th grade, with data collected from different informants in each year of the study to reduce respondent bias. A path analysis supported the model; a lack of peer acceptance in the classroom in 4th grade predicted lower academic self-concept and more internalizing symptoms the following year, which in turn, predicted lower academic performance in 6th grade. An alternative path with internalizing symptoms predicting declines in peer acceptance was tested and received some support as well. Implications of the findings for schools are discussed.
Achievement↗
Risk-adjusted morbidity and mortality models to compare the performance of two units after major lung resections.
OBJECTIVE: We sought to develop risk-adjusted morbidity and mortality models to compare the performance of 2 different thoracic surgery units in patients submitted to major lung resections. METHODS: Seven hundred forty-three patients (551 male and 192 female patients) who underwent lobectomy (n = 611) or pneumonectomy (n = 132) from January 2000 through August 2004 at 2 European thoracic units (519 patients in unit A and 224 patients in unit B) were analyzed. Risk-adjusted models of 30-day or in-hospital cardiopulmonary morbidity and mortality were developed by using stepwise logistic regression analyses and validated by means of bootstrap analysis. Preoperative and operative variables were initially screened by using univariate analysis. Those with a P value of less than .10 were used as independent variables in the regression analyses. The regression equations were then used to estimate the risk of outcome, and the observed and predicted outcome rates of the 2 units were compared by using the z test for comparison of proportions. RESULTS: The following regression models were developed. Predicted morbidity: lnR/1-R=-2.4+0.03Xage-0.02XppoFEV1+0.6Xcardiaccomorbidity (Hosmer-Lemeshow statistic = 6.1 [P = .6], c index = 0.65). Predicted mortality: lnR/1-R=-6.97+0.095Xage-0.042XppoFEV1 (Hosmer-Lemeshow statistic = 2.99 [P = .9], c index = 0.77). The models proved to be stable at bootstrap analyses. No differences were noted between observed and predicted outcome rates within each unit, despite an apparent unadjusted better performance of unit B. CONCLUSIONS: The use of risk-adjusted outcome models avoided misleading information derived from the unadjusted analysis of performance. Risk modeling is essential for the evaluation of the quality of care.
Aged↗