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PubMed1984

Mechanical determinants of transient changes in stroke volume.

To better define beat-to-beat regulation of stroke volume (SV), the several-beat transient response of the left ventricle (LV) to sudden changes in hydraulic loading impedance was studied. Data were collected from eight canine isolated heart-lung preparations with controlled LV loading impedance. At a selected diastolic interval, a sudden increase in hydraulic loading resistance was induced. The resulting transient response in SV, end-diastolic pressure (EDP), and end-systolic pressure (ESP) was analyzed by comparing the relative predictive capability of six competing models, each incorporating different degrees of complexity in the relationship between SV and EDP and ESP. The basic model assumed linear LV pressure-volume relationships at both end diastole and end systole. Incorporation of nonlinear, end-state interaction or coronary perfusion pressure effects into the basic model did not improve predictive performance. Models incorporating SV and ESP of the preceding beat as well as ejecting beat ESP and EDP were consistently superior to all other models. The ranking of the relative influence of the determinants of SV was 1) ejecting beat ESP, 2) preceding beat ESP, 3) ejecting beat EDP, and 4) preceding beat SV.

Animals↗

PubMedDate not supplied

Intraocular lens power calculation in eyes after corneal refractive surgery.

PURPOSE: The purpose of this review article is to discuss the major reasons for postoperative hyperopia after cataract surgery following radial keratotomy (RK) and photorefractive keratectomy (PRK) and to illustrate potential methods for improvement of intraocular lens (IOL) power prediction after keratorefractive surgery based on exemplary model calculations. METHODS: We previously performed model calculations in eyes after PRK for myopia (-1.50 to -8.00 D, mean -5.40 +/- 1.90 D) using keratometry readings as measured by the Zeiss keratometer and the TMS-1 topography unit and as calculated using the "clinical history method" (spherical equivalent refraction change) and change in anterior surface keratometry readings. RESULTS: We found that after PRK, mean measured keratometry readings were significantly greater than respective calculated values considering the preoperative to postoperative change of anterior corneal surface (P < .001), which itself was significantly greater than calculated keratometry readings considering the preoperative to postoperative change of spherical equivalent refraction (P < .001). IOL power underestimation correlated significantly with the difference between preoperative and postoperative spherical equivalent refraction (P = .001). CONCLUSIONS: For correct assessment of keratometric readings to be entered into more than one modern third-generation IOL power calculation formula (but not a regression formula), the clinical history method should be applied whenever refraction and keratometric diopters before the keratorefractive procedure are available to the cataract surgeon. If preoperative keratometric diopters and refraction are not known, average central power on the postoperative videokeratograph may be used after RK, but refined calculation of keratometric diopters from radius of anterior and posterior corneal surface should be used after PRK and/or LASIK.

Cataract Extraction↗

PubMed2003

Visual development and the acquisition of motion velocity sensitivities.

We consider the hypothesis that systems learning aspects of visual perception may benefit from the use of suitably designed developmental progressions during training. Four models were trained to estimate motion velocities in sequences of visual images. Three of the models were developmental models in the sense that the nature of their visual input changed during the course of training. These models received a relatively impoverished visual input early in training, and the quality of this input improved as training progressed. One model used a coarse-to-multiscale developmental progression (it received coarse-scale motion features early in training and finer-scale features were added to its input as training progressed), another model used a fine-to-multiscale progression, and the third model used a random progression. The final model was nondevelopmental in the sense that the nature of its input remained the same throughout the training period. The simulation results show that the coarse-to-multiscale model performed best. Hypotheses are offered to account for this model's superior performance, and simulation results evaluating these hypotheses are reported. We conclude that suitably designed developmental sequences can be useful to systems learning to estimate motion velocities. The idea that visual development can aid visual learning is a viable hypothesis in need of further study.

Artifacts↗

PubMed1989

Children's expressions of spatial knowledge.

Different expressions of spatial knowledge were examined by having groups of first-, fourth-, and sixth-grade children perform model construction, verbal description, and route reversal tasks after they learned the correct path through a pedestrian maze. Age-related improvement was found in the rate of learning the maze and in the accuracy of verbal descriptions, suggesting that maze learning may be verbally mediated. All children performed well in sequencing intersections in the model but performed poorly in choosing path options in the model. Route reversal after learning was accurate and equivalent across groups. Overall, results suggest that both general and task-specific skills are involved in different products of spatial knowledge.

Child↗

PubMed2006

Modelling and evaluation of surgical performance using hidden Markov models.

Minimally invasive surgery has become very widespread in the last ten years. Since surgeons experience difficulties in learning and mastering minimally invasive techniques, the development of training methods is of great importance. While the introduction of virtual reality-based simulators has introduced a new paradigm in surgical training, skill evaluation methods are far from being objective. This paper proposes a method for defining a model of surgical expertise and an objective metric to evaluate performance in laparoscopic surgery. Our approach is based on the processing of kinematic data describing movements of surgical instruments. We use hidden Markov model theory to define an expert model that describes expert surgical gesture. The model is trained on kinematic data related to exercises performed on a surgical simulator by experienced surgeons. Subsequently, we use this expert model as a reference model in the definition of an objective metric to evaluate performance of surgeons with different abilities. Preliminary results show that, using different topologies for the expert model, the method can be efficiently used both for the discrimination between experienced and novice surgeons, and for the quantitative assessment of surgical ability.

Computer Simulation↗

PubMedDate not supplied

Is risk-adjustor selection more important than statistical approach for provider profiling? Asthma as an example.

OBJECTIVES: To examine how the selections of different risk adjustors and statistical approaches affect the profiles of physician groups on patient satisfaction. DATA SOURCES: Mailed patient surveys. Patients with asthma were selected randomly from each of 20 California physician groups between July 1998 and February 1999. A total of 2515 patients responded. RESEARCH DESIGN: A cross-sectional study. Patient satisfaction with asthma care was the performance indicator for physician group profiling. Candidate variables for risk-adjustment model development included sociodemographic, clinical characteristics, and self-reported health status. Statistical strategies were the ratio of observed-to-expected rate (OE), fixed effects (FE), and the random effects (RE) approaches. Model performance was evaluated using indicators of discrimination (C-statistic) and calibration (Hosmer-Lemeshow chi2). Ranking impact of using different risk adjustors and statistical approaches was based on the changes in absolute ranking (AR) and quintile ranking (QR) of physician group performance and the weighted kappa for quintile ranking. RESULTS: Variables that added significantly to the discriminative power of risk-adjustment models included sociodemographic (age, sex, prescription drug coverage), clinical (asthma severity), and health status (SF-36 PCS and MCS). Based on an acceptable goodness-of-fit (P > 0.1)and higher C-statistics, models adjusting for sociodemographic, clinical, and health status variables (Model S-C-H) using either the FE or RE approach were more favorable. However, the C-statistic (=0.68) was only fair for both models. The influence of risk-adjustor selection on change of performance ranking was more salient than choice of statistical strategy (AR: 50%-80% v. 20%-55%; QR: 10%-30% v. 0%-10%). Compared to the model adjusting for sociodemographic and clinical variables only and using OE approach, the Model S-C-H using RE approach resulted in 70% of groups changing in AR and 25% changing in QR (weighted kappa: 0.88). Compared to the Consumer Assessment of Health Plans model, the Model S-C-H using RE approach resulted in 65% of groups changing in AR and 20% changing in QR (weighted kappa: 0.88). CONCLUSIONS: In comparing the performance of physician groups on patient satisfaction with asthma care, the use of sociodemographic, clinical, and health status variables maximized risk-adjustment model performance. Selection of risk adjustors had more influence on ranking profiles than choice of statistical strategies. Stakeholders employing provider profiling should pay careful attention to the selection of both variables and statistical approach used in risk-adjustment.

Adult↗

PubMed2006

Prediction of significant fibrosis in hepatitis C virus infected liver transplant recipients by artificial neural network analysis of clinical factors.

OBJECTIVES: Interest in developing noninvasive markers of liver fibrosis continues to increase, especially in recurrent hepatitis C virus infection after liver transplantation. Recently, a model for predicting significant fibrosis (bridging fibrosis and cirrhosis) on the basis of logistic regression and routine laboratory data has been proposed (logit model). The aim of the present study was to evaluate the accuracy of an artificial neural network, a technique reported to work better than logit models in complex biological situations, built on those same clinical variables and data set of patients, in predicting significant fibrosis. METHODS: The neural network was constructed on the training set of 414 protocol biopsies, from liver transplant recipients, and then tested on the remaining 96 biopsies, as validation set. Model performances of neural network and logit model were evaluated and compared by means of areas under receiver operating characteristic curves. RESULTS: With a cutoff value of >0.4 to predict significant fibrosis, the neural network provided sensitivity, specificity, positive and negative predictive values, respectively, of 100, 79.5, 60.5 and 100%, in the validation set. The performance of the neural network was significantly better than that of the logit model (in the validation set area under the curve = 0.93 vs. 0.84; P = 0.045). CONCLUSIONS: Artificial neural network provides accurate prediction of the presence or absence of significant fibrosis from clinical variables, allowing theoretically protocol liver biopsy to be avoided in several instances, a result of particular interest, given the lack of other types of reliable noninvasive indexes of fibrosis in the setting of transplantation.

Adult↗

PubMed1996

A method for checking regression models in survival analysis based on the risk score.

We propose to perform model check for the Cox and Aalen regression models using martingale residual processes grouped after the risk score. Asymptotic distributions of the grouped martingale residual processes are deduced, so both formal and graphical model check can be performed. The method is validated by stochastic simulation. A data example with patients with primary biliary cirrhosis of the liver is discussed.

Humans↗

PubMed2006

Vascular dynamics and BOLD fMRI: CBF level effects and analysis considerations.

Changes in the cerebral blood flow (CBF) baseline produce significant changes to the hemodynamic response. This work shows that increases in the baseline blood flow level produce blood oxygenation-level dependent (BOLD) and blood flow responses that are slower and lower in amplitude, while decreases in the baseline blood flow level produce faster and higher amplitude hemodynamic responses. This effect was characterized using a vascular model of the hemodynamic response that separated arterial blood flow response from the venous blood volume response and linked the input stimulus to the vascular response. The model predicted the baseline blood flow level effects to be dominated by changes in the arterial vasculature. Specifically, it predicted changes in the arterial blood flow time constant and venous blood volume time constant parameters of +294% and -24%, respectively, for a 27% increase in the baseline blood flow. The vascular model performance was compared to an empirical model of the hemodynamic response. The vascular and empirical hemodynamic models captured most of the baseline blood flow level effects observed and can be used to correct for these effects in fMRI data. While the empirical hemodynamic model is easy to implement, it did not incorporate any explicit physiological information.

Algorithms↗

PubMed2003

Error in skilled performance: a control model of prescribing.

A control model of skilled performance is proposed as a framework for understanding why prescribing errors occur at a particular rate. Model error rate depends on skill level, system design characteristics, the range of different types of prescriptions that are produced and the time available to complete each prescription. The parameters of the model can be adjusted so it produces error rates that are quantitatively equal to those found in studies of the incidence of different types of prescribing error. The model will also produce error rates that are qualitatively consistent with the results of studies that show increases in prescribing error rates as a result of increases in distractions and workload. The model is used to determine the likely effectiveness of different prescribing error prevention interventions.

Attention↗

PubMedDate not supplied

Using an Hebbian learning rule for multi-class SVM classifiers.

Regarding biological visual classification, recent series of experiments have enlighten the fact that data classification can be realized in the human visual cortex with latencies of about 100-150 ms, which, considering the visual pathways latencies, is only compatible with a very specific processing architecture, described by models from Thorpe et al. Surprisingly enough, this experimental evidence is in coherence with algorithms derived from the statistical learning theory. More precisely, there is a double link: on one hand, the so-called Vapnik theory offers tools to evaluate and analyze the biological model performances and on the other hand, this model is an interesting front-end for algorithms derived from the Vapnik theory. The present contribution develops this idea, introducing a model derived from the statistical learning theory and using the biological model of Thorpe et al. We experiment its performances using a restrained sign language recognition experiment. This paper intends to be read by biologist as well as statistician, as a consequence basic material in both fields have been reviewed.

Algorithms↗

PubMed2005

Asymmetric subsethood-product fuzzy neural inference system (ASuPFuNIS).

This paper presents an asymmetric subsethood-product fuzzy neural inference system (ASuPFuNIS) that directly extends the SuPFuNIS model by permitting signal and weight fuzzy sets to be modeled by asymmetric Gaussian membership functions. The asymmetric subsethood-product network admits both numeric as well as linguistic inputs. Input nodes, which act as tunable feature fuzzifiers, fuzzify numeric inputs with asymmetric Gaussian fuzzy sets; and linguistic inputs are presented as is. The antecedent and consequent labels of standard fuzzy if-then rules are represented as asymmetric Gaussian fuzzy connection weights of the network. The model uses mutual subsethood based activation spread and a product aggregation operator that works in conjunction with volume defuzzification in a gradient descent learning framework. Despite the increase in the number of free parameters, the proposed model performs better than SuPFuNIS, on various benchmarking problems, both in terms of the performance accuracy and architectural economy and compares excellently with other various existing models with a performance better than most of them.

Algorithms↗

PubMed2026

Machine learning-based clinical prediction model and multi-omics integration for assessing pancreatic cancer risk in new-onset diabetes.

BACKGROUND: Given that pancreatic cancer (PC) is typically diagnosed at an advanced stage but is often preceded by new-onset diabetes mellitus (NODM), providing a window for early detection, we sought to develop and validate an interpretable machine-learning model integrated with multi-omics profiling to identify early biomarkers of NODM-associated PC. METHODS: In a population-based cohort, individuals with NODM-associated PC and NODM without PC were identified and randomly divided (70:30) into training and validation sets after feature selection. Eight machine learning (ML) classifiers were compared using fivefold cross-validation, and model performance was evaluated in terms of discrimination, calibration, and decision curve–based clinical utility. We evaluated interpretability using the Shapley additive explanations (SHAP) analyses. Mechanistically, Olink proteomic profiling and metabolomics were analyzed through clinical classifications and model-defined risk strata. RESULTS: Categorical boosting achieved the best performance in the independent validation set (AUROC = 0.844). The NODM cohort was stratified into high- (n = 2,362) and low-risk (n = 5,030) groups, and internal validation together with SHAP analyses demonstrated consistent model performance and identified clinically interpretable predictors. Proteomic and metabolomic analyses under clinical and risk-based grouping identified 39 overlapping differentially expressed proteins and 145 overlapping metabolites with enriched across 11 shared KEGG pathways. Cross-platform validation highlighted PLTP, CRTAC1, and ITGAV as serum biomarkers with a strong potential for early NODM-PC detection. CONCLUSIONS: We developed an interpretable ML framework centered on NODM enables practical risk stratification for early PC detection by multi-omics and provides a pathway of ML-based triage followed by biomarker confirmation for earlier detection and diagnosis.

Humans↗

PubMed2004

Fatigue models for applied research in warfighting.

The U.S. Department of Defense (DOD) has long pursued applied research concerning fatigue in sustained and continuous military operations. In 1996, Hursh developed a simple homeostatic fatigue model and programmed the model into an actigraph to give a continuous indication of performance. Based on this initial work, the Army conducted a study of 1 wk of restricted sleep in 66 subjects with multiple measures of performance, termed the Sleep Dose-Response Study (SDR). This study provided numerical estimation of parameters for the Walter Reed Army Institute of Research Sleep Performance Model (SPM) and elucidated the relationships among several sleep-related performance measures. Concurrently, Hursh extended the original actigraph modeling structure and software expressions for use in other practical applications. The model became known as the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) Model, and Hursh has applied it in the construction of a Fatigue Avoidance Scheduling Tool. This software is designed to help optimize the operational management of aviation ground and flight crews, but is not limited to that application. This paper describes the working fatigue model as it is being developed by the DOD laboratories, using the conceptual framework, vernacular, and notation of the SAFTE Model. At specific points where the SPM may differ from SAFTE, this is discussed. Extensions of the SAFTE Model to incorporate dynamic phase adjustment for both transmeridian relocation and shift work are described. The unexpected persistence of performance effects following chronic sleep restriction found in the SDR study necessitated some revisions of the SAFTE Model that are also described. The paper concludes with a discussion of several important modeling issues that remain to be addressed.

Aviation↗

PubMed2006

Reversing demosaicking and compression in color filter array image processing: performance analysis and modeling.

In the conventional processing chain of single-sensor digital still cameras (DSCs), the images are captured with color filter arrays (CFAs) and the CFA samples are demosaicked into a full color image before compression. To avoid additional data redundancy created by the demosaicking process, an alternative processing chain has been proposed to move the compression process before the demosaicking. Recent empirical studies have shown that the alternative chain can outperform the conventional one in terms of image quality at low compression ratios. To provide a theoretically sound basis for such conclusion, we propose analytical models for the reconstruction errors of the two processing chains. The models developed confirm the results of existing empirical studies and provide better understanding of DSC processing chains. The modeling also allows performance predictions for more advanced compression and demosaicking methods, thus providing important cues for future development in this area.

Algorithms↗

PubMed2004

A phenomenological model for estimating metabolic energy consumption in muscle contraction.

A phenomenological model for muscle energy consumption was developed and used in conjunction with a simple Hill-type model for muscle contraction. The model was used to address two questions. First, can an empirical model of muscle energetics accurately represent the total energetic behavior of frog muscle in isometric, isotonic, and isokinetic contractions? And second, how does such a model perform in a large-scale, multiple-muscle model of human walking? Four simulations were conducted with frog sartorius muscle under full excitation: an isometric contraction, a set of isotonic contractions with the muscle shortening a constant distance under various applied loads, a set of isotonic contractions with the muscle shortening over various distances under a constant load, and an isokinetic contraction in lengthening. The model calculations were evaluated against results of similar thermal in vitro experiments performed on frog sartorius muscle. The energetics model was then incorporated into a large-scale, multiple-muscle model of the human body for the purpose of predicting energy consumption during normal walking. The total energy estimated by the model accurately reflected the observed experimental behavior of frog muscle for an isometric contraction. The model also accurately reproduced the experimental behavior of frog muscle heat production under isotonic shortening and isokinetic lengthening conditions. The estimated rate of metabolic energy consumption for walking was 29% higher than the value typically obtained from gait measurements.

Animals↗

PubMed2000

Urn models and vaccine efficacy estimation.

We derive the distribution of the number of infections among unvaccinated and vaccinated individuals for model 1 (leaky) and model 2 (all/nothing) vaccines, assuming random mixing of a homogeneous population. For all/nothing vaccines, we show that the distribution of the number of infected vaccinated individuals conditioning on n observed infections follows a hypergeometric distribution, and the vaccine efficacy estimate (VE) can be derived from the usual estimate of the total population size in a capture-recapture sampling program. For leaky vaccines, we show that the number of vaccinated infected follows a distribution that was first derived by Wallenius. We found that the current point estimates of VE for each model perform very well, but the urn model construction presented here provides a strong framework for estimation and hypothesis testing on the parameters, and can be applied when the available data are a sample of the population. Since the method does not require an underlying transmission model, it can be applied to estimate the VE for non-contagious diseases.

Adolescent↗

PubMedDate not supplied

Predicting rehospitalization and outpatient services from administration and clinical databases.

The study tests whether psychiatric services utilization may be predicted from administrative databases without clinical variables equally as well as from databases with clinical variables. Persons with a psychiatric hospitalization at an urban medical center were followed for 1 year postdischarge (N = 1384.) Dependent variables included statewide rehospitalization and the number of hours of outpatient services received. Three linear and logistic regression models were developed and cross-validated: a basic model with limited administrative independent variables, an intermediate model with diagnostic and limited clinical indicators, and a full model containing additional clinical predictors. For rehospitalization, the clinical cross-validated model accounted for twice the variance accounted by the basic model (adjusted R2 = .13 and .06, respectively). For outpatient hours, the basic cross-validated model performed as well as the clinical model (adjusted R2 = .36 and .34, respectively). Clinical indicators such as assessment of functioning and co-occurring substance use disorder should be considered for inclusion in predicting rehospitalization.

Adult↗