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At least 1,027 records · Page 57Linked to original sources
PubMed2005

[Bayesian regularized BP neural network model for quantitative relationship between the electrochemical reduction potential and molecular structures of chlorinated aromatic compounds].

Bayesian regularized BP neural network (BRBPNN) technique was applied in QSPR model in environmental field. The BRBPNN model for quantitative relationship between the electrochemical reduction potential (ERP) and chemical structures of 87 chlorinated aromatic compounds was established. The structure descriptor pool is consisted of Cl number (Cl), molecular weight (MW) and 6 quantum chemistry parameters which are calculated by MOPAC2000 built in ChemOffice2004, including energy of the highest occupied molecular orbital (E(HOMO)), energy of the lowest occupied molecular orbital (E(LUMO)), heat of formation(HF), dipole(DIP), electronic energy(EE), core-core repulsion(CCR). The achieved optimal network structure was 6-20-1, which possessed stronger fitting and prediction capacity than that of the stepwise linear regression and with the correlation coefficients square and the mean square error for the training set and the test set as 0.999 and 0.000105, 0.965 and 0.00159 respectively. The sum of square weights between each input neuron and the hidden layer of BRBPNN(6-20-1) indicate the effect of descriptor on the electric potential declining in the order of ELUMO > EHOMO > HF> CCR > EE > DIP. The scatter diagrams show that the EE descriptors had positive effect on ERP, and ELUMO, HF, DIP had negative effects, and EHOMO and CCR showed ambiguous effects. Results show that Bayesian regularized BP neural network is of automated regularization parameter selection capability and thus may ensure the excellent generation ability and robustness. This study threw more light on the applicability of electrochemical treatment for the chlorinated aromatic compounds and the analysis on electrochemical reduction mechanism.

Bayes Theorem↗
PubMed2005

A new method for sleep apnea classification using wavelets and feedforward neural networks.

OBJECTIVES: This paper presents a novel approach for sleep apnea classification. The goal is to classify each apnea in one of three basic types: obstructive, central and mixed. MATERIALS AND METHODS: Three different supervised learning methods using a neural network were tested. The inputs of the neural network are the first level-5-detail coefficients obtained from a discrete wavelet transformation of the samples (previously detected as apnea) in the thoracic effort signal. In order to train and test the systems, 120 events from six different patients were used. The true error rate was estimated using a 10-fold cross validation. The results presented in this work were averaged over 100 different simulations and a multiple comparison procedure was used for model selection. RESULTS: The method finally selected is based on a feedforward neural network trained using the Bayesian framework and a cross-entropy error function. The mean classification accuracy, obtained over the test set was 83.78+/-1.90%. CONCLUSION: The proposed classifier surpasses, up to the author's knowledge, other previous results. Finally, a scheme to maintain and improve this system during its clinical use is also proposed.

Algorithms↗
PubMed1997

Bayesian registration of models using finite element eigenmodes.

This paper is concerned with registering three-dimensional wire-frame organ models. This involves finding correspondences between points on the models of two different examples of the same organ. Such registration is widely used in the processing of medical data; for example in segmentation, or to superimpose functional information on a more detailed structural map. The algorithm described in this paper is based on matching the modes of deformation of organ shapes. Modes with lower spatial frequency characterise large scale organ features whereas small scale variations determine the high frequency modes. First, the organ sizes are normalised using a generalised version of the centroid size metric. The axes of the fundamental frequency modes are then aligned to provide initial rigid-body registration. The registration is refined by matching increasingly high frequency modes using the 'Highest confidence first' algorithm. The matches are evaluated using a Bayesian combination of local prior and likelihood functions. The prior is derived from the Gompertz metric of biological growth and ensures that physically impossible matches are not accepted. The likelihood function is a measure of the similarity between local modal deformation components. The registration algorithm has been applied by the authors in the analysis of three dimensional ultrasound data. Results are presented showing the registration of two liver models derived from 3D ultrasound.

Algorithms↗
PubMed1995

Pharmaco-informatics: more precise drug therapy from 'multiple model' (MM) adaptive control regimens: evaluation with simulated vancomycin therapy.

A multiple model (MM) stochastic control of dosage regimens permits essentially optimal use of the information contained in either a population pharmacokinetic model or in a MM Bayesian updated parameter set to achieve and maintain selected therapeutic goals with optimal precision. The regimens are visibly more precise than those achieved using mean parameter values. Feedback has now also been incorporated into the MM software. An evaluation of MM adaptive control precision versus control achieved using population mean parameter values is presented using a real population model (Vancomycin). Further feedback control was evaluated, incorporating simulated clinical errors in the preparation and administration of doses.

Bayes Theorem↗
PubMed1986

A microcomputer program for critical evaluation of diagnostic tests.

We developed a microcomputer program that provides a Bayesian model of diagnostic performance and a simple decision tree model of clinical utility. We have used this program to review diagnostic performance and clinical utility for proposed new services at our 360-bed university hospital. We believe that significant benefits can be achieved if medical journals report complete data on test performance. First, this allows physicians to perform their own evaluations of diagnostic performance. Second, this allows physicians to evaluate clinical utility using either standard decision trees or decision trees that reflect specific clinical problems.

Bayes Theorem↗
PubMedDate not supplied

Bayesian communication of research results over the World Wide Web.

The World Wide Web provides a unique opportunity to reconsider how the results of scientific studies can best be presented to clinicians. For decades statisticians, philosophers, medical investigators, and others interested in data analysis have assumed that the Bayesian paradigm is the proper approach for reporting the findings of scientific analyses for use by client computers and readers. At the heart of that approach is the inclusion of the reader's preexisting knowledge and beliefs. Yet, to date, the methods for inclusion have been too complicated for non-statisticians to use. We believe that the World Wide Web provides an ideal environment for putting the Bayesian paradigm into practice: the author publishes the data from the server side, the reader uses the client to represent her or his prior belief, a downloaded program (a Java applet) combines the two. This article describes a prototype implementation for two-arm clinical trials with normally distributed outcomes.

Bayes Theorem↗
PubMed2001

Bayesian technique for investigating linearity in event-related BOLD fMRI.

Event-related BOLD fMRI data is modeled as a linear time-invariant system. Together with Bayesian inference techniques, a statistical test is developed for rigorously detecting linearity/nonlinearity in the BOLD response system. The test is applied to data collected from eight subjects using an event-related paradigm with a switching checkerboard as the visual stimulus. Analyzed as a group, the results clearly find the response to be nonlinear. When each subject is analyzed individually, however, the results are predominantly nonlinear, but there is some evidence to suggest that there may be a crossover from a linear to a nonlinear regime and vice versa. This could be important when estimating physiological parameters for individuals. Additionally, estimates of the hemodynamic response function and corresponding response were obtained, but there was no consistent appearance of a poststimulus undershoot in the event-related BOLD response.

Adult↗
PubMed1996

Imaging the traction stresses exerted by locomoting cells with the elastic substratum method.

The cytoskeletal activity of motile or adherent cells is frequently seen to induce detectable displacements of sufficiently compliant substrata. The physics of this phenomenon is discussed in terms of the classical theory of small-strain, plane-stress elasticity. The main results of such analysis is a transform expressing the displacement field of the elastic substrate as an integral over the traction field. The existence of this transform is used to derive a Bayesian method for converting noisy measurements of substratum displacement into "images" of the actual traction forces exerted by adherent or locomoting cells. Finally, the Monte Carlo validation of the statistical method is discussed, some new rheological studies of films are presented, and a practical application is given.

Animals↗
PubMed2004

Software reliability prediction using recurrent neural network with Bayesian regularization.

A recurrent neural network modeling approach for software reliability prediction with respect to cumulative failure time is proposed. Our proposed network structure has the capability of learning and recognizing the inherent internal temporal property of cumulative failure time sequence. Further, by adding a penalty term of sum of network connection weights, Bayesian regularization is applied to our network training scheme to improve the generalization capability and lower the susceptibility of overfitting. The performance of our proposed approach has been tested using four real-time control and flight dynamic application data sets. Numerical results show that our proposed approach is robust across different software projects, and has a better performance with respect to both goodness-of-fit and next-step-predictability compared to existing neural network models for failure time prediction.

Algorithms↗
PubMed2004

Interpreting posterior relative risk estimates in disease-mapping studies.

There is currently much interest in conducting spatial analyses of health outcomes at the small-area scale. This requires sophisticated statistical techniques, usually involving Bayesian models, to smooth the underlying risk estimates because the data are typically sparse. However, questions have been raised about the performance of these models for recovering the "true" risk surface, about the influence of the prior structure specified, and about the amount of smoothing of the risks that is actually performed. We describe a comprehensive simulation study designed to address these questions. Our results show that Bayesian disease-mapping models are essentially conservative, with high specificity even in situations with very sparse data but low sensitivity if the raised-risk areas have only a moderate (less than 2-fold) excess or are not based on substantial expected counts (> 50 per area). Semiparametric spatial mixture models typically produce less smoothing than their conditional autoregressive counterpart when there is sufficient information in the data (moderate-size expected count and/or high true excess risk). Sensitivity may be improved by exploiting the whole posterior distribution to try to detect true raised-risk areas rather than just reporting and mapping the mean posterior relative risk. For the widely used conditional autoregressive model, we show that a decision rule based on computing the probability that the relative risk is above 1 with a cutoff between 70 and 80% gives a specific rule with reasonable sensitivity for a range of scenarios having moderate expected counts (approximately 20) and excess risks (approximately 1.5- to 2-fold). Larger (3-fold) excess risks are detected almost certainly using this rule, even when based on small expected counts, although the mean of the posterior distribution is typically smoothed to about half the true value.

Bayes Theorem↗
PubMed2005

Predicting hospital admission for Emergency Department patients using a Bayesian network.

Hospital admission delays in the Emergency Department (ED) reduce volume capacity and contribute to the nation's ED diversion problem. This study evaluated the accuracy of a Bayesian network for the early prediction of hospital admission status using data from 16,900 ED encounters. The final model included nine variables that are commonly available in many ED settings. The area under the receiver operating characteristic curve was 0.894 (95% CI: 0.887-0.902) for the validation set. The system had high accuracy an may be used to alert clinicians to initiate admission processes earlier during a patient's ED encounter.

Area Under Curve↗
PubMed2006

The contribution of nursing data to the development of a predictive model for the detection of acute pancreatitis.

The increasing use of information system has resulted in the accumulation of a large volume of nursing data in electronic medical records. These data have great potential for supporting the various clinical decisions made by physicians, nurses, and managers. However, how to re-use of nursing data remains largely an issue of informatics. The aim of this study was to demonstrate how these nursing data can be used and how much they could contribute to developing a predictive model for an expert system for early detection of acute pancreatitis. We employed a probability-based model consisting of a Bayesian network and trained this model with the patient data retrospectively retrieved from the enterprise data warehouse of a tertiary hospital. The performance of the predictive model was measured based on the error rate and the area under receiver operating characteristics curve, which were 13.89 % and 0.93, respectively. The sensitivity of the acute pancreatitis to the findings from each nursing data was measured using a test of sensitivity. The results showed that the role of nursing data is as important as laboratory data in formulating a model for an expert system.

Acute Disease↗
PubMed2006

Using satellite imagery for stormwater pollution management with Bayesian networks.

Urban stormwater runoff is the primary source of many pollutants to Santa Monica Bay, but its monitoring and modeling is inherently difficult and often requires land use information as an intermediate process. Many approaches have been developed to estimate stormwater pollutant loading from land use. This research investigates an alternative approach, which estimates stormwater pollutant loadings directly from satellite imagery. We proposed a Bayesian network approach to classify a Landsat ETM(+) image of the Marina del Rey area in the Santa Monica Bay watershed. Eight water quality parameters were examined, including: total suspended solids, chemical oxygen demand, nutrients, heavy metals, and oil and grease. The pollutant loads for each parameter were classified into six levels: very low, low, medium low, medium high, high, and very high. The results provided spatial estimates of each pollutant load as thematic maps from which the greatest pollutant loading areas were identified. These results may be useful in developing best management strategies for stormwater pollution at regional and global scales and in establishing total maximum daily loads in the watershed. The approach can also be used for areas without ground-survey land use data.

Bayes Theorem↗
PubMed1975

The impact of laboratory error on the normal range: a Bayesian model.

Interpretation of clinical laboratory results, aside from clinical considerations, is based on the probability of the result being within a given normal range. This probability is influenced by the degree of error inherent in the analytical method. It would be advantageous to assign a more definite probability to the result of the measurement by combining the error distribution of the result around the true value and the distribution of the healthy population that serves as a reference. Bayesian statistics permits the revision of this prior information into a single probability.

Calcium↗
PubMed2005

Bayesian analysis of hierarchical pattern-mixture models for clinical trials data with attrition and comparisons to commonly used ad-hoc and model-based approaches.

This article addresses the problem of making scientifically sound inferences from clinical trials data with attrition, when reasons of attrition seem related to the outcomes of interest. The problem is particularly difficult when the effect of a covariate is to be estimated and if the dropout mechanism appears to operate differently at different levels of the covariate. In this context, multiple imputation under a multilevel pattern-mixture model that allows random variation across dropout groups is presented. Using simulated data generated around an alcoholic hepatitis trial, we compare the performance of this model and commonly used ad-hoc and model-based approaches.

Algorithms↗
PubMed2006

The accuracy and reliability of perceived depth from linear perspective as a function of image size.

We investigated the ability to use linear perspective to perceive depth from monocular images. Specifically, we focused on the information provided by convergence of parallel lines in an image due to perspective projection. Our stimuli were trapezoid-shaped projected contours, which appear as rectangles slanted in depth. If converging edges of a contour are assumed to be parallel edges of a 3D object, then it is possible in principle to recover its 3D orientation and relative dimensions. This 3D interpretation depends on projected size; hence, if an image contour were scaled, accurate use of perspective predicts changes in perceived slant and shape. We tested this prediction and measured the accuracy and precision with which observers can judge depth from perspective alone. Observers viewed monocular images of slanted rectangles and judged whether the rectangles appeared longer versus wider than a square. The projected contours had varying widths (7, 14, or 21 deg) and side angles (7 or 25 deg), and heights were varied by a staircase procedure to compute a point of subjective equality and 75% threshold for each condition. Observers were able to reliably judge aspect ratios from the monocular images: Weber fractions were 6-9% for the largest rectangles, increasing to as high as 17% for small rectangles with high simulated slant. Overall, the contours judged to be squares were taller than the projections of actual squares, consistent with perceptual underestimation of depth. Judgments were modulated by image size in the direction expected from perspective geometry, but the effect of size was only about 20-30% of what was predicted. We simulated the performance of a Bayesian ideal observer that integrated perspective information with an a priori bias toward compression of depth and which was able to qualitatively model the pattern of results.

Cues↗
PubMed2004

Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data.

We propose a dynamic Bayesian network and nonparametric regression model for constructing a gene network from time series microarray gene expression data. The proposed method can overcome a shortcoming of the Bayesian network model in the sense of the construction of cyclic regulations. The proposed method can analyze the microarray data as a continuous data and can capture even nonlinear relations among genes. It can be expected that this model will give a deeper insight into complicated biological systems. We also derive a new criterion for evaluating an estimated network from Bayes approach. We conduct Monte Carlo experiments to examine the effectiveness of the proposed method. We also demonstrate the proposed method through the analysis of the Saccharomyces cerevisiae gene expression data.

Bayes Theorem↗
PubMed1999

Computational analysis of disparity modulation sensitivity: an explanation in terms of a Bayesian surface reconstruction.

Sensitivity to binocular disparity modulation has been shown to have a bandpass nature. This paper presents a computational account for the disparity modulation function (DMF) in terms of a Bayesian surface reconstruction. The Bayesian approach suggests that prior assumptions about surface structure will affect the perception of disparity modulation. Taking into account a prior constraint of surface smoothness being imposed on the perceived surfaces, we propose that computing the first derivatives of the surfaces determines the bandpass shape of the DMF. Based on this idea, we derive an analytical prediction of the DMF. It is then shown that the prediction gives a good fit to the empirical data. Implications for possible mechanisms underlying the DMF are discussed.

Depth Perception↗