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PubMed2001

Application of a gamma model of absorption to oral cyclosporin.

BACKGROUND: Some drugs, such as cyclosporin, exhibit flat and delayed absorption profiles, with a correlation between the delay and the peak width. Such profiles can be described by an absorption model in which the absorption rate is derived from a gamma distribution (of which the classical first-order absorption model is a special case). OBJECTIVE: To develop a model for the pharmacokinetics of extravascular administration of cyclosporin and apply it to a study of the pharmacokinetics of cyclosporin microemulsion in stable renal transplant recipients. PATIENTS AND PARTICIPANTS: 21 renal transplant patients receiving oral cyclosporin microemulsion 75 to 175 mg twice daily. METHODS: The equation of the plasma concentration-time curve after oral administration was expressed as a convolution product between the absorption rate and a multi-exponential impulse response. The convolution integral was computed analytically and expressed in terms of the incomplete gamma function. Cyclosporin was assayed by liquid chromatography/mass spectrophotometry. The model was fitted by nonlinear regression, using a specially developed program. RESULTS: The gamma model yielded a good fit in all of the 21 patients studied. Attempts to fit the same data by a classical exponential with lag-time model failed in most patients. CONCLUSIONS: This model could simplify the Bayesian monitoring of cyclosporin therapy.

Area Under Curve↗
PubMed1991

C. R. Henderson: contributions to predicting genetic merit.

The contributions of C. R. Henderson to the genetic evaluation of livestock have been widely accepted, utilized, and enhanced by animal breeders and statisticians. Not well known are the possible alternatives to BLUP that have been suggested by Henderson, such as biased predictors and Bayesian methodology to incorporate prior information. A search for rapid methods of inverting dominance and additive by additive genetic relationship matrices has taken place since Henderson first described his method for computing the inverse of the additive genetic relationship matrix. Accounting for inbreeding and selected base populations continues to be a problem. There is a need to derive accurate descriptions of selection processes and the appropriate selection model in order to solve the problems of cow culling, nonrandom association of sires with herd-year-seasons, and preferential treatment. Henderson solved many animal breeding problems and left hints for solving others, but there are still many difficult problems to be tackled that will have to be resolved without the availability of his generous advice.

Animals↗
PubMed1996

A comparison of three techniques for rapid model development: an application in patient risk-stratification.

Accurately risk-stratifying patients is a key component of health care outcomes assessment. And, many health care organizations increasingly are relying upon automated means for assistance in making patient risk-stratification decisions. Unfortunately, the process of outcome model development, as it is currently practiced, is both time consuming and difficult. We investigated the relative abilities of three modeling techniques (logistic regression, artificial neural network (ANN), and Bayesian) to rapidly develop models for risk-stratifying patients. Our results demonstrated that all three modeling techniques perform equally well in certain situations. However, the Bayesian model with conditional independence had the best overall performance. Unfortunately, none of the models were able to achieve the degree of accuracy which would be required in a medical setting.

APACHE↗
PubMed2005

An image model and segmentation algorithm for reflectance confocal images of in vivo cervical tissue.

The automatic segmentation of nuclei in confocal reflectance images of cervical tissue is an important goal toward developing less expensive cervical precancer detection methods. Since in vivo confocal reflectance microscopy is an emerging technology for cancer detection, no prior work has been reported on the automatic segmentation of in vivo confocal reflectance images. However, prior work has shown that nuclear size and nuclear-to-cytoplasmic ratio can determine the presence or extent of cervical precancer. Thus, segmenting nuclei in confocal images will aid in cervical precancer detection. Successful segmentation of images of any type can be significantly enhanced by the introduction of accurate image models. To enable a deeper understanding of confocal reflectance microscopy images of cervical tissue, and to supply a basis for parameter selection in a classification algorithm, we have developed a model that accounts for the properties of the imaging system and of the tissues. Using our model in conjunction with a powerful image enhancement tool (anisotropic median-diffusion), appropriate statistical image modeling of spatial interactions (Gaussian Markov random fields), and a Bayesian framework for classification-segmentation, we have developed an effective algorithm for automatically segmenting nuclei in confocal images of cervical tissue. We have applied our algorithm to an extensive set of cervical images and have found that it detects 90% of hand-segmented nuclei with an average of 6 false positives per frame.

Algorithms↗
PubMed2006

Multiscale detection of localized anomalous structure in aggregate disease incidence data.

We present a modelling framework for detection of potentially anomalous structure in aggregate spatial disease incidence data in a manner sensitive to localization at multiple scales and/or positions. The key technical contribution is the re-casting of the components of a multiscale disease mapping methodology, recently introduced by the authors in an earlier paper, into a form appropriate for hypothesis testing. In particular, we describe how hypotheses of spatially clustered variations in disease incidence may be linked in one-to-one correspondence with collections of hypotheses on the values of certain multiscale parameters associated with a user-defined hierarchy of nested partitions of an overall spatial region. A Bayesian hypothesis testing methodology is developed in the context of a standard Poisson measurement model, over the collection of possible multiscale hypotheses. We discuss the specification of hyper parameters and prior distributions on the space of models. The methodology is illustrated on both simulated and real data.

Cluster Analysis↗
PubMed1997

Excitatory synaptic site heterogeneity during paired pulse plasticity in CA1 pyramidal cells in rat hippocampus in vitro.

1. The properties of individual excitatory synaptic sites onto adult CA1 hippocampal neurons were investigated using paired pulse minimal stimulation and low noise whole-cell recordings. Non-NMDA receptor-mediated synaptic responses were isolated using a pharmacological blockade of NMDA and GABAA receptors. Amongst the twenty-five stationary ensembles there were twelve showing paired pulse potentiation, two showing paired pulse depression and eleven with no significant net change. The signal-to-noise ratio averaged 4.5:1. There was no correlation between the amplitude of the first and second responses after separation of failures: the percentage of failures averaged 33.6% for the conditioning pulse and 31.7% for the test pulse. 2. Site-directed Bayesian statistical analysis was developed to predict the likely number of activated synapses, synaptic response amplitudes, probability of release and intrinsic variation at each individual synaptic site. Extensive simulations showed the usefulness of this model and defined appropriate parameters. These simulations demonstrated only small errors in estimating parameters of data sets with a small number of sites (< 10) and similar characteristics to the physiological data sets. 3. Physiological ensembles showed between one and three synaptic sites, which exhibited a wide range of values for release probability (0.03-0.99), synaptic amplitudes (1.46-16.8 pA; approximately 62% coefficient of variation between sites) and intrinsic variation over time (approximately 36%). Paired pulse plasticity occurred primarily from alterations in the release probabilities but a few ensembles also showed small changes in site amplitude. Initial release probability correlated negatively with the degree of paired pulse potentiation. Whilst it was possible to use simple assumptions regarding site homogeneity (such as required for a binomial process) for 48% (12 out of 25) of the data sets, the Bayesian analysis was necessary to reveal the complex changes and heterogeneity that occurred in the other 52% of the data sets. The Bayesian site analysis robustly indicated the presence of considerable site heterogeneity, significant intrinsic site variation over time and changes in parameters at individual synaptic sites with plasticity.

6-Cyano-7-nitroquinoxaline-2,3-dione↗
PubMed2001

Signal processing in magnetoencephalography.

The subject of this article is detection of brain magnetic fields, or magnetoencephalography (MEG). The brain fields are many orders of magnitude smaller than the environmental magnetic noise and their measurement represent a significant metrological challenge. The only detectors capable of resolving such small fields and at the same time handling the large dynamic range of the environmental noise are superconducting quantum interference devices (or SQUIDs). The SQUIDs are coupled to the brain magnetic fields using combinations of superconducting coils called flux transformers (primary sensors). The environmental noise is attenuated by a combination of shielding, primary sensor geometry, and synthetic methods. One of the most successful synthetic methods for noise elimination is synthetic higher-order gradiometers. How the gradiometers can be synthesized is shown and examples of their noise cancellation effectiveness are given. The MEG signals measured on the scalp surface must be interpreted and converted into information about the distribution of currents within the brain. This task is complicated by the fact that such inversion is nonunique. Additional mathematical simplifications, constraints, or assumptions must be employed to obtain useful source images. Methods for the interpretation of the MEG signals include the popular point current dipole, minimum norm methods, spatial filtering, beamformers, MUSIC, and Bayesian techniques. The use of synthetic aperture magnetometry (a class of beamformers) is illustrated in examples of interictal epileptic spiking and voluntary hand-motor activity.

Brain↗
PubMed1999

Prediction of oculocardiac reflex in strabismus surgery using neural networks.

Successfully predicting an oculocardiac reflex (OCR) is difficult to achieve despite various proposed maneuvers. The aim of this study was to test the models built up by neural networks to predict the occurrence of OCR during strabismus surgery in children. Premedication was not given. Atropine 0.01 mg/kg was medicated just before induction. Induction was performed with fentanyl or ketorolac, followed by propofol. Atracurium or vecuronium was given for intubation. Anesthesia was maintained with O2-N2O with continuous propofol infusion. Chi-square test was performed for induction agents, gender, weight, muscle blockade, repaired muscle, number of repaired muscles, duration of operation to detect any association between the occurrence of OCR and to develop the model of neural networks. The multi-layer perceptron, radial basis function and Bayesian backpropagation network were tested. The occurrence of OCR was significantly associated with gender and repaired muscle (p < 0.05). Gender, repaired muscle and age were considered as input for the multi-layer perceptron, radial basis function and Bayesian backpropagation network. Three neural networks had predicted the same correction rate in the occurrence of OCR as being 87.5% overall among 16 patients' records tested. These models are conceptually different in predicting compared to conventional maneuvers, and have the advantage of testing individually and foretelling the propensity. By comparison neural networks use grouped experiential data and predict OCR by the learning rule. Neural networks require a relatively abundant number of experienced and homogenous patients' records to establish an accurate model. The multi-layer perceptron, radial basis function and Bayesian backpropagation modeling network may be an alternative way, and preferable to vagal tone maneuvers if the associated relationships to the occurrence of OCR are more clearly defined.

Adolescent↗
PubMed1999

Naive Bayesian-based nomogram for prediction of prostate cancer recurrence.

This paper introduces a schema with naive-Bayesian classifier and patient weighting technique to develop a prostate cancer recurrence prediction model from patient data. We propose the graphical presentation of naive-Bayesian classifier with a nomogram, which can be used both for prediction or can provide means to data analysis. The resulting model was experimentally evaluated; the results were favorable both in terms of interpretability and predictive accuracy.

Bayes Theorem↗
PubMed2004

Bayesian analysis of binary prediction tree models for retrospectively sampled outcomes.

Classification tree models are flexible analysis tools which have the ability to evaluate interactions among predictors as well as generate predictions for responses of interest. We describe Bayesian analysis of a specific class of tree models in which binary response data arise from a retrospective case-control design. We are also particularly interested in problems with potentially very many candidate predictors. This scenario is common in studies concerning gene expression data, which is a key motivating example context. Innovations here include the introduction of tree models that explicitly address and incorporate the retrospective design, and the use of nonparametric Bayesian models involving Dirichlet process priors on the distributions of predictor variables. The model specification influences the generation of trees through Bayes' factor based tests of association that determine significant binary partitions of nodes during a process of forward generation of trees. We describe this constructive process and discuss questions of generating and combining multiple trees via Bayesian model averaging for prediction. Additional discussion of parameter selection and sensitivity is given in the context of an example which concerns prediction of breast tumour status utilizing high-dimensional gene expression data; the example demonstrates the exploratory/explanatory uses of such models as well as their primary utility in prediction. Shortcomings of the approach and comparison with alternative tree modelling algorithms are also discussed, as are issues of modelling and computational extensions.

Algorithms↗
PubMed2006

Bayesian method for gene detection and mapping, using a case and control design and DNA pooling.

Association mapping studies aim to determine the genetic basis of a trait. A common experimental design uses a sample of unrelated individuals classified into 2 groups, for example cases and controls. If the trait has a complex genetic basis, consisting of many quantitative trait loci (QTLs), each group needs to be large. Each group must be genotyped at marker loci covering the region of interest; for dense coverage of a large candidate region, or a whole-genome scan, the number of markers will be very large. The total amount of genotyping required for such a study is formidable. A laboratory effort efficient technique called DNA pooling could reduce the amount of genotyping required, but the data generated are less informative and require novel methods for efficient analysis. In this paper, a Bayesian statistical analysis of the classic model of McPeek and Strahs is proposed. In contrast to previous work on this model, I assume that data are collected using DNA pooling, so individual genotypes are not directly observed, and also account for experimental errors. A complete analysis can be performed using analytical integration, a propagation algorithm for a hidden Markov model, and quadrature. The method developed here is both statistically and computationally efficient. It allows simultaneous detection and mapping of a QTL, in a large-scale association mapping study, using data from pooled DNA. The method is shown to perform well on data sets simulated under a realistic coalescent-with-recombination model, and is shown to outperform classical single-point methods. The method is illustrated on data consisting of 27 markers in an 880-kb region around the CYP2D6 gene.

Alleles↗
PubMed2006

Bayesian multioutput feedforward neural networks comparison: a conjugate prior approach.

A Bayesian method for the comparison and selection of multioutput feedforward neural network topology, based on the predictive capability, is proposed. As a measure of the prediction fitness potential, an expected utility criterion is considered which is consistently estimated by a sample-reuse computation. As opposed to classic point-prediction-based cross-validation methods, this expected utility is defined from the logarithmic score of the neural model predictive probability density. It is shown how the advocated choice of a conjugate probability distribution as prior for the parameters of a competing network, allows a consistent approximation of the network posterior predictive density. A comparison of the performances of the proposed method with the performances of usual selection procedures based on classic cross-validation and information-theoretic criteria, is performed first on a simulated case study, and then on a well known food analysis dataset.

Journal Article↗
PubMed2006

Bayesian semiparametric dynamic frailty models for multiple event time data.

Many biomedical studies collect data on times of occurrence for a health event that can occur repeatedly, such as infection, hospitalization, recurrence of disease, or tumor onset. To analyze such data, it is necessary to account for within-subject dependency in the multiple event times. Motivated by data from studies of palpable tumors, this article proposes a dynamic frailty model and Bayesian semiparametric approach to inference. The widely used shared frailty proportional hazards model is generalized to allow subject-specific frailties to change dynamically with age while also accommodating nonproportional hazards. Parametric assumptions on the frailty distribution are avoided by using Dirichlet process priors for a shared frailty and for multiplicative innovations on this frailty. By centering the semiparametric model on a conditionally conjugate dynamic gamma model, we facilitate posterior computation and lack-of-fit assessments of the parametric model. Our proposed method is demonstrated using data from a cancer chemoprevention study.

Algorithms↗
PubMedDate not supplied

Population kinetics and conditional assessment of the optimal dosage regimen using the P-PHARM software package.

The adjustment of individual dosage regimen is an adaptive control process based upon an individual response to a pharmacokinetic model. To attain this objective, it is very helpful to know the characteristics of the population to which the subject belongs, in terms of mean parameters and interindividual variability. Usually the available information consists of incomplete and sparse data. For this reason it is essential to employ a computational methodology based on non-linear mixed-effect procedures in order to obtain a population parameter estimate. A Bayesian methodology can then be applied from the population parameters to the specific data for the individual requiring a dosage adjustment (such data includes drug concentration(s) of the active drug, demographic data, etc). The result of the Bayesian calculation supplies the required individual pharmacokinetic parameters. An optimal dosage regimen can be defined on the basis of therapeutical criteria (concentration ranges) as well as practical constraints such as: the size of available unitary drug dosages, feasible drug intake times, penalties associated with expected concentrations falling outside the therapeutic concentration ranges. In this paper we present the methodology and results obtained using the P-Pharm software tool. P-Pharm implements a non-linear mixed-effect population parameter estimation algorithm based on the EM algorithm. This method allows the inclusion of explicit variables into the calculations, it implements an individual Bayesian parameter estimation procedure and also an algorithm for the conditional assessment of the optimal dosage regimen given a list of practical constraints.

Algorithms↗
PubMed1997

DIAS--the diabetes advisory system: an outline of the system and the evaluation results obtained so far.

The present paper gives a description of the Diabetes Advisory System (DIAS), and the evaluation results obtained so far. DIAS is a decision support system for the management of insulin dependent diabetes. The core of the system is a compartment model of the human carbohydrate metabolism implemented as a causal probabilistic network (CPN or Bayesian network), which gives it the ability to handle the uncertainty, for example, in blood glucose measurements or physiological variations in glucose metabolism. The evaluation results suggest that, at least in our hands, DIAS can generate advice that is safe and of a quality that is at least comparable to what is available from experienced clinicians.

Bayes Theorem↗
PubMed2004

Advances on BYY harmony learning: information theoretic perspective, generalized projection geometry, and independent factor autodetermination.

The nature of Bayesian Ying-Yang harmony learning is reexamined from an information theoretic perspective. Not only its ability for model selection and regularization is explained with new insights, but also discussions are made on its relations and differences from the studies of minimum description length (MDL), Bayesian approach, the bit-back based MDL, Akaike information criterion (AIC), maximum likelihood, information geometry, Helmholtz machines, and variational approximation. Moreover, a generalized projection geometry is introduced for further understanding such a new mechanism. Furthermore, new algorithms are also developed for implementing Gaussian factor analysis (FA) and non-Gaussian factor analysis (NFA) such that selecting appropriate factors is automatically made during parameter learning.

Algorithms↗
PubMed1990

Hypermedia and randomized algorithms for medical expert systems.

KNET is an environment for constructing probabilistic, knowledge-intensive systems within the axiomatic framework of decision theory. The KNET architecture defines a complete separation between the hypermedia user interface on the one hand, and the representation and management of expert opinion on the other. KNET offers a choice of algorithms for probabilistic inference. We and our coworkers have used KNET to build consultation systems for lymph-node pathology, bone-marrow transplantation therapy, clinical epidemiology, and alarm management in the intensive-care unit. Most important, KNET contains a randomized approximation scheme (RAS) for the difficult and almost certainly intractable problem of Bayesian inference. Our algorithm can, in many circumstances, perform efficient approximate inference in large and richly interconnected models of medical diagnosis. In this article, we describe the architecture of KNET, construct a randomized algorithm for probabilistic inference, and analyze the algorithm's performance. Finally, we characterize our algorithms' empiric behavior and explore its potential for parallel speedups. From design to implementation, then, KNET demonstrates the crucial interaction between theoretical computer science and medical informatics.

Algorithms↗
PubMed2005

A comparison of different bivariate correlated frailty models and estimation strategies.

Frailty models are becoming increasingly popular in multivariate survival analysis. Shared frailty models in particular are often used despite their limitations. To overcome their disadvantages numerous correlated frailty models were established during the last decade. In the present study, we examine bivariate correlated frailty models, and especially the behavior of the parameter estimates when using different estimation strategies. We consider three different bivariate frailty models: the gamma model and two versions of the log-normal model. The traditional maximum likelihood procedure of parameter estimation in the gamma case with an explicit available likelihood function is compared with maximum likelihood methods based on numerical integration and a Bayesian approach using MCMC methods with the help of a comprehensive simulation study. We detected a strong dependence between the two parameter estimates (variance and correlation of frailties) in the bivariate correlated frailty model and analyzed this dependence in detail.

Bayes Theorem↗