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At least 1,297 records · Page 72Linked to original sources
PubMed2006

Statistical atlas of acute stroke from magnetic resonance diffusion-weighted-images of the brain.

This study presents a method for computing a probabilistic atlas that describes the spatial distributions of acute infarcts of the brain. The data consisted of diffusion-weighted-images (DWI) and high-resolution T1-weighted MR images representing 22 studies from 22 subjects. All DWI data sets contained high-intensity lesions on B-1000 maps, known from clinical history to be related to acute stroke. To compute the atlas, manually segmented infarcts on original DWI were spatially transformed and registered to a common coordinate system. This coordinate system allowed combining all lesions into a statistical atlas in the model space. As a result, the computed probabilistic map showed mild left-sided predominance of brain infarcts, which likely represents asymmetry in eloquence of brain regions. In our opinion, the statistical atlas of acute brain infarcts can facilitate computer-based detection of stroke in large image data sets.

Brain↗
PubMed2002

Mathematical modelling of host-parasitoid systems: effects of chemically mediated parasitoid foraging strategies on within- and between-generation spatio-temporal dynamics.

In this paper we develop a novel discrete, individual-based mathematical model to investigate the effect of parasitoid foraging strategies on the spatial and temporal dynamics of host-parasitoid systems. The model is used to compare naïve or random search strategies with search strategies that depend on experience and sensitivity to semiochemicals in the environment. It focuses on simple mechanistic interactions between individual hosts, parasitoids, and an underlying field of a volatile semiochemical (emitted by the hosts during feeding) which acts as a chemoattractant for the parasitoids. The model addresses movement at different spatial scales, where scale of movement also depends on the internal state of an individual. Individual interactions between hosts and parasitoids are modelled at a discrete (micro-scale) level using probabilistic rules. The resulting within-generation dynamics produced by these interactions are then used to generate the population levels for successive generations. The model simulations examine the effect of various key parameters of the model on (i) the spatio-temporal patterns of hosts and parasitoids within generations; (ii) the population levels of the hosts and parasitoids between generations. Key results of the model simulations show that the following model parameters have an important effect on either the development of patchiness within generations or the stability/instability of the population levels between generations: (i) the rate of diffusion of the kairomones; (ii) the specific search strategy adopted by the parasitoids; (iii) the rate of host increase between successive generations. Finally, evolutionary aspects concerning competition between several parasitoid subpopulations adopting different search strategies are also examined.

Animals↗
PubMed1999

An empirically based model of the electrically evoked compound action potential.

The relationship between electrically evoked single-fiber action potentials and the electrically evoked compound action potential of the auditory nerve is of interest to those attempting to model such responses with computational techniques. It also relates to efforts to exploit the gross potentials that can now be recorded by some implantable cochlear prostheses. In this paper, we develop a computational model of the auditory nerve response to single, pulsatile, electrical stimuli based upon the response characteristics obtained from 230 single fibers of 13 cats. These fibers were stimulated by brief (39s) monophasic cathodic stimuli delivered by a monopolar intracochlear electrode. The data were pooled to obtain an estimate of the distribution of fiber thresholds. Post-stimulus time histograms were modeled using Poisson functions and adjusted to account for empirically determined latency and jitter characteristics. The probabilistic nature of single-fiber input-output functions (i.e. Verveen's (1961) 'relative spread') was also modelled. PST histograms from 5000 modelled fibers were then summed and convolved with an estimated 'unit potential' following the method of Goldstein and Kiang (1958). This convolution produced modelled compound action potentials, which were then compared with experimentally obtained data. Manipulations of model parameters affecting threshold, jitter, and relative spread suggest that the most important determinant of the shape of the EAP amplitude-level function is the threshold distribution. A model based solely on threshold distribution produces an EAP input-output function similar to one that accounts for probabilistic single-fiber input-output functions. Discrepancies between these two models do occur if the threshold distribution function is compressed significantly, as might be the case in pathological cochleae with altered distributions or numbers of nerve fibers.

Algorithms↗
PubMed1995

Probabilistic risk assessment of nephrotoxic effect of uranium in drinking water.

Assessments of the health effects of pollutants generally use models in which exposure variables and model parameters are point values, often chosen as conservative estimates. A more realistic approach is to characterize the uncertainty of each variable and parameter explicitly as a probability distribution. This paper presents a probabilistic version of an established model for metabolism of ingested uranium and characterizes the parameters of the model as distributions. It then characterizes the uncertainty of the threshold level for nephrotoxic risk of uranium, and uses distributions of the heterogeneity in drinking water intake of uranium in the U.S. to assess nephrotoxic risk. It further evaluates the implications of inadequate data on the heterogeneity of dietary intake.

Humans↗
PubMed2001

Computational expansion of genetic networks.

We present a new methodology for computational analysis of gene and protein networks. The aim is to generate new educated hypotheses on gene functions and on the logic of the biological network circuitry, based on gene expression profiles. The framework supports the incorporation of biologically motivated network constraints and rules to improve specificity. Since current data is insufficient for de-novo reconstruction, the method receives as input a known pathway core and suggests likely expansions to it. Network modeling is combinatorial, yet data can be probabilistic. At the heart of the approach are a fitness function which estimates the quality of suggested network expansions given the core and the data, and a specificity measure of the expansions. The approach has been implemented in an interactive software tool called GENESYS. We report encouraging results in preliminary analysis of yeast ergosterol pathway based on transcription profiles. In particular, the analysis suggests a novel ergosterol transcription factor.

Algorithms↗
PubMedDate not supplied

Representation and analysis of medical decision problems with influence diagrams.

Influence diagrams are a powerful graphic representation for decision models, complementary to decision trees. Influence diagrams and decision trees are different graphic representations for the same underlying mathematical model and operations. This article describes the elements of an influence diagram, and shows several familiar decision problems represented as decision trees and as influence diagrams. The authors also contrast the information highlighted in each graphic representation, demonstrate how to calculate the expected utilities of decision alternatives modeled with an influence diagram, provide an overview of the conceptual basis of the solution algorithms that have been developed for influence diagrams, discuss the strengths and limitations of influence diagrams relative to decision trees, and describe the mathematical operations that are used to evaluate both decision trees and influence diagrams. They use clinical examples to illustrate the mathematical operations of the influence-diagram-evaluation algorithm; these operations are arc reversal, chance node removal by averaging, and decision node removal by policy determination. Influence diagrams may be helpful when problems have a high degree of conditional independence, when large models are needed, when communication of the probabilistic relationships is important, or when the analysis requires extensive Bayesian updating. The choice of graphic representation should be governed by convenience, and will depend on the problem being analyzed, on the experience of the analyst, and on the background of the consumers of the analysis.

AIDS-Related Opportunistic Infections↗
PubMed2006

Forced and unforced ocean temperature changes in Atlantic and Pacific tropical cyclogenesis regions.

Previous research has identified links between changes in sea surface temperature (SST) and hurricane intensity. We use climate models to study the possible causes of SST changes in Atlantic and Pacific tropical cyclogenesis regions. The observed SST increases in these regions range from 0.32 degrees C to 0.67 degrees C over the 20th century. The 22 climate models examined here suggest that century-timescale SST changes of this magnitude cannot be explained solely by unforced variability of the climate system. We employ model simulations of natural internal variability to make probabilistic estimates of the contribution of external forcing to observed SST changes. For the period 1906-2005, we find an 84% chance that external forcing explains at least 67% of observed SST increases in the two tropical cyclogenesis regions. Model "20th-century" simulations, with external forcing by combined anthropogenic and natural factors, are generally capable of replicating observed SST increases. In experiments in which forcing factors are varied individually rather than jointly, human-caused changes in greenhouse gases are the main driver of the 20th-century SST increases in both tropical cyclogenesis regions.

Atlantic Ocean↗
PubMed2001

Scaling theory and spreading dynamics in systems with one absorbing state derived from an equilibrium statistical model.

We show that for systems with one absorbing state, the widely assumed scaling properties of the survival probability and of the probability density of the size of activity avalanches cannot be true in the asymptotic limit. Trying to answer the question, what is the true asymptotic limit of these quantities, we study Domany-Kinzel probabilistic cellular automata using an equilibrium statistical mechanic model (ESM). We are able to express important quantities of the avalanche dynamics by correlation functions of the ESM. The application of scaling theory to the ESM allows for the derivation of the scaling properties of quantities of the avalanche dynamics in the form of infinite series. From these results we can obtain possible solutions for the apparent scaling problem, but cannot decide definitely which one is true. The most appealing solution, for which some evidence is given, states that there is a narrow range around the critical point in which, for example, the survival probability has the same power-law behavior as on the critical point. Outside this narrow range, the usually assumed scaling should be approximately valid.

Journal Article↗
PubMed2004

Insulator dynamics and the setting of chromatin domains.

The early discovery of cis-regulatory elements able to promote transcription of genes over large distances led to the postulate that elements, termed insulators, should also exist that would limit the action of enhancers, LCRs and silencers to defined domains. Such insulators were indeed found during the past fifteen years in a wide range of organisms, from yeast to humans. Recent advances point to an important role of transcription factors in insulator activity and demonstrate that the operational observation of an insulator effect relies on a delicate balance between the "efficiency" of the insulator and that of the element to be counteracted. In addition, genuine insulator elements now appear less common than initially envisaged, and they are only found at loci displaying a high density of coding or regulatory information. Where this is not the case, chromatin domains of opposing properties are thought to confront each other at "fuzzy" boundaries. In this article, we propose models for both fixed and fuzzy boundaries that incorporate probabilistic and dynamic parameters.

Animals↗
PubMed2003

Probabilistic framework for the estimation of the adult and child toxicokinetic intraspecies uncertainty factors.

Human health risk assessments use point values to develop risk estimates and thus impart a deterministic character to risk, which, by definition, is a probability phenomenon. The risk estimates are calculated based on individuals and then, using uncertainty factors (UFs), are extrapolated to the population that is characterized by variability. Regulatory agencies have recommended the quantification of the impact of variability in risk assessments through the application of probabilistic methods. In the present study, a framework that deals with the quantitative analysis of uncertainty (U) and variability (V) in target tissue dose in the population was developed by applying probabilistic analysis to physiologically-based toxicokinetic models. The mechanistic parameters that determine kinetics were described with probability density functions (PDFs). Since each PDF depicts the frequency of occurrence of all expected values of each parameter in the population, the combined effects of multiple sources of U/V were accounted for in the estimated distribution of tissue dose in the population, and a unified (adult and child) intraspecies toxicokinetic uncertainty factor UFH-TK was determined. The results show that the proposed framework accounts effectively for U/V in population toxicokinetics. The ratio of the 95th percentile to the 50th percentile of the annual average concentration of the chemical at the target tissue organ (i.e., the UFH-TK) varies with age. The ratio is equivalent to a unified intraspecies toxicokinetic UF, and it is one of the UFs by which the NOAEL can be divided to obtain the RfC/RfD. The 10-fold intraspecies UF is intended to account for uncertainty and variability in toxicokinetics (3.2x) and toxicodynamics (3.2x). This article deals exclusively with toxicokinetic component of UF. The framework provides an alternative to the default methodology and is advantageous in that the evaluation of toxicokinetic variability is based on the distribution of the effective target tissue dose, rather than applied dose. It allows for the replacement of the default adult and children intraspecies UF with toxicokinetic data-derived values and provides accurate chemical-specific estimates for their magnitude. It shows that proper application of probability and toxicokinetic theories can reduce uncertainties when establishing exposure limits for specific compounds and provide better assurance that established limits are adequately protective. It contributes to the development of a probabilistic noncancer risk assessment framework and will ultimately lead to the unification of cancer and noncancer risk assessment methodologies.

Adolescent↗
PubMed2003

Model uncertainty and choices made by modelers: lessons learned from the International Atomic Energy Agency model intercomparisons.

The treatment of uncertainties associated with modeling and risk assessment has recently attracted significant attention. The methodology and guidance for dealing with parameter uncertainty have been fairly well developed and quantitative tools such as Monte Carlo modeling are often recommended. However, the issue of model uncertainty is still rarely addressed in practical applications of risk assessment. The use of several alternative models to derive a range of model outputs or risks is one of a few available techniques. This article addresses the often-overlooked issue of what we call "modeler uncertainty," i.e., difference in problem formulation, model implementation, and parameter selection originating from subjective interpretation of the problem at hand. This study uses results from the Fruit Working Group, which was created under the International Atomic Energy Agency (IAEA) BIOMASS program (BIOsphere Modeling and ASSessment). Model-model and model-data intercomparisons reviewed in this study were conducted by the working group for a total of three different scenarios. The greatest uncertainty was found to result from modelers' interpretation of scenarios and approximations made by modelers. In scenarios that were unclear for modelers, the initial differences in model predictions were as high as seven orders of magnitude. Only after several meetings and discussions about specific assumptions did the differences in predictions by various models merge. Our study shows that parameter uncertainty (as evaluated by a probabilistic Monte Carlo assessment) may have contributed over one order of magnitude to the overall modeling uncertainty. The final model predictions ranged between one and three orders of magnitude, depending on the specific scenario. This study illustrates the importance of problem formulation and implementation of an analytic-deliberative process in risk characterization.

Journal Article↗
PubMedDate not supplied

Probabilistic analysis of decision trees using Monte Carlo simulation.

The authors describe methods for modeling uncertainty in the specification of decision tree probabilities and utilities using Monte Carlo simulation techniques. Exact confidence levels based upon the underlying probabilistic structure are provided. Probabilistic measures of sensitivity are derived in terms of classical information theory. These measures identify which variables are probabilistically important components of the decision. These techniques are illustrated in terms of the clinical problem of anticoagulation versus observation in the setting of deep vein thrombosis during the first trimester of pregnancy. These methods provide the decision analyst with powerful yet simple tools which give quantitative insight into the structure and inherent limitations of decision models arising from specification uncertainty. The techniques may be applied to complex decision models.

Adult↗
PubMed2005

Inland and coastal flooding: developments in prediction and prevention.

We review the scientific and engineering understanding of various types of inland and coastal flooding by considering the different causes and dynamic processes involved, especially in extreme events. Clear progress has been made in the accuracy of numerical modelling of meteorological causes of floods, hydraulics of flood water movement and coastal wind-wave-surge. Probabilistic estimates from ensemble predictions and the simultaneous use of several models are recent techniques in meteorological prediction that could be considered for hydraulic and oceanographic modelling. The contribution of remotely sensed data from aircraft and satellites is also considered. The need to compare and combine statistical and computational modelling methodologies for long range forecasts and extreme events is emphasized, because this has become possible with the aid of kilometre scale computations and network grid facilities to simulate and analyse time-series and extreme events. It is noted that despite the adverse effects of climatic trends on flooding, appropriate planning of rapidly growing urban areas could mitigate some of the worst effects. However, resources for flood prevention, including research, have to be considered in relation to those for other natural disasters. Policies have to be relevant to the differing geology, meteorology and cultures of the countries affected.

Computer Simulation↗
PubMed2006

Semi-supervised analysis of gene expression profiles for lineage-specific development in the Caenorhabditis elegans embryo.

MOTIVATION: Gene expression profiling is a powerful approach to identify genes that may be involved in a specific biological process on a global scale. For example, gene expression profiling of mutant animals that lack or contain an excess of certain cell types is a common way to identify genes that are important for the development and maintenance of given cell types. However, it is difficult for traditional computational methods, including unsupervised and supervised learning methods, to detect relevant genes from a large collection of expression profiles with high sensitivity and specificity. Unsupervised methods group similar gene expressions together while ignoring important prior biological knowledge. Supervised methods utilize training data from prior biological knowledge to classify gene expression. However, for many biological problems, little prior knowledge is available, which limits the prediction performance of most supervised methods. RESULTS: We present a Bayesian semi-supervised learning method, called BGEN, that improves upon supervised and unsupervised methods by both capturing relevant expression profiles and using prior biological knowledge from literature and experimental validation. Unlike currently available semi-supervised learning methods, this new method trains a kernel classifier based on labeled and unlabeled gene expression examples. The semi-supervised trained classifier can then be used to efficiently classify the remaining genes in the dataset. Moreover, we model the confidence of microarray probes and probabilistically combine multiple probe predictions into gene predictions. We apply BGEN to identify genes involved in the development of a specific cell lineage in the C. elegans embryo, and to further identify the tissues in which these genes are enriched. Compared to K-means clustering and SVM classification, BGEN achieves higher sensitivity and specificity. We confirm certain predictions by biological experiments. AVAILABILITY: The results are available at http://www.csail.mit.edu/~alanqi/projects/BGEN.html.

Algorithms↗
PubMed2002

Context-tree modeling of observed symbolic dynamics.

Modern techniques invented for data compression provide efficient automated algorithms for the modeling of the observed symbolic dynamics. We demonstrate the relationship between coding and modeling, motivating the well-known minimum description length (MDL) principle, and give concrete demonstrations of the "context-tree weighting" and "context-tree maximizing" algorithms. The predictive modeling technique obviates many of the technical difficulties traditionally associated with the correct MDL analyses. These symbolic models, representing the symbol generating process as a finite-state automaton with probabilistic emission probabilities, provide excellent and reliable entropy estimations. The resimulations of estimated tree models satisfying the MDL model-selection criterion are faithful to the original in a number of measures. The modeling suggests that the automated context-tree model construction could replace fixed-order word lengths in many traditional forms of empirical symbolic analysis of the data. We provide an explicit pseudocode for implementation of the context-tree weighting and maximizing algorithms, as well as for the conversion to an equivalent Markov chain.

Journal Article↗
PubMed2006

Cost-effectiveness of self-managed versus physician-managed oral anticoagulation therapy.

BACKGROUND: Patient self-management of long-term oral anticoagulation therapy is an effective strategy in a number of clinical situations, but it is currently not a funded option in the Canadian health care system. We sought to compare the incremental cost and health benefits of self-management with those of physician management from the perspective of the Canadian health care payer over a 5-year period. METHODS: We developed a Bayesian Markov model comparing the costs and quality-adjusted life years (QALYs) accrued to patients receiving oral anticoagulation therapy through self-management or physician management for atrial fibrillation or for a mechanical heart valve. Five health states were defined: no events, minor hemorrhagic events, major hemorrhagic events, thrombotic events and death. Data from published literature were used for transition probabilities. Canadian 2003 costs were used, and utility estimates were obtained from various published sources. RESULTS: Self-management resulted in 3.50 fewer thrombotic events, 0.78 fewer major hemorrhagic events and 0.12 fewer deaths per 100 patients than physician management. The average discounted incremental cost of self-management over physician management was found to be 989 dollars (95% confidence interval [CI] 310 dollars-1655 dollars) per patient and the incremental QALYs gained was 0.07 (95% CI 0.06-0.08). The cost-effectiveness of self-management was 14,129 dollars per QALY gained. There was a 95% chance that self-management would be cost-effective at a willingness to pay of 23,800 dollars per QALY. Results were robust in probabilistic and deterministic sensitivity analyses. INTERPRETATION: This model suggests that self-management is a cost-effective strategy for those receiving long-term oral anticoagulation therapy for atrial fibrillation or for a mechanical heart valve.

Administration, Oral↗
PubMed2000

Handling uncertainty in cost-effectiveness models.

The use of modelling in economic evaluation is widespread, and it most often involves synthesising data from a number of sources. However, even when economic evaluations are conducted alongside clinical trials, some form of modelling is usually essential. The aim of this article is to review the handling of uncertainty in the cost-effectiveness results that are generated by the use of decision-analytic-type modelling. The modelling process is split into a number of stages: (i) a set of methods to be employed in a study are defined, which should include a 'reference case' of agreed methods to enhance the comparability of results; (ii) the clinical and demographic characteristics of the patients the model relates to should be specified as carefully as in any experimental study; and (iii) the data requirements of the model should be estimated using the principles of Bayesian statistics, such that prior distributions are specified for unknown model parameters. Monte Carlo simulation can then be employed to sample from these prior distributions to obtain a distribution of the cost effectiveness of the intervention. Such probabilistic analyses are related to parameter uncertainty. In addition, modelling uncertainty is likely to add a further layer of uncertainty to the results of particular analyses.

Cost-Benefit Analysis↗
PubMed2007

A response-time approach to comparing generalized rational and take-the-best models of decision making.

The authors develop and test generalized versions of take-the-best (TTB) and rational (RAT) models of multiattribute paired-comparison inference. The generalized models make allowances for subjective attribute weighting, probabilistic orders of attribute inspection, and noisy decision making. A key new test involves a response-time (RT) approach. TTB predicts that RT is determined solely by the expected time required to locate the 1st discriminating attribute, whereas RAT predicts that RT is determined by the difference in summed evidence between the 2 alternatives. Critical test pairs are used that partially decouple these 2 factors. Under conditions in which ideal observer TTB and RAT strategies yield equivalent decisions, both the RT results and the estimated attribute weights suggest that the vast majority of subjects adopted the generalized TTB strategy. The RT approach is also validated in an experimental condition in which use of a RAT strategy is essentially forced upon subjects.

Decision Making↗