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Results for “probabilistic modelling”

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PubMed1999

A diagnostic advice system based on pathophysiological models of diseases.

Medical decision-support systems in which uncertainty plays an essential role are increasingly based on the formalism of probabilistic networks. Although this formalism is very powerful, the construction of actual networks is not straightforward, and requires the availability of clearly structured medical domain models as a starting point. In this paper it is argued that medical pathophysiological knowledge constitutes a good start for the development of such models, even though pathophysiological knowledge is semantically different from probabilistic knowledge. Two models concerning anaemia, which are part of a broad system covering the domain of anaemia, are discussed to illustrate the general approach.

Anemia, Pernicious↗
PubMed1999

Hidden neural networks.

A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum likelihood criterion. The HNN can be viewed as an undirected probabilistic independence network (a graphical model), where the neural networks provide a compact representation of the clique functions. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear performance gains compared to standard HMMs tested on the same task.

Databases as Topic↗
PubMed2005

Segmentation of fetal ultrasound images.

This paper describes a new method for segmentation of fetal anatomic structures from echographic images. More specifically, we estimate and measure the contours of the femur and of cranial cross-sections of fetal bodies, which can thus be automatically measured. Contour estimation is formulated as a statistical estimation problem, where both the contour and the observation model parameters are unknown. The observation model (or likelihood function) relates, in probabilistic terms, the observed image with the underlying contour. This likelihood function is derived from a region-based statistical image model. The contour and the observation model parameters are estimated according to the maximum likelihood (ML) criterion, via deterministic iterative algorithms. Experiments reported in the paper, using synthetic and real images, testify for the adequacy and good performance of the proposed approach.

Algorithms↗
PubMed2002

The use of spatial modeling in an aquatic food web to estimate exposure and risk.

This paper quantitatively evaluates interactions among foraging behavior, habitat preferences, site characteristics and the spatial distribution of contaminants in estimating PCB exposure concentrations for winter flounder at a hypothetical open water dredged material disposal site in the coastal waters of New York and New Jersey (NY-NJ). The models implemented in this study include a spatial submodel to account for spatial and temporal characteristics of fish exposure and a probabilistic adaptation of the Gobas bioaccumulation model to account for temporal variation in concentrations of polychlorinated biphenyls (PCBs) in sediment and water. We estimated the geographic distribution of an offshore winter flounder subpopulation based on species biology, including such variables as foraging area, habitat size, disposal site size and migration characteristics. We incorporated these variables together with an estimate of differential attraction to a management site within a spatially explicit model to assess the range of expected PCB exposures to a winter flounder population. The output of this modeling effort, flounder PCB tissue concentrations, provides exposure point concentrations for estimates of human health risk through ingestion of locally caught flounder. The risks obtained for the spatially non-explicit case are as much as one order of magnitude higher than those obtained after incorporating spatial and temporal characteristics of winter flounder foraging and seasonal migration. Incorporating spatial and temporal variables in food chain models can help support sediment management decisions by providing a quantitative expression of the confidence in risk estimates.

Animals↗
PubMed2002

Risk based management of contaminated sediments: consideration of spatial and temporal patterns in exposure modeling.

This paper addresses interactions among foraging behavior, habitat preferences, site characteristics, and spatial distribution of contaminants in developing PCB exposure estimates for winter flounder at a hypothetical open water dredged material disposal site in the coastal waters of New York and New Jersey (NY-NJ). The implications of these interactions for human health risk estimates for local recreational anglers who fish for and eat flounder are described. The models implemented in this study include a spatial submodel to account for spatial and temporal characteristics of fish exposures and a probabilistic adaptation of the Gobas bioaccumulation model that accounts for temporal variation in concentrations of hydrophobic contaminants in sediment and water. We estimated the geographic distribution of a winter flounder subpopulation offshore of NY-NJ based on species biology and its vulnerability to local recreational fishing, the foraging area of individual fish, and their migration patterns. We incorporated these parameters and an estimate of differential attraction to a management site into a spatially explicit model to assess the range of exposures within the population. The output of this modeling effort, flounder PCB tissue concentrations, provided exposure point concentrations for an estimate of human health risk through ingestion of locally caught flounder. The risks obtained for the spatially nonexplicit case are as much as 1 order of magnitude higher than those obtained with explicit consideration of spatial and temporal characteristics of winter flounder foraging and seasonal migration. This practice of "defaulting" to extremely conservative estimates for exposure parameters in the face of uncertainty ill serves the decision-making process for management of contaminated sediments in general and specifically for disposal of dredged materials. Consideration of realistic spatial and temporal scales in food chain models can help support sediment management decisions by providing a quantitative expression of the confidence in risk estimates.

Animals↗
PubMed2001

Evaluation of decay times in coupled spaces: Bayesian parameter estimation.

Determination of sound decay times in coupled spaces often demands considerable effort. Based on Schroeder's backward integration of room impulse responses, it is often difficult to distinguish different portions of multirate sound energy decay functions. A model-based parameter estimation method, using Bayesian probabilistic inference, proves to be a powerful tool for evaluating decay times. A decay model due to one of the authors [N. Xiang, J. Acoust. Soc. Am. 98, 2112-2121 (1995)] is extended to multirate decay functions. Following a summary of Bayesian model-based parameter estimation, the present paper discusses estimates in terms of both synthesized and measured decay functions. No careful estimation of initial values is required, in contrast to gradient-based approaches. The resulting robust algorithmic estimation of more than one decay time, from experimentally measured decay functions, is clearly superior to the existing nonlinear regression approach.

Journal Article↗
PubMed2003

FPGA implementation of a pyramidal Weightless Neural Networks learning system.

A hardware architecture of a Probabilistic Logic Neuron (PLN) is presented. The suggested model facilitates the on-chip learning of pyramidal Weightless Neural Networks using a modified probabilistic search reward/penalty training algorithm. The penalization strategy of the training algorithm depends on a predefined parameter called the probabilistic search interval. A complete Weightless Neural Network (WNN) learning system is modeled and implemented on Xilinx XC4005E Field Programmable Gate Array (FPGA), allowing its architecture to be configurable. Various experiments have been conducted to examine the feasibility and performance of the WNN learning system. Results show that the system has a fast convergence rate and good generalization ability.

Algorithms↗
PubMed2006

Statistical methods for geographical surveillance in veterinary epidemiology.

Spatial clustering and cluster detection are statistical analysis developed to address relevant scientific hypothesis. The difficulty stays in the large number of alternative hypothesis due to the different mechanisms that could generate the anomalous cases aggregation. We review methods for marked point data (case/control) aimed to describe spatial intensity of disease risk, to test for randomness and to locate significant excesses. Bayesian Gaussian Spatial Exponential models are used to illustrate probabilistic aspects and the link with simpler non parametric tools are shown. We develop an informal guideline to the analysis and used data on faecal contamination and dog parasitic diseases in the city of Naples, Italy. Kernel density estimation resulted very sensitive to bandwidth choice and overemphasized localized excess, Ripley'K function and Cuzick-Edwards test were very consistent each other while the SatScan failed to detect excesses. The spatial range was around 600 meters and justifies several small clusters. Bayesian models were very powerful in reconstructing the phenomenon and allow inference on model parameters in good agreement with the non parametric analysis.

Algorithms↗
PubMed2006

A probabilistic arsenic exposure assessment for children who contact CCA-treated playsets and decks, Part 1: Model methodology, variability results, and model evaluation.

Concerns have been raised regarding the safety of young children who may contact arsenic residues while playing on and around chromated copper arsenate (CCA)-treated wood playsets and decks. Although CCA registrants voluntarily canceled the production of treated wood for residential use in 2003, the potential for exposure from existing structures and surrounding soil still poses concerns. The EPA's Office of Research and Development developed and applied the probabilistic Stochastic Human Exposure and Dose Simulation model for wood preservatives (SHEDS-Wood) to estimate children's absorbed dose of arsenic from CCA. Skin contact with, and nondietary ingestion of, arsenic in soil and wood residues were considered for the population of children in the United States who frequently contact CCA-treated wood playsets and decks. Model analyses were conducted to assess the range in population estimates and the impact of potential mitigation strategies such as the use of sealants and hand washing after play events. The results show predicted central values for lifetime annual average daily dose values for arsenic ranging from 10(-6) to 10(-5) mg/kg/day, with predicted 95th percentiles on the order of 10(-5) mg/kg/day. There were several orders of magnitude between lower and upper percentiles. Residue ingestion via hand-to-mouth contact was determined to be the most significant exposure route for most scenarios. Results of several alternative scenarios were similar to baseline results, except for the scenario with greatly reduced residue concentrations through hypothetical wood sealant applications; in this scenario, exposures were lower, and the soil ingestion route dominated. SHEDS-Wood estimates are typically consistent with, or within the range of, other CCA exposure models.

Arsenates↗
PubMed1991

[Medical diagnosis].

The diagnostic process holds a firm position in medical practice, but is often claimed to be part of the "art of medicine", partially beyond reach of rational and logical analysis. Research in clinical cognition, decision analysis and artificial intelligence have, however, elucidated essential parts of medical diagnosis. A characteristic feature of diagnosis is the manner in which uncertainties are handled. Early generation of hypotheses about the nature of the condition present seems to be useful method. Similarly, probabilistic, causal and deterministic reasoning can be illustrated by diagnostic models which have found favor during recent years. A certain type of cognitive process (heuristic) is employed when assessing information of probabilistic nature. The diagnostic models are partial and concern the parts of the process, which may be represented verbally and consciously. This raises the question of how the clinician actually draws upon experience (background knowledge), which preconditions shape the generation of applicable diagnostic hypotheses and how the diagnostic capability of the individual physician can be facilitated.

Clinical Competence↗
PubMedDate not supplied

An attempt to 3D reconstruct vessel morphology from X-ray projections and intravascular ultrasounds modeling and fusion.

The emergency of interventional revascularization techniques in the treatment of atheromateous vascular diseases has resulted in the need for additional valuable diagnostic information about the 3D morphology and nature of the lesion. To overcome the limitations inherent to common vascular imaging techniques, such as Digital Angiography (DA) which only gives partial information on lumen narrowing or Intravascular Ultrasound (IVUS) which provides randomly oriented transversal images, a 3D reconstruction of the vessel by fusion of X-ray and IVUS images has been developed. For that purpose, X-ray and IVUS images are acquired according to a well-defined protocol and useful information to be fused is extracted. A geometric model then leads to the determination of the unknown parameters which allow the alignment of all data in a common reference frame. The registered data are then directly introduced into a probabilistic reconstruction process using a Markovian modeling associated with a simulated annealing-based optimization algorithm. Taking into account all the information available about the vessel, the method avoids the uncertainties and ambiguities of a reconstruction based only on one modality, and the probabilistic fusion solves the possible contradictions between both acquisitions. Results of vascular lumen 3D reconstruction are shown with data acquired on an excised dog aorta. The accuracy of reconstruction of the lumen by data fusion is significantly improved compared to results obtained with separate reconstruction from angiographic or ultrasonic data. Further work will include introduction of vessel wall texture elements into the probabilistic fusion process to increase the amount of information gained by intravascular ultrasonic tissue characterization.

Animals↗
PubMed2005

An empirical assessment of long-branch attraction artefacts in deep eukaryotic phylogenomics.

In the context of exponential growing molecular databases, it becomes increasingly easy to assemble large multigene data sets for phylogenomic studies. The expected increase of resolution due to the reduction of the sampling (stochastic) error is becoming a reality. However, the impact of systematic biases will also become more apparent or even dominant. We have chosen to study the case of the long-branch attraction artefact (LBA) using real instead of simulated sequences. Two fast-evolving eukaryotic lineages, whose evolutionary positions are well established, microsporidia and the nucleomorph of cryptophytes, were chosen as model species. A large data set was assembled (44 species, 133 genes, and 24,294 amino acid positions) and the resulting rooted eukaryotic phylogeny (using a distant archaeal outgroup) is positively misled by an LBA artefact despite the use of a maximum likelihood-based tree reconstruction method with a complex model of sequence evolution. When the fastest evolving proteins from the fast lineages are progressively removed (up to 90%), the bootstrap support for the apparently artefactual basal placement decreases to virtually 0%, and conversely only the expected placement, among all the possible locations of the fast-evolving species, receives increasing support that eventually converges to 100%. The percentage of removal of the fastest evolving proteins constitutes a reliable estimate of the sensitivity of phylogenetic inference to LBA. This protocol confirms that both a rich species sampling (especially the presence of a species that is closely related to the fast-evolving lineage) and a probabilistic method with a complex model are important to overcome the LBA artefact. Finally, we observed that phylogenetic inference methods perform strikingly better with simulated as opposed to real data, and suggest that testing the reliability of phylogenetic inference methods with simulated data leads to overconfidence in their performance. Although phylogenomic studies can be affected by systematic biases, the possibility of discarding a large amount of data containing most of the nonphylogenetic signal allows recovering a phylogeny that is less affected by systematic biases, while maintaining a high statistical support.

Animals↗
PubMed1979

The scientific status of the Rorschach.

Demonstrated that the interpretation of projective test data is semantic, not probabilistic. The clinician does not employ the model of statistical inference in evaluating the meaning of test responses, although he may employ probabilistic rules as guides to interpreation. Procedural rules for interpreting meaning, the nature of clinical diagnosis from psychological tests, and the meaning of prediction as a clinical activity are discussed. It was concluded that clinicians do not make inferences, in the mathematical sense of this term.

Association↗
PubMed2005

A model of protein translation including codon bias, nonsense errors, and ribosome recycling.

We present and analyse a model of protein translation at the scale of an individual messenger RNA (mRNA) transcript. The model we develop is unique in that it incorporates the phenomena of ribosome recycling and nonsense errors. The model conceptualizes translation as a probabilistic wave of ribosome occupancy traveling down a heterogeneous medium, the mRNA transcript. Our results show that the heterogeneity of the codon translation rates along the mRNA results in short-scale spikes and dips in the wave. Nonsense errors attenuate this wave on a longer scale while ribosome recycling reinforces it. We find that the combination of nonsense errors and codon usage bias can have a large effect on the probability that a ribosome will completely translate a transcript. We also elucidate how these forces interact with ribosome recycling to determine the overall translation rate of an mRNA transcript. We derive a simple cost function for nonsense errors using our model and apply this function to the yeast (Saccharomyces cervisiae) genome. Using this function we are able to detect position dependent selection on codon bias which correlates with gene expression levels as predicted a priori. These results indirectly validate our underlying model assumptions and confirm that nonsense errors can play an important role in shaping codon usage bias.

Animals↗
PubMed2005

Probabilistic assessment of clone overlaps in DNA fingerprint mapping via a priori models.

We construct a combinatorially exact probability distribution for the problem of a priori clone overlap assessment in DNA fingerprint mapping. It is implemented as a general enumeration methodology using Bell's exponential polynomials. Established computational metrics do not consider the conditional nature of the problem. They dramatically overpredict actual match probabilities as a consequence. This elevates the rate of false-negative overlap declarations, which is consistent with previous validation studies. Increased measurement resolution does not significantly improve accuracy. We describe general trends in error behavior for intermediate-size DNA clones and discuss corresponding limits on the ability to assess certain overlaps.

Chromosome Mapping↗
PubMed2006

Revised assessment of cancer risk to dichloromethane II. Application of probabilistic methods to cancer risk determinations.

An updated PBPK model of methylene chloride (DCM, dichloromethane) carcinogenicity in mice was recently published using Bayesian statistical methods (Marino et al., 2006). In this work, this model was applied to humans, as recommended by Sweeney et al.(2004). Physiological parameters for input into the MCMC analysis were selected from multiple sources reflecting, in each case, the source that was considered to represent the most current scientific evidence for each parameter. Metabolic data for individual subjects from five human studies were combined into a single data set and population values derived using MCSim. These population values were used for calibration of the human model. The PBPK model using the calibrated metabolic parameters was used to perform a cancer risk assessment for DCM, using the same tumor incidence and exposure concentration data relied upon in the current IRIS entry. Unit risks, i.e., the risk of cancer from exposure to 1 microg/m3 over a lifetime, for DCM were estimated using the calibrated human model. The results indicate skewed distributions for liver and lung tumor risks, alone or in combination, with a mean unit risk (per microg/m3) of 1.05 x 10(-9), considering both liver and lung tumors. Adding the distribution of genetic polymorphisms for metabolism to the ultimate carcinogen, the unit risks range from 0 (which is expected given that approximately 20% of the US population is estimated to be nonconjugators) up to a unit risk of 2.70 x 10(-9) at the 95th percentile. The median, or 50th percentile, is 9.33 x 10(-10), which is approximately a factor of 500 lower than the current EPA unit risk of 4.7 x 10(-7) using a previous PBPK model. These values represent the best estimates to date for DCM cancer risk because all available human data sets were used, and a probabilistic methodology was followed.

Carcinogens↗
PubMed2005

Modelling the long term cost effectiveness of clopidogrel for the secondary prevention of occlusive vascular events in the UK.

OBJECTIVE: To assess the long term cost effectiveness of clopidogrel monotherapy compared with acetylsalicylic acid (aspirin; ASA) monotherapy in patients at risk of secondary occlusive vascular events (OVEs) in the UK. DESIGN: Cost utility analysis based on clinical data from CAPRIE (a multicentre randomised controlled trial, involving 19185 patients); long-term effects were extrapolated beyond the trial period using a Markov model populated with data from UK observational studies. Health economic evaluation carried out from the perspective of the UK National Health Service. PARTICIPANTS: A representative cohort of 1000 UK patients aged 60 years (approximate mean age of the CAPRIE population), with the qualifying diagnoses of myocardial infarction, ischaemic stroke and peripheral arterial disease, who are at risk of secondary OVEs (non-fatal myocardial infarction, non-fatal stroke or vascular death). INTERVENTIONS: Patients were assumed to receive treatment with either clopidogrel (75 mg/day) for 2 years followed by ASA (325 mg/day, average) for their remaining lifetime, or ASA alone (325 mg/day, average) for life. MAIN OUTCOME MEASURES: Incremental cost per life year gained and incremental cost per quality-adjusted life year (QALY) gained. RESULTS: In the base case, the incremental cost effectiveness of clopidogrel versus ASA in this population is estimated at 18888 pounds per life year gained and 21 489 pounds per QALY gained. Multiple deterministic and probabilistic sensitivity analyses suggest the model is robust to variations in a wide range of input parameters. CONCLUSION: Two years of treatment with clopidogrel can be considered a cost effective intervention in patients at risk of secondary OVEs in the UK.

Aged↗