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Results for “probabilistic modelling”

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At least 145 records · Page 8Linked to original sources
PubMed2001

A probabilistic model for silver bioaccumulation in aquatic systems and assessment of human health risks.

Silver (Ag) is discharged in wastewater effluents and is also a component in a proposed secondary water disinfectant. A steady-state model was developed to simulate bioaccumulation in aquatic biota and assess ecological and human health risks. Trophic levels included phytoplankton, invertebrates, brown trout, and common carp. Uptake routes included water, food, or sediment. Based on an extensive review of the literature, distributions were derived for most inputs for use in Monte Carlo simulations. Three scenarios represented ranges of dilution and turbidity. Compared with the limited field data available, median estimates of Ag in carp (0.07-2.1 micrograms/g dry weight) were 0.5 to 9 times measured values, and all measurements were within the predicted interquartile range. Median Ag concentrations in biota were ranked invertebrates > phytoplankton > trout > carp. Biotic concentrations were highest for conditions of low dilution and low turbidity. Critical variables included Ag assimilation efficiency, specific feeding rate, and the phytoplankton bioconcentration factor. Bioaccumulation of Ag seems unlikely to result in toxicity to aquatic biota and humans consuming fish. Although the highest predicted Ag concentrations in water (> 200 ng/L) may pose chronic risks to early survival and development of salmonids and risks of argyria to subsistence fishers, these results occur under highly conservative conditions.

Animals↗
PubMed2002

Probabilistic modeling of platelet aggregation: effects of activation time and receptor occupancy.

A mathematical model is constructed to predict the probability that a collision between two activated platelets results in doublet formation mediated by fibrinogen cross-bridges. The model is used to explore the effect of time from activation, looking at both simultaneous and non-simultaneous activation times. Also considered are the impact of blood fibrinogen concentration and various shear rates. The idea of hydrodynamic efficiency [Tandon & Diamond (1997) Biophys. J.73, 2819-2835] is extended by varying the separation distance which is considered to be a collision. From fitting the model to data [Xia & Frojmovic (1994) Biophys. J.66, 2190-2201], it is found that the hydrodynamic efficiency corresponds to short interaction distances ( approximately 14 nm). The model predicts that the probability of forming a doublet increases quickly after activation, remains near its maximum for a significant time interval, and then declines. This may contribute to the regulation of the time and location of platelet aggregation, by ensuring that platelets are more likely to aggregate near an injury, rather than downstream in the vascular system. A newly activated platelet has a high probability of cross-bridging with an already activated platelet. Fibrinogen concentration strongly affects the time course and the equilibrium values of the aggregation probability. These results indicate the importance of considering the progression of the reaction between solution fibrinogen and surface receptors in determining a platelet's ability to aggregate.

Computer Simulation↗
PubMed1995

Prevention of sudden cardiac death: a probabilistic model for decision support.

As part of the Cardiac Arrhythmia and Risk of Death Patient Outcomes Research Team (CARD PORT) study we are developing a comprehensive decision model to help physicians identify preferred strategies for preventing sudden cardiac death. The model integrates three components: a screening model, a treatment model, and a value model. Ultimately this model will use the CARD PORT's collective findings to produce policy recommendations and will support patient-specific clinical decision making. Our initial modeling suggests the importance of patient-specific value models in an analysis of treatment options. Although our model is specific to cardiac sudden death, other medical domains that exhibit similar characteristics--the importance of patient preferences and the uncertainty regarding the benefits of strategies for risk stratification and treatment--can use a conceptual framework similar to the approach we used to represent strategies to prevent sudden cardiac death.

Death, Sudden, Cardiac↗
PubMed1996

A probabilistic model of hypobaric decompression sickness based on 66 chamber tests.

One consequence of the NASA tissue ratio (TR) model is that calculated probability of decompression sickness [P(DCS)] is constant in tests at different ambient pressures so long as the ratio of P1N2 to P2 is the same in each test; P1N2 is N2 pressure in the 360 minute half-time compartment, and P2 is ambient pressure after decompression. We test the hypothesis that constant P(DCS) is better described by TRs that decrease as P2 decreases. Data were from 66 NASA and USAF hypobaric chamber tests resulting in 211 cases of DCS in 1075 exposures. The response variable was presence or absence of DCS while at P2. Explanatory variables were P1N2, P2, exercise at P2, (yes or no), time to DCS (failure time), and time to end of test in those without DCS (censored time). Probability models were fitted using techniques from survival analysis. The log likelihood for the two parameter log logistic survival model was -846 with only failure and censored times, -801 when TR [P1N2/P2] plus exercise were added, and -663 when modified TR [(((P1N2+cl)/P2)-1)c2] plus exercise were added, where c1 and c2 are fitted parameters in the five parameter model. Constant P(DCS) was better described by TRs that decrease as P2 decreases; a conclusion supported by additional empirical observations, and bubble growth models that are independent of DCS data. Exercise increased the P(DCS) at P2. As a description of decompression "dose", the modified TR was superior to TR over a wider range of experimental conditions.

Adult↗
PubMed2006

Varying complexity in tree-structured image distribution models.

Probabilistic models of image statistics underlie many approaches in image analysis and processing. An important class of such models have variables whose dependency graph is a tree. If the hidden variables take values on a finite set, most computations with the model can be performed exactly, including the likelihood calculation, training with the EM algorithm, etc. Crouse et al. developed one such model, the hidden Markov tree (HMT). They took particular care to limit the complexity of their model. We argue that it is beneficial to allow more complex tree-structured models, describe the use of information theoretic penalties to choose the model complexity, and present experimental results to support these proposals. For these experiments, we use what we call the hierarchical image probability (HIP) model. The differences between the HIP and the HMT models include the use of multivariate Gaussians to model the distributions of local vectors of wavelet coefficients and the use of different numbers of hidden states at each resolution. We demonstrate the broad utility of image distributions by applying the HIP model to classification, synthesis, and compression, across a variety of image types, namely, electrooptical, synthetic aperture radar, and mammograms (digitized X-rays). In all cases, we compare with the HMT.

Algorithms↗
PubMed2006

New probabilistic network models and algorithms for oncogenesis.

Chromosomal aberrations in solid tumors appear in complex patterns. It is important to understand how these patterns develop, the dynamics of the process, the temporal or even causal order between aberrations, and the involved pathways. Here we present network models for chromosomal aberrations and algorithms for training models based on observed data. Our models are generative probabilistic models that can be used to study dynamical aspects of chromosomal evolution in cancer cells. They are well suited for a graphical representation that conveys the pathways found in a dataset. By allowing only pairwise dependencies and partition aberrations into modules, in which all aberrations are restricted to have the same dependencies, we reduce the number of parameters so that datasets sizes relevant to cancer applications can be handled. We apply our framework to a dataset of colorectal cancer tumor karyotypes. The obtained model explains the data significantly better than a model where independence between the aberrations is assumed. In fact, the obtained model performs very well with respect to several measures of goodness of fit and is, with respect to repetition of the training, more or less unique.

Algorithms↗
PubMed1986

Probabilistic sensitivity analysis methods for general decision models.

Probabilistic sensitivity analysis has previously been described for the special case of dichotomous decision trees. We now generalize these techniques for a wider range of decision problems. These methods of sensitivity analysis allow the analyst to evaluate the impact of the multivariate uncertainty in the data used in the decision model and to gain insight into the probabilistic contribution of each of the variables to the decision outcome. The techniques are illustrated using Monte Carlo simulation on a trichotomous decision tree. Application of these powerful tools permits the decision analyst to investigate the structure and limitations of more complex decision problems with inherent uncertainties in the data upon which the decisions are based. Probabilistic sensitivity measures can provide guidance into the allocation of resources to resolve uncertainty about critical components of medical decisions.

Decision Theory↗
PubMed1994

A probabilistic multidimensional model of location information.

A probabilistic multidimensional model of location discrimination is developed and applied to data from an experiment in which subjects are required to determine whether a briefly presented horizontal and vertical bar are touching. The proposed gap-detection model assumes that errors in perception are due to variability in the perceived location and/or in the perceived length of the bars. A series of gap-detection models that allow variability only in perceived location were rejected on the basis of likelihood-ratio tests of overall goodness of fit. However, when the models were modified to account for: (a) a compression of the distance perceived between the bars (Wolford, 1975), or (b) the bisection illusion (Künnapas, 1955), excellent absolute fits to the data were obtained. A pair of models that suggests that the horizontal/vertical illusion or a response bias was operative failed. Applications of the model to more conventional object-perception experiments (e.g., the illusory-conjunction experiment) are discussed.

Adult↗
PubMed2001

The Rasch model, additive conjoint measurement, and new models of probabilistic measurement theory.

This research describes some of the similarities and differences between additive conjoint measurement (a type of fundamental measurement) and the Rasch model. It seems that there are many similarities between the two frameworks, however, their differences are nontrivial. For instance, while conjoint measurement specifies measurement scales using a data-free, non-numerical axiomatic frame of reference, the Rasch model specifies measurement scales using a numerical frame of reference that is, by definition, data dependent. In order to circumvent difficulties that can be realistically imposed by this data dependence, this research formalizes new non-parametric item response models. These models are probabilistic measurement theory models in the sense that they explicitly integrate the axiomatic ideas of measurement theory with the statistical ideas of order-restricted inference and Markov Chain Monte Carlo. The specifications of these models are rather flexible, as they can represent any one of several models used in psychometrics, such as Mokken's (1971) monotone homogeneity model, Scheiblechner's (1995) isotonic ordinal probabilistic model, or the Rasch (1960) model. The proposed non-parametric item response models are applied to analyze both real and simulated data sets.

Humans↗
PubMed2006

Probabilistic biokinetic modelling of radiocaesium uptake in Arctic seal species: verification of modelled data with empirical observations.

The necessity to provide information about radionuclide concentrations in Arctic marine species has been heightened in recent years due to a number of accidents in Arctic regions involving nuclear vessels and the presence of a large number of potential radioactive contamination sources. The provision for such information is largely dependent on the use of radionuclide uptake and transfer models. The uptake of radionuclides in Arctic seal species in this study has been modelled using a probabilistic biokinetic approach. In this paper, model results are compared with empirical data from relevant samples taken within the Arctic region. Results indicate that the model performs well when estimating concentrations of (137)Cs in two seal species for both median values and reproduction of the distribution of data values, but not as well for a third seal species. Likely factors affecting the results are the probability density functions used for the input parameters.

Animals↗