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PubMed1999

Using probabilistic and decision-theoretic methods in treatment and prognosis modeling.

Causal probabilistic networks, also called Bayesian networks, allow both qualitative knowledge about the structure of a problem and quantitative knowledge, derived from case databases, expert opinion and literature to be exploited in the construction of decision support systems for diagnosis, treatment and prognosis. This mixing of qualitative and quantitative knowledge will be illustrated, using the selection of antibiotics for a subset of patients with severe infections. The subset consists of patients where bacteria or fungi have been found in the blood. A simple pathophysiological model of infection is used to calculate a prognosis, dependent on the choice of antibiotics. A decision-theoretic approach is used to balance the therapeutic benefit of antibiotic treatment against the cost of antibiotics in the form of direct monetary cost, side effects and ecological cost. A retrospective trial on patients with bacteria or fungi in the blood stemming from the urinary tract indicates that with this approach, it may be possible to suggest balanced choices of antibiotics that not only achieve greater therapeutic benefit, but also reduce the cost of therapy.

Anti-Bacterial Agents↗
PubMed2005

A probabilistic transmission dynamic model to assess indoor airborne infection risks.

The purpose of this article is to quantify the public health risk associated with inhalation of indoor airborne infection based on a probabilistic transmission dynamic modeling approach. We used the Wells-Riley mathematical model to estimate (1) the CO2 exposure concentrations in indoor environments where cases of inhalation airborne infection occurred based on reported epidemiological data and epidemic curves for influenza and severe acute respiratory syndrome (SARS), (2) the basic reproductive number, R0 (i.e., expected number of secondary cases on the introduction of a single infected individual in a completely susceptible population) and its variability in a shared indoor airspace, and (3) the risk for infection in various scenarios of exposure in a susceptible population for a range of R0. We also employ a standard susceptible-infectious-recovered (SIR) structure to relate Wells-Riley model derived R0 to a transmission parameter to implicate the relationships between indoor carbon dioxide concentration and contact rate. We estimate that a single case of SARS will infect 2.6 secondary cases on average in a population from nosocomial transmission, whereas less than 1 secondary infection was generated per case among school children. We also obtained an estimate of the basic reproductive number for influenza in a commercial airliner: the median value is 10.4. We suggest that improving the building air cleaning rate to lower the critical rebreathed fraction of indoor air can decrease transmission rate. Here, we show that virulence of the organism factors, infectious quantum generation rates (quanta/s by an infected person), and host factors determine the risk for inhalation of indoor airborne infection.

Air Microbiology↗
PubMed2000

Assessing the empirical validity of the "take-the-best" heuristic as a model of human probabilistic inference.

The boundedly rational 'Take-The-Best" heuristic (TTB) was proposed by G. Gigerenzer, U. Hoffrage, and H. Kleinbölting (1991) as a model of fast and frugal probabilistic inferences. Although the simple lexicographic rule proved to be successful in computer simulations, direct empirical demonstrations of its adequacy as a psychological model are lacking because of several methodical problems. In 4 experiments with a total of 210 participants, this question was addressed. Whereas Experiment 1 showed that TTB is not valid as a universal hypothesis about probabilistic inferences, up to 28% of participants in Experiment 2 and 53% of participants in Experiment 3 were classified as TTB users. Experiment 4 revealed that investment costs for information seem to be a relevant factor leading participants to switch to a noncompensatory TTB strategy. The observed individual differences in strategy use imply the recommendation of an idiographic approach to decision-making research.

Adult↗
PubMed1997

Integrating database homology in a probabilistic gene structure model.

We present an improved stochastic model of genes in DNA, and describe a method for integrating database homology into the probabilistic framework. A generalized hidden Markov model (GHMM) describes the grammar of a legal parse of a DNA sequence. Probabilities are estimated for gene features by using dynamic programming to combine information from multiple sensors. We show how matches to homologous sequences from a database can be integrated into the probability estimation by interpreting the likelihood of a sequence in terms of the bit-cost to encode a sequence given a homology match. We also demonstrate how homology matches in protein databases can be exploited to help identify splice sites. Our experiments show significant improvements in the sensitivity and specificity of gene structure identification when these new features are added to our gene-finding system, Genie. Experimental results in tests using a standard set of annotated genes showed that Genie identified 95% of coding nucleotides correctly with a specificity of 91%, and 77% of exons were identified exactly.

Algorithms↗
PubMed2002

Probabilistic neural network modeling of the toxicity of chemicals to Tetrahymena pyriformis with molecular fragment descriptors.

We present the results of an investigation into the use of a probabilistic neural network (PNN) based methodology to model the 48-60-h ICG50 (inhibitory concentration for population growth) sublethal toxicity to the ciliate Tetrahymena pyriformis. The information fed into the neural network is solely based on simple molecular descriptors as can be derived from the chemical structure. In contrast to most other toxicological models, the octanol/water partition coefficient is not used as an input parameter and no rules of thumb, or other substance selection-criteria, are involved. The model was trained on a 1,000 substances data set and validated using an 84 substances external test set. The associated analysis of errors confirms the excellent recognitive and predictive capabilities of the model.

Animals↗
PubMed2005

On the relationship between deterministic and probabilistic directed Graphical models: from Bayesian networks to recursive neural networks.

Machine learning methods that can handle variable-size structured data such as sequences and graphs include Bayesian networks (BNs) and Recursive Neural Networks (RNNs). In both classes of models, the data is modeled using a set of observed and hidden variables associated with the nodes of a directed acyclic graph. In BNs, the conditional relationships between parent and child variables are probabilistic, whereas in RNNs they are deterministic and parameterized by neural networks. Here, we study the formal relationship between both classes of models and show that when the source nodes variables are observed, RNNs can be viewed as limits, both in distribution and probability, of BNs with local conditional distributions that have vanishing covariance matrices and converge to delta functions. Conditions for uniform convergence are also given together with an analysis of the behavior and exactness of Belief Propagation (BP) in 'deterministic' BNs. Implications for the design of mixed architectures and the corresponding inference algorithms are briefly discussed.

Bayes Theorem↗
PubMed1983

Probabilistic automata as a model for epigenesis of cellular networks.

Probabilistic automata are compared with deterministic ones in simulations of growing networks made of dividing interconnected cells. On examples of chains, wheels and tree-like structures made of large numbers of cells it is shown that the number of necessary states in the initial generating cell automaton is reduced drastically when the automaton is probabilistic rather than deterministic. Since the price being paid is a decrease in the accuracy of the generated network, conditions under which reasonable compromises can be achieved are studied. They depend on the degree of redundancy of the final network (defined from the complexity of a deterministic automaton capable of generating it with maximum accuracy), on the "entropy" of the generating probabilistic automaton, and on the effects of different inputs on its transition probabilities (as measured by its "'capacity" in the sense of Shannon's information theory). The results are used to discuss and make more precise the notion of biological specificity. It is suggested that the weak metaphor of a genetic program, classically used to account for the role of DNA in specific genetic determinations, is replaced by that of inputs to biochemical probabilistic automata.

Biological Evolution↗
PubMed2001

A joint physics-based statistical deformable model for multimodal brain image analysis.

A probabilistic deformable model for the representation of multiple brain structures is described. The statistically learned deformable model represents the relative location of different anatomical surfaces in brain magnetic resonance images (MRIs) and accommodates their significant variability across different individuals. The surfaces of each anatomical structure are parameterized by the amplitudes of the vibration modes of a deformable spherical mesh. For a given MRI in the training set, a vector containing the largest vibration modes describing the different deformable surfaces is created. This random vector is statistically constrained by retaining the most significant variation modes of its Karhunen-Loève expansion on the training population. By these means, the conjunction of surfaces are deformed according to the anatomical variability observed in the training set. Two applications of the joint probabilistic deformable model are presented: isolation of the brain from MRI using the probabilistic constraints embedded in the model and deformable model-based registration of three-dimensional multimodal (magnetic resonance/single photon emission computed tomography) brain images without removing nonbrain structures. The multi-object deformable model may be considered as a first step toward the development of a general purpose probabilistic anatomical atlas of the brain.

Anatomy, Cross-Sectional↗
PubMed2003

Mixture models and the probabilistic structure of depth cues.

Monocular cues to depth derive their informativeness from a combination of perspective projection and prior constraints on the way scenes in the world are structured. For many cues, the appropriate priors are best described as mixture models, each of which characterizes a different category of objects, surfaces, or scenes. This paper provides a Bayesian analysis of the resulting model selection problem, showing how the mixed structure of priors creates the potential for non-linear, cooperative interactions between cues and how the information provided by a single cue can effectively determine the appropriate constraint to apply to a given image. The analysis also leads to a number of psychophysically testable predictions. We test these predictions by applying the framework to the problem of perceiving planar surface orientation from texture. A number of psychophysical experiments are described that show that the visual system is biased to interpret textures as isotropic, but that when sufficient image data is available, the system effectively turns off the isotropy constraint and interprets texture information using only a homogeneity assumption. Human performance is qualitatively similar to an optimal estimator that assumes a mixed prior on surface textures--some proportion being isotropic and homogeneous and some proportion being anisotropic and homogeneous.

Adult↗
PubMed1998

A General Formulation for Unidimensional Unfolding and Pairwise Preference Models: Making Explicit the Latitude of Acceptance.

Probabilistic unfolding models for direct responses of persons to statements are characterised by single peaked response functions. The range in which a positive response is most likely is termed the latitude of acceptance, a well known but little researched concept in the modelling of attitude measurement. This paper derives a general form for probabilistic unfolding models in which a natural parameter characterises the latitude of acceptance. It is shown that a number of already known models for unfolding can be reexpressed in this general form with different operational functions and that, in doing so, the implied latitude of acceptance for these models is identified. It is also shown that other unfolding models can be generated readily by specifying the operational function of the general form. A general form for pairwise preference models is also presented. A discussion on the latitude of acceptance parameter as a scale parameter is also provided. Copyright 1998 Academic Press.

Journal Article↗
PubMed2002

Model misspecification and probabilistic tests of topology: evidence from empirical data sets.

Probabilistic tests of topology offer a powerful means of evaluating competing phylogenetic hypotheses. The performance of the nonparametric Shimodaira-Hasegawa (SH) test, the parametric Swofford-Olsen-Waddell-Hillis (SOWH) test, and Bayesian posterior probabilities were explored for five data sets for which all the phylogenetic relationships are known with a very high degree of certainty. These results are consistent with previous simulation studies that have indicated a tendency for the SOWH test to be prone to generating Type 1 errors because of model misspecification coupled with branch length heterogeneity. These results also suggest that the SOWH test may accord overconfidence in the true topology when the null hypothesis is in fact correct. In contrast, the SH test was observed to be much more conservative, even under high substitution rates and branch length heterogeneity. For some of those data sets where the SOWH test proved misleading, the Bayesian posterior probabilities were also misleading. The results of all tests were strongly influenced by the exact substitution model assumptions. Simple models, especially those that assume rate homogeneity among sites, had a higher Type 1 error rate and were more likely to generate misleading posterior probabilities. For some of these data sets, the commonly used substitution models appear to be inadequate for estimating appropriate levels of uncertainty with the SOWH test and Bayesian methods. Reasons for the differences in statistical power between the two maximum likelihood tests are discussed and are contrasted with the Bayesian approach.

Animals↗
PubMed1989

Probabilistic division systems modeling the generation of mosaic fields.

The explanation of mosaic pattern in chimeric organs analyzed by in situ methods requires modeling of specific hypotheses. The use of computer simulations to achieve this has led to the conclusion that finely variegated mixtures of cell lineage within chimeric tissues does not require extensive cell movement. Cell division models were used to determine the distribution of patch size as mosaic fields are generated. The results establish that these distributions are sensitive to the proportion of the two cell types which comprise the mosaic.

Animals↗
PubMed1993

On a probabilistic set covering model for diagnosing psychiatric disorders.

Correct diagnosis and treatment of the patients attending a hospital is the foremost requirement if that hospital is to be a profitable proposition. F.T. DeDombal achieved 70% reduction in unwanted surgeries and almost 100% reduction in delayed surgeries in cases of acute abdomen through computer-aided diagnosis. Similarly, R. Boom et al proved an increase in the professional efficiency of internees in diagnosing cases of jaundice, acute abdominal pain & upper G.I. bleeding after they started using computer. N.G. Rao reported diagnostic accuracy of 94% for various types of anemias with the use of expert system developed by him. Johri & Guha showed an enhancement of performance of non-expert clinician in tackling psychiatric disorders from 64% to 86%. Many developed countries have developed data bases for studying the epidemiological patterns of various diseases for future health planning and combating strategy. Another advantage of computer based diagnosis is that it will provide uniformity for defining diseases, symptoms & signs which is very important for pursuing medical research. A very simple methodology is described in this paper which can provide data driven as well as heuristic driven approach to medical decision making. To start with, this approach has been used to analyse psychiatric disorders but can be implemented elsewhere with the same efficiency.

Decision Making↗
PubMedDate not supplied

Mobilis in mobile: a probabilistic and chronotopic model of mobility in urban spaces.

In this communication we propose an urban mobility model based on individual random walk driven by a chronotopic action with a deterministic public transportation network. In the absence of chronotopoi the mean field analytic results are found in good agreement with simulations on a computer. When the chronotopoi are switched on, they attract people according to a given law and we obtain a sort of diffusive motion. The model can describe many different kinds of dynamical systems, including biological ones. The work is in progress and the next step will be an empirical test in a concrete case.

City Planning↗
PubMed2001

Demonstrating attainment of the air quality standards: integration of observations and model predictions into the probabilistic framework.

This paper introduces an integrated observational-modeling approach to transform the deterministic nature of attainment demonstrations of the National Ambient Air Quality Standard (NAAQS) into the probabilistic framework. While the methods presented here can be used to address any air quality standard that is based on extreme values, this paper focuses on the application to the 1-hr and 8-hr NAAQS for ozone. Extreme value statistics and resampling techniques are applied to estimate the probability of exceeding the NAAQS for both 1-hr and 8-hr ozone concentrations. Within the integrated observation-modeling analysis approach, we show that the model-to-model differences in the predicted responses to emission reductions are smaller than the model-to-model differences in predicted absolute ozone concentrations. We illustrate that the emission reductions stemming from a real-world emission control strategy would substantially reduce the probability of exceeding the NAAQS over a large portion of the eastern United States, especially for the 8-hr average ozone concentrations.

Air Pollution↗