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At least 19 recordsLinked to original sources
PubMed1992

Clusters galore: insights about environmental clusters from probability theory.

The posterior probability of a causal explanation given that an environmental cancer cluster is statistically significant depends on the prior probability of an environmentally caused cluster, the sensitivity of the statistical test and its specificity. The prior probability is low, because it is rare to have enough carcinogen in the general environment to cause a relative risk of cancer high enough to achieve statistical significance in a small geographic area. The sensitivity and specificity are not great. The likelihood that a census tract escapes statistically significant elevations in all 80 types of cancer can be calculated. Many of the thousands of census tracts will, by chance alone, have at least one type of cancer whose elevation is statistically significant. Actual observation from a large cancer registry confirms this probabilistic prediction. Applying the principles of Bayes' Theorem would suggest that most statistically significant environmental cancer clusters are not due to environmental carcinogens. One would have to investigate hundreds of environmental cancer clusters to find one with a true environmental cause.

Bayes Theorem↗
PubMed1985

Use of the average antibody-antigen bond concept and probability theory to simplify modeling of linear and circular antibody-antigen complex formation.

We illustrate use of a simple approach to describe the equilibrium interactions of mixtures of monoclonal antibodies and antigens. This procedure is based on elementary concepts in probability theory and is readily suited to describing interactions of antibodies and antigens which form circular as well as linear complexes. The method is also suited to describing the inhibitory effects of antibodies which compete for overlapping epitopes and an example is provided to show how the procedure can be used to describe the interactions of antibodies which inhibit circular complex formation. We also outline simple strategies for preparing computer programs to simulate binding of antigens to defined antibody mixtures. The methods described should facilitate design of immunoassay procedures based on the use of defined mixtures of monoclonal antibodies.

Antibodies, Monoclonal↗
PubMed1990

An application of probability theory to a group of breath-alcohol and blood-alcohol data.

Many jurisdictions have "per se" driving-while-intoxicated (DWI) status expressed in terms of a blood-alcohol concentration (BAC) standard (in grams per 100 mL or the equivalent). Since breath-alcohol (BrAC) analysis is typically employed to determine BAC, there is often challenge to the use of an assumed 2100:1 conversion ratio. This concern may be relevant in light of considerable data that show a low percentage of cases in which BrAC greater than BAC, and this concern increases when the BrAC is used to predict BAC in the context of "per se" legislation. Probability theory provides a basis for estimating the likelihood of an individual having a BrAC greater than or equal to g/210 L with a corresponding BAC less than 0.10 g/100 mL. Actual field data from the state of Wisconsin (n = 404) were evaluated to determine the probability of this occurrence. The probability for this occurrence involves the multiplication law for independent events. The computed probability from the data was 0.018. The actual number of occurrences where BrAC greater than or equal to 0.10 g/210 L and BAC less than 0.10 g/100 mL was 5, resulting in a probability of 0.012. The concern of having BrAC greater than BAC at the critical "per se" level has a very low probability of occurrence, which thus supports the reasonableness of "per se" DWI legislation based upon a blood-alcohol standard determined by breath-alcohol analysis.

Alcoholic Intoxication↗
PubMed1986

Probability theory in the use of diagnostic tests. An introduction to critical study of the literature.

The purpose of this article is to provide an understanding of methods that are useful in formulating advice about when to use diagnostic tests. If the clinician expresses diagnostic uncertainty as the probability of a disease in a patient, Bayes' theorem may be used to predict the effect of doing various tests and their impact on patient management. To use Bayes' theorem wisely, one must be aware of pitfalls in estimating probability and must understand the limitations of most studies of the sensitivity and specificity of diagnostic tests.

Bayes Theorem↗
PubMedDate not supplied

The feasibility of axiomatically-based expert systems.

We distinguish axiomatically-based expert systems, whose design and implementation are guided by one or more axiomatically-based theories of decision-making (e.g., decision theory, Bayesian probability theory, maximum entropy theory), from traditional expert systems. An analysis of the knowledge acquisition and computational needs of axiomatically-based expert systems is presented. An explicit quantitative comparison is made between the actual knowledge acquisition effort required to build an existing expert system, and the effort that would be required to build an analogous axiomatically-based advice system. The costs and benefits of the axiomatic approach are discussed. The analysis suggests that the small additional cost of knowledge acquisition for the axiomatic approach are outweighed by the long-term benefits this approach provides.

Computer Simulation↗
PubMed1992

Expert systems in histopathology. V. DS theory, certainty factors and possibility theory.

Uncertainty management for the evaluation of evidence based on linguistic and conceptual data is taking advantage of developments in the Dempster-Shafer (DS) theory of evidence, possibility theory and fuzzy logic. The DS theory offers the capability to assess the uncertainty of different subsets of assertions in a domain and the way in which uncertainty is affected by accumulating evidence. The DS theory goes beyond probability theory in its ability to represent ignorance about certain aspects of a situation. However, the theory is very sensitive to the numerical assessments provided by users and can lead to intuitively unexpected and even undesirable results. Certainty factors are widely used in various expert systems. Their definition and updating may follow either a probabilistic model or fuzzy set theoretic concept.

Data Interpretation, Statistical↗
PubMed1991

Reasoning in uncertainties. An analysis of five strategies and their suitability in pathology.

In reasoning systems, uncertainty plays a crucial part, especially for those fields in which judgements are essential, as in pathology. Uncertainty has several aspects, such as prevalence of diseases, occurrence of findings and the sensitivity and predictive value of findings. For the functioning of a reasoning system, two aspects are crucial: (1) the internal representation of the uncertainty and (2) the way in which the uncertainty is propagated in the reasoning process when combining formal statements. Five well-known reasoning strategies (Bayes' probability theory, MYCIN's certainty factor model, fuzzy set theory, the theory of Dempster-Shafer and Pathfinder's scoring mechanism) are compared, with particular attention to: (1) Under what conditions will the model function? In particular, what information is to be specified a priori to the system? (2) Can the different aspects of uncertainty be dealt with as separate entities? (3) How are unknown uncertainties dealt with? (4) How is evidence in favor of a hypothesis combined with evidence against it? (5) How does the model treat the simultaneous occurrence of more than one disorder, that is, how does the model support reasoning with compound hypotheses? It is preliminarily concluded that the different aspects of uncertainty are expressed as separate entities only in Pathfinder and probability theory. Hence, the other models do not accurately represent uncertain knowledge. Also, such theoretically attractive models as the Bayes, MYCIN and Dempster-Shafer theory can only function properly under the tight condition of mutual exclusiveness of hypotheses, which is not always suited for broader areas of pathology. They may, however, be suited for smaller areas, with a limited number of defined diseases and a limited number of features. All models but the Bayes model lack a predictable performance since there is no (or only a partial) underlying theory to guarantee minimization of the overall error.

Humans↗
PubMed1987

Spatial distribution of twitch and tonic fibres in a snake muscle one myofibre thick.

The spatial distribution of twitch and tonic fibres in a snake muscle one myofibre thick (ventral costocutaneous) has been investigated. It was found that small groups of like fibres were favoured at the expense of larger groups when compared with sequences generated by a computer in which fibres were distributed randomly. The number of times that like fibres occurred next to one another was used as another measure of their spatial distribution. The number of adjacencies of like fibres was less than the expected number of adjacencies determined both by random sequences and by probability theory. The expected number of adjacencies determined by means of random sequences and a priori probability theory were almost identical. This dispersion of like fibres may reflect processes that occur during muscle development and may have functional implications.

Animals↗
PubMed1979

Probability and the brain.

Probability theory asserts the lawfulness of seemingly random events in large populations and seems to be a reasonable approach to a general understanding of the structure and function of the nervous system. The brain, by virtue of the number of its components, the multiplicity of their possible interconnections, and the range and rapidity of their outputs, is almost implausably complex in its over-all design. Probability theory, therefore, is usually applied to (a) descriptions of the behavior of large neuronal populations, (b) statistical analysis of neuronal spike trains, and (c) theoretical models of neuronal interaction. A consideration of each of these subjects is presented, as is a discussion of the most fundamental level of application of the theory to the nervous system: (d) the assertion that the neuron and/or brain is inherently nondeterministic. In practical terms this is shown to be a "nonissue," the uncertainty principle that follows has rather definite philosophical implications.

Auditory Perception↗
PubMedDate not supplied

Clinical reasoning and cognitive processes.

Expected utility theory, and the Bayesian probability theory on which it is based, form the normative basis of most work in medical decision analysis. Recent work in the psychology of judgments and decisions indicates that people do not conform to the axioms of this theory and that these deviations occur in clinical reasoning as well as in the psychology laboratory. At issue is what to do now. The authors argue that the important next steps lie at the interface between descriptive, prescriptive, and normative accounts, all of which affect each other. They point to examples in which the simplest application of supposedly normative theory seems inappropriate, and suggest ways in which the tension between normative and descriptive models may be resolved.

Cognition↗
PubMed1987

The cardiac ventricular defibrillation threshold: inherent limitations in its application and interpretation.

A quantity termed the "threshold" has been used to describe the results of electrical ventricular defibrillation studies, with the implication of a clear distinction between ineffective and effective shock intensities. Although several definitions of the threshold have been suggested, and various methods have been used to quantify it, no comparison of the accuracies of the various methods could be found in the literature. This article, after presenting a method of applying basic probability theory to an assumed distribution relating probability of successful defibrillation to current amplitude, uses the method to examine several popular algorithms for defibrillation-threshold determination. The results show that where a sharp transition exists from ineffective to effective current amplitudes, most algorithms yield fairly good results. Where that transition is gradual (as it appears to be in all of the published reports examined), the algorithms are shown to be inadequate.

Algorithms↗
PubMed1991

A probabilistic view on steady-state enzyme reactions.

A new theoretical description of steady-state enzyme reactions is proposed. The description is based on the concepts of the probability theory instead of the conventional formalism of chemical kinetics. A general steady-state rate equation is obtained using a probabilistic model of the catalytic act. As a result, the classical problems of enzyme kinetics can be formulated and considered in a different aspect. It is shown that the new theory presents a clearer treatment of some commonly obscure questions, such as the steady-state establishment. The theory also gives additional possibilities in the interpretation of the experimental results of traditional methods and proposes certain new ways in experimental investigations of enzymes.

Animals↗
PubMed1992

Beyond positivism: a metaphysical basis for clinical practice?

Medicine does not have its own unified body of scientific knowledge. Instead, physicians who are oriented to research make sporadic incursions into the basic sciences such as genetics, biochemistry, immunology, epidemiology, physiology, pharmacology and so on. These latter, taken together, comprise biomedicine which is said to have adopted the positivist epistemology or the Cartesian/Newtonian one that regards the scientist as an uninvolved observer of nature. In effect, medical science has come to rest on a theory of knowledge which links meaning to probability and considers prediction as the scientist's chief task. Like its predecessor, the probability theory of meaning rejects metaphysical speculation and remains connected to observations made, directly or indirectly, by means of the five senses. Despite some brilliant successes touching on relatively uncommon disorders, biomedicine cannot explain most day-to-day clinical activity. An understanding of what transpires between patient and doctor, of its diagnostic potential and therapeutic weight requires hermeneutic, or phenomenological, inquiry which brings about changes in both parties to it. Such a science, as ontological speculation has been called, cannot be deciphered by an epistemology couched in the imagery of physics and chemistry.

Metaphysics↗