Probabilistic Evaluation of Candidates and Symptom Clustering for Multidisorder Diagnosis

Thomas D. Wu

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arXiv · 1304.1136

Probabilistic Evaluation of Candidates and Symptom Clustering for Multidisorder Diagnosis

Thomas D. Wu

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Abstract

This paper derives a formula for computing the conditional probability of a set of candidates, where a candidate is a set of disorders that explain a given set of positive findings. Such candidate sets are produced by a recent method for multidisorder diagnosis called symptom clustering. A symptom clustering represents a set of candidates compactly as a cartesian product of differential diagnoses. By evaluating the probability of a candidate set, then, a large set of candidates can be validated or pruned simultaneously. The probability of a candidate set is then specialized to obtain the probability of a single candidate. Unlike earlier results, the equation derived here allows the specification of positive, negative, and unknown symptoms and does not make assumptions about disorders not in the candidate.

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Thomas D. Wu. 2013-03-27. Probabilistic Evaluation of Candidates and Symptom Clustering for Multidisorder Diagnosis. https://arxiv.org/abs/1304.1136

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