Probabilistic Evaluation of Candidate Sets for Multidisorder Diagnosis
Thomas Wu
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.
Keywords: null
Pages: 107115
PS Link:
PDF Link: /papers/90/p107wu.pdf
BibTex:
@INPROCEEDINGS{Wu90,
AUTHOR = "Thomas Wu
",
TITLE = "Probabilistic Evaluation of Candidate Sets for Multidisorder Diagnosis",
BOOKTITLE = "Uncertainty in Artificial Intelligence 6 Annual Conference on Uncertainty in Artificial Intelligence (UAI90)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1990",
PAGES = "107115"
}

