KSL-89-36
## A Tractable Inference Algorithm for Diagnosing Multiple Diseases

**Reference: **
Heckerman, D. A Tractable Inference Algorithm for Diagnosing Multiple Diseases. Elsevier Science Publishers B.V, North Holland, 1989.

**Abstract:**
In this paper, I examine a probabilistic model for the diagnosis of multiple
diseases. In the model, diseases and findings are represented as binary
variables. Also, diseases are marginally independent, features are
conditionally independent given disease instances, and diseases interact to
produce findings via a noisy OR-gate. An algorithm for computing the
posterior probability of each disease, given a set of observed findings,
called quickscore, is presented. The time complexity of the algorithm is 0((n
m- 26m+)$, where n is the number of diseases, m+ is the number of positive
findings and m- is the number of negative findings. Although the time
complexity of quickscore is exponential in the number of positive findings,
the algorithm is useful in practice because the number of observed positive
findings is usually far less than the number of diseases under consideration.
Performance results for quickscore applied to a probabilistic version of Quick
Medical Reference (QMR) are provided.

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