A Tractable Inference Algorithm for Diagnosing Multiple Diseases
  We 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 O(nm-2m+), 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 i5 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.
