Uncertainty in Artificial Intelligence
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A Tractable Inference Algorithm for Diagnosing Multiple Diseases
David Heckerman
Abstract:
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.
Keywords:
Pages: 174-181
PS Link:
PDF Link: /papers/89/p174-heckerman.pdf
BibTex:
@INPROCEEDINGS{Heckerman89,
AUTHOR = "David Heckerman ",
TITLE = "A Tractable Inference Algorithm for Diagnosing Multiple Diseases",
BOOKTITLE = "Proceedings of the Fifth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-89)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "1989",
PAGES = "174--181"
}


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