A tractable inference algorithm for diagnosing multiple diseases
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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+)$, where n is the number of diseases, m+ is the number of positive findings and m- is the number of negative findings. Although the time complexity of quickscore is exponential in the number of positive findings, the algorithm is useful in practice because the number of observed positive findings is usually far less than the number of diseases under consideration. Performance results for quickscore applied to a probabilistic version of Quick Medical Reference (QMR) are provided.
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| Abstract | In this paper, I examine a probabilistic m … In this paper, I examine a probabilistic model for the diagnosis of multiple diseases. In the model, diseases and findings are represented as binary variables. Also, diseases are marginally independent, features are conditionally independent given disease instances, and diseases interact to produce findings via a noisy OR-gate. An algorithm for computing the posterior probability of each disease, given a set of observed findings, called quickscore, is presented. The time complexity of the algorithm is 0((n m- 26m+)$, 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. uick Medical Reference (QMR) are provided. |
| Address | North Holland + |
| Author | David Heckerman + |
| Bibtype | inproceedings + |
| Booktitle | Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence + |
| Key | KSL-89-36 + |
| Pages | 163-172 + |
| Publisher | Elsevier Science booktitles B.V + |
| Tag | Computer science + |
| Title | A Tractable Inference Algorithm for Diagnosing Multiple Diseases + |
| Tr id | KSL-89-36 + |
| Year | 1989 + |
