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Citation: David Heckerman. (1989) A Tractable Inference Algorithm for Diagnosing Multiple Diseases. In Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence, 163-172,1989.
| Publication inproceedings ( Edit )
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| type | InProceedings
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| bibtype | inproceedings
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| Bibtex basics
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| author | David Heckerman
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| title | A Tractable Inference Algorithm for Diagnosing Multiple Diseases
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| booktitle | Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
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| pages | 163-172
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| address | North Holland
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| year | 1989
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| Bibtex more
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| publisher | Elsevier Science booktitles B.V
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| Access Paper
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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+)$, where n is the number of diseases, m+ is the number of positive findings and m- is the number of negative findings. Although the time complexity of quickscore is exponential in the number of positive findings, the algorithm is useful in practice because the number of observed positive findings is usually far less than the number of diseases under consideration. Performance results for quickscore applied to a probabilistic version of Quick Medical Reference (QMR) are provided.
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| KSL Technical Report ID: KSL-89-36
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Facts about A Tractable Inference Algorithm for Diagnosing Multiple DiseasesRDF feed
| Abstract | In this paper, I examine a probabilistic m … In this paper, I examine a probabilistic model for the diagnosis of multiple diseases. In the model, diseases and findings are represented as binary variables. Also, diseases are marginally independent, features are conditionally independent given disease instances, and diseases interact to produce findings via a noisy OR-gate. An algorithm for computing the posterior probability of each disease, given a set of observed findings, called quickscore, is presented. The time complexity of the algorithm is 0((n m- 26m+)$, 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 + |
| Has author | David Heckerman + |
| Has identifier | KSL-89-36 + |
| Has publishing details | 163-172,1989 + |
| Has title | A Tractable Inference Algorithm for Diagnosing Multiple Diseases + |
| Has where published | Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence + |
| Has year | 1989 + |
| Ksl tr id | KSL-89-36 + |
| Pages | 163-172 + |
| Process note | GOOGLE + |
| Publisher | Elsevier Science booktitles B.V + |
| Title | A Tractable Inference Algorithm for Diagnosing Multiple Diseases + |
| Year | 1989 + |
