A probabilistic reformulation of the quick medical reference system
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abstract: We report on the design and implementation of a two-level multiply connected belief-network representation of the QMR knowledge base. We use probabilities derived from QMR disease profiles and from National Center for Health Statistics hospital-discharge statistics. Using a stochastic simulation algorithm for inference on the belief network, we compare the performance of QMR to that of the probabilistic reformulation on cases abstracted from continuing medical education materials from Scientific American Medicine.
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| Abstract | We report on the design and implementation … We report on the design and implementation of a two-level multiply connected belief-network representation of the QMR knowledge base. We use probabilities derived from QMR disease profiles and from National Center for Health Statistics hospital-discharge statistics. Using a stochastic simulation algorithm for inference on the belief network, we compare the performance of QMR to that of the probabilistic reformulation on cases abstracted from continuing medical education materials from Scientific American Medicine. terials from Scientific American Medicine. |
| Author | Michael Shwe +, Blackford Middleton +, David Heckerman +, Max Henrion +, Eric Horvitz +, Harold P. Lehmann +, and Gregory F. Cooper + |
| Bibtype | techreport + |
| Institution | Knowledge Systems, AI Laboratory + |
| Key | KSL-90-11 + |
| Number | KSL-90-11 + |
| Tag | Computer science + |
| Title | A Probabilistic Reformulation of the Quick Medical Reference System + |
| Tr id | KSL-90-11 + |
| Year | 1990 + |
