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Medical diagnosis using a probabilistic causal network

By William Long, Shapur Naimi, M. G. Criscitiello, Stephen Pauker and Greg Larsen


This paper relates our experience in developing a mechanism for reasoning about the differential diagnosis of cases involving the symptoms of heart failure using a causal model of the cardiovascular hemodynamics with probabilities relating cause to effect. Since the problem requires the determination of causal mechanism as well as primary cause, the model has many intermediate nodes as well as causal circularities requiring a heuristic approach to evaluating probabilities. The method we have developed builds hypotheses incrementally by adding the highest probability path to each finding to the hypothesis. With a number of enhancements and computational tactics, this method has proven effective for generating good hypotheses for typical cases in less than a minute

Year: 1989
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