Medical Diagnosis using a probabilistic causal network

This paper relates our experience in developing a mechanism for reasoning about the di erential diagnosis of cases involving the symptoms of heart failure using a causal model of the cardiovascular hemodynamics with probabilities relating cause to e ect. 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 nding to the hypothesis. With a number of enhancements and computational tactics, this method has proven e ective for generating good hypotheses for typical cases in less than a minute.