Next: Acknowledgements
Medical Diagnosis Using a Probabilistic Causal Network
William Long
Principal Research Associate
M.I.T. Laboratory for Computer Science
545 Technology Square, Room 368
Cambridge, MA 02139, U.S.A.
Reprinted from Applied Artificial Intelligence, 3: 367-383, 1989
Abstract:
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.