Development of a knowledge base for diagnostic reasoning in cardiology.
This paper reports on a formative evaluation of the diagnostic capabilities of the Heart Failure Program, which uses a probability network and a heuristic hypothesis generator. Using 242 cardiac cases collected from discharge summaries at a tertiary care hospital, we compared the diagnoses of the program to diagnoses collected from cardiologists using the same information as was available to the program. With some adjustments to the knowledge base, the Heart Failure Program produces appropriate diagnoses about 90% of the time on this training set. The main reasons for the inappropriate diagnoses of the remaining 10% include inadequate reasoning with temporal relations between cause and effect, severity relations, and independence of acute and chronic diseases.
Long
W J
WJ
MIT Laboratory for Computer Science, Cambridge.
Naimi
S
S
Criscitiello
M G
MG
D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted; D005069:Evaluation Studies as Topic; D006331:Heart Diseases / Q000175:diagnosis*; D006801:Humans; D011336:Probability; D012984:Software