Expert system design in hematology diagnosis.

A two-part study was designed to test the hypothesis that sufficient information is available from a modern hematology analyzer (the Coulter STKS) to reach a reliable intermediate conclusion which can be used as input to the next decision-making level in the design of a high-performance expert system for hematology diagnosis. In phase one, we analyzed the performance of three probabilistic systems (using Bayes' rule) which interpret STKS data: a control system which took the traditional approach of classifying cases into specific diagnoses, and two test systems which were designed to reach only an intermediate conclusion but not a final diagnosis. One of the test systems classified cases into "textbook categories" of disease and the other utilized defined diagnostic patterns. The systems were tested with 150 cases. The pattern approach ranked the correct choice first in 141 of 150 cases (94%). In phase two, we abandoned Bayes' rule, reformulated the pattern approach into a heuristic classification system, and tested its reliability on 820 cases. The algorithm of the reformulated system was able to classify all 820 cases into the same predominant pattern as a panel of three experienced laboratory hematologists.