Probabilistic Reasoning and Certainty Factors

The. development of automated assistance for medical diagnosis and decision making is an area of both theoretical and practical interest. Of methods for utilizing evidence to select diagnoses or decisions, probability theory has the firmest appeal. Probability theory in the form of Bayes’ Theorem has been used by a number of" workers (Ross, 1972). Notable among recent developments are those of de Dombal and coworkers (de Dombal, 1973; de Dombal et al., 1974; 1975) and Pipberger and coworkers (Pipberger et al., 1975). The usefulness of Bayes’ Theorem is limited practical difficulties, principally the lack of data adequate to estimate accurately the a priori and conditional probabilities used in the theorem. One attempt to mitigate this problem has been to assume statistical independence among various pieces of evidence. How seriously this approximation affects results is often unclear, and correction mechanisms have been explored (Ross, 1972; Norusis and Jacquez, 1975a; 1975b). Even the independence assumption requires an unmanageable number of estimates of" probabilities for most applications with realistic complexity. To circumvent this problem, some have tried to elicit estimates of probabilities directly from experienced physicians (Gorry, 1973; Ginsberg, 1971; Gustafson et al., 1971), while others have turned from the use of Bayes’ Theorem and probability theory to the use of" discriminant analysis (Ross, 1972) and nonprobabilistic methods (Scheinok and Rinaldo, 1971; Cumberbatch and Heaps, 1973; Cumberbatch et al., 1974; Glesser and Collen, 1972). Shortliffe and Buchanan (1975) have offered a model of inexact reasoning in medicine used in the MYCIN system (Chapter 11). Their model