Dealing with Uncertain Knowledge in Medical Decision Making

It has widely been recognized that knowledge-based expert systems need efficient mechanisms to model the uncertainty associated with many decision-making activities. Such a need is particularly urgent in medicine. In this paper, we present an approach based on fuzzy logic to give a possible solution to this problem; its pros and cons are discussed by taking into account the experience gained in developing LITO1 and LITO2, two expert systems devoted to the assessment of the liver function and to the diagnosis of hepatic diseases. The advantages of mixing fuzzy production rules with frame-like structures (introduced for representing the clinical data) are discussed. In particular, the use of fuzzy linguistic variables for modeling the possible values of the clinical data is described: this allows, for a clear and perspicuous description of the correspondence between quantitative and qualitative expressions. Furthermore, different alternatives for evaluating and combining evidence are reviewed. Finally, the need of introducing frame structures also for representing diagnostic hypotheses is discussed, together with the problem of evaluating the fuzzy match between the prototypical description of a diagnostic hypothesis and the data describing the status of the specific patient under examination.