On the co-operation between abductive and temporal reasoning in medical diagnosis

• Authors: Luca Console;Pietro Torasso
• Venue: Artificial Intelligence in Medicine
• Year: 1991

On of the basic (and often implicit) assumptions of most first generation diagnostic expert systems is that they operate in a static environment. However, the static domain assumption is very limiting since it requires that all manifestations are observable (and observed) at a unique time point in order to perform diagnosis (and this is unrealistic in medical applications). The adoption of deep and causal models in second generation expert systems provided some insights into how to deal with time in the diagnostic process. There is, in fact, a strong relationship between the notion of causation and the notion of time. In the paper we present an architecture for diagnostic problem solving based on the use of a pathophysiological model in which both causal and temporal relations are explicitly represented. In particular, the architecture is an extension of the causal component of CHECK which has been used to model pathophysiology in the fields of cirrhosis and leprosis. We show that in such an extended framework diagnostic problems can be solved correctly only by means of a strict co-operation between abductive and temporal reasoning. The complexity of such forms of reasoning is analysed and some sources of complexity are singled out. Possible restrictions of the representation formalism are presented and forms of temporal reasoning providing approximate solutions are discussed.