Modeling diagnosis at multiple levels of abstraction. I. Representing causal relations at multiple levels of abstraction

Abstract

A significant body of causal knowledge for diagnostic problem-solving is organized at multiple levels of abstraction. By this we mean that causal relations are specified in terms of disorder and manifestation classes that can be further refined as well as in terms of specific, unrefinable disorders and manifestations. Such knowledge enables diagnostic problem-solvers (human or automated) to efficiently form initial, high-level diagnostic hypotheses while avoiding the explicit consideration of unnecessary details. This article develops a knowledge representation framework to precisely yet naturally capture causal relations at multiple levels of abstraction. Different interpretations of high-level causal associations are precisely defined and systematically tabulated. Rules to infer implicit causal relations from explicitly declared causal relations are also identified. These ideas have been implemented in a working system for medical diagnosis. the results presented in this article also offers a new perspective on studying the semantics of knowledge representation in general.