Abstract
This paper describes the knowledge representations that are used in MedFrame/CADIAG-IV, a medical computer consultation system. Similar to its predecessor system, CADIAG-2 [1], fuzzy medical knowledge bases are used to model the uncertainty and the vagueness of medical concepts and fuzzy logic reasoning mechanisms provide the basic inference engines. Because the elicitation and acquisition of the required knowledge is a difficult and time-consuming task (even more so when unfamiliar representations like fuzzy membership functions are to be acquired), MedFrame/CADIAG-IV has been designed to provide better support for knowledge engineers and domain experts to define fuzzy knowledge concepts as well as fuzzy inference rules. Knowledge acquisition procedures and computer tools have been implemented in order to make the three main tasks of (a) defining medical concepts, (b) providing appropriate interpretations for patient data, and (c) constructing inferential knowledge easier and more accessible. The paper discusses the motivations and rationale for some system design and data modeling decisions and explains how the main tasks are supported both by special representations and by a stepwise knowledge acquisition process.
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