STENO:an expert system for medical diagnosis based on graphical models and model search

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Abstract
Causal probabilistic models have been suggested for representing diagnostic knowledge in expert systems. This paper describes the theoretical basis for and the implementation of an expert system based on causal probabilistic networks. The system includes model search for building the knowledge base, a shell for making the knowledge base available for users in consultation sessions, and a user interface. The system contains facilities for storing knowledge and propagating new knowledge, and mechanisms for building the knowledge base by semi-automated analysis of a large sparse contingency table. The contingency table contains data acquired for patients in the same diagnostic category as the intended application area of the expert system. The knowledge base of the expert system is created by combining expert knowledge and a statistical model search in a model conversion scheme based on a theory developed by Lauritzen & Spiegelhalter and using exact tests as suggested by Kreiner. The system is implemented on a PC and has been used to simulate the diagnostic value of additional clinical information for coronary artery disease patients under consideration for being referred to coronary arteriography.

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