Inferring Knowledge From Clinical Data Banks Utilizing Techniques From Artificial Intelligence

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Abstract
This paper introduces the RX Project, a software design for facilitating the induction and storage of medical knowledge from clinical data banks. The RX Project is an attempt to bridge the gap between clinical data bank systems and knowledge-based medical consultation systems. By integrating these two approaches our expectation is that the benefits of both may be realized and that limitations may be overcome that are present in data bank systems which use only statistical techniques and in knowledge-based methodologies which use mainly artificial intelligence techniques. The RX Project is an outgrowth of two existing computer systems at Stanford Medical Center: ARAMIS Project (American Rheumatism Association Medical Information System) and the MYCIN Project, a knowledge-based system which advises physicians on antimicrobial therapy. We shall first discuss each of these projects individually as examples of 1) statistical and 2) symbolic approaches to medical consultation systems, respectively, in order to present their relative strengths and limitations. Then we shall present an overview of the RX Project architecture, which integrates these methodologies.