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We have investigated the ability to simulate a patient from a knowledge base. Specifically, we have examined the use of knowledge bases that associate findings with diseases through the use of probability measures, and their ability to generate realistic patient cases that can be used for teaching purposes. Many of these knowledge bases encode neither the interdependence among findings, nor intermediate disease states. Because of this, the use of these knowledge bases results in the generation of inconsistent or nonsensical patients. This paper describes an approach for the addition of causal structure to these knowledge bases which can overcome many of these limitations and improve the explanatory capability of such systems. |
