A theoretic framework for intelligent expert systems in medical encounter evaluation

Abstract: This paper describes a novel approach to implementation of a medical diagnosis expert system that can assist physicians with their daily practices. Differential artificial intelligence techniques are incorporated into a multi-stage expert system to best represent the various phases of the patient diagnosis process. A weighted scoring system is used to represent the subjective analysis stage, while a rule-based fuzzy expert system is employed to both interpret laboratory tests and imaging findings and suggest the final diagnosis. A model of various patient flow scenarios is presented to demonstrate the functionality of the proposed expert system. An actual example of patient walkthrough is used to demonstrate various computation steps from recording the patient chief complaint to arriving at the final diagnosis. It is shown that the conclusion arrived at by using the proposed system is consistent with a common diagnosis of a third party specialist who is asked to evaluate the performance of the system.