Applying a new rule-base inference methodology into clinical decision making

A critical issue in clinical decision support system (CDSS) research area is how to represent and reason with both medical domain knowledge and clinical symptoms to arrive at reliable conclusions even when under uncertainty. This paper describes how to apply a recently developed generic rule-base inference methodology using the evidential reasoning (RIMER) approach to model clinical domain knowledge and clinical inference process in a CDSS. A simple case study is employed to illustrate the new belief rule-based CDSS, and the result shows that the proposed CDSS is capable of modeling and reasoning with both clinical domain knowledge and clinical symptoms under various types of uncertainties. Moreover, the diagnosis results generated by the CDSS can be used to rank the severity of patient cases.