Premium
Applying a belief rule‐base inference methodology to a guideline‐based clinical decision support system
Author(s) -
Kong Guilan,
Xu DongLing,
Liu Xinbao,
Yang JianBo
Publication year - 2009
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2009.00500.x
Subject(s) - computer science , evidential reasoning approach , inference , vagueness , knowledge base , guideline , clinical decision support system , process (computing) , domain (mathematical analysis) , rule based system , decision support system , artificial intelligence , rule of inference , machine learning , data mining , business decision mapping , fuzzy logic , medicine , mathematics , mathematical analysis , pathology , operating system
A critical issue in the clinical decision support system (CDSS) research area is how to represent and reason with both uncertain medical domain knowledge and clinical symptoms to arrive at accurate conclusions. Although a number of methods and tools have been developed in the past two decades for modelling clinical guidelines, few of those modelling methods have capabilities of handling the uncertainties that exist in almost every stage of a clinical decision‐making process. This paper describes how to apply a recently developed generic rule‐base inference methodology using the evidential reasoning approach (RIMER) to model clinical guidelines and the clinical inference process in a CDSS. In RIMER, a rule base is designed with belief degrees embedded in all possible consequents of a rule. Such a rule base is capable of capturing vagueness, incompleteness and non‐linear causal relationships, while traditional IF–THEN rules can be represented as a special case. Inference in such a rule base is implemented using the evidential reasoning approach which has the capability of handling different types and degrees of uncertainty in both medical domain knowledge and clinical symptoms. A case study demonstrates that employing RIMER in developing a guideline‐based CDSS is a valid novel approach.