Combining Multi-Criteria Analysis with CBR for Medical Decision Support

One of the most visible developments in Decision Support Systems (DSS) was the emergence of rule-basedexpert systems. Hence, despite their success in many sectors, developers of Medical Rule-Based Systems havemet several critical problems. Firstly, the rules are related to a clearly stated subject. Secondly, a rule-basedsystem can only learn by updating of its rule-base, since it requires explicit knowledge of the used domain. Solutions to these problems have been sought through improved techniques and tools, improveddevelopment paradigms, knowledge modeling languages and ontology, as well as advanced reasoningtechniques such as case-based reasoning (CBR) which is well suited to provide decision support in thehealthcare setting. However, using CBR reveals some drawbacks, mainly in its interrelated tasks: the retrievaland the adaptation. For the retrieval task, a major drawback raises when several similar cases are found andconsequently several solutions. Hence, a choice for the best solution must be done. To overcome theselimitations, numerous useful works related to the retrieval task were conducted with simple and convenientprocedures or by combining CBR with other techniques. Through this paper, we provide a combiningapproach using the multi-criteria analysis (MCA) to help, the traditional retrieval task of CBR, in choosingthe best solution. Afterwards, we integrate this approach in a decision model to support medical decision. Wepresent, also, some preliminary results and suggestions to extend our approach. KCI Citation Count: 1