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Possibilistic Medical Knowledge Representation Model

Medical Decision Support Systems involve two main issues: med- ical knowledge representation and reasoning mechanisms adapted to the considered representation model. This paper proposes an ap- proach to construct a new medical knowledge representation model, based on the use of possibility theory. The major interest of using the possibility theory comes from its capacity to represent differ- ent types of information (quantitative, qualitative, binary, etc.), as well as different forms of information imperfections such as uncer- tainty, imprecision, ambiguity and incompleteness. Starting from the description, realized by an expert of the medical knowledge, describing the relationship between symptoms and diagnoses, the proposed approach consists on building a possibilistic model includ- ing the Medical Knowledge Base. Moreover, the proposed approach integrates several possibilistic reasoning mechanisms based on the considered knowledge. The validation of the proposed approach is then conducted using an Endoscopic Knowledge Base. The proposed representation, reasoning model and the obtained validation results show a real interest in order to realize various goals of Medical De- cision Support Systems such as classification, similarity estimation, etc.