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Causal knowledge representation techniques: A case study in medical informatics

Abstract:

It is a common endeavor in medicine to identify and represent causal relationships between variables of interest. A computational representation of causal knowledge should be based on directed graphs. There are two main techniques: bayesian networks and fuzzy cognitive maps. The present paper compares the two techniques and shows the advantages of fuzzy cognitive maps. It is suggested that fuzzy cognitive maps be used in medicine. A procedure to obtain causal models is described. A case study is presented showing the applicability of the proposal, as well as the advantages of cognitive maps to represent causal knowledge in a given situation. Future research is proposed to expand the use of fuzzy cognitive maps.