Diagnostic reasoning and medical decision-making with fuzzy influence diagrams.
Influence diagrams have been widely used as knowledge bases in medical informatics and many applied domains. In conventional influence diagrams, the numerical models of uncertainty are probability distributions associated with chance nodes and value tables for value nodes. However, when incomplete knowledge or linguistic vagueness is involved in the reasoning systems, the suitability of probability distributions is questioned. This study intends to propose an alternative numerical model for influence diagrams, possibility distributions, which extend influence diagrams into fuzzy influence diagrams. In fuzzy influence diagrams, each chance node and value node is associated with a possibility distribution which expresses the uncertain features of the node. This study also develops a simulation algorithm and a fuzzy programming model for diagnosis and optimal decision in medical settings.
Kao
Han-Ying
HY
Department of Computer and Information Science, National Hualien University of Education, 123 Hua-Hsi Road, Hualien 970, Taiwan, ROC. teresak_hk@yahoo.com.tw
D000465:Algorithms; D003658:Decision Making, Computer-Assisted; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005103:Expert Systems; D017143:Fuzzy Logic