A Fuzzy Knowledge-Editing Framework for Encoding Guidelines into Clinical DSSs

Clinical guidelines have been more and more promoted as a means to foster effective and efficient medical practices and improve health outcomes, especially when implemented in clinical Decision Support Systems (DSSs). In this context, Fuzzy Logic has been proposed as the most suitable approach for profitably tackling uncertainty and vagueness in both clinical recommendations and signs triggering them. In this respect, since the task of building and maintaining a fuzzy knowledge base can be very complex and must be carried out carefully, this paper proposes AFEF (A Fuzzy knowledge Editing Framework), an editing and visualization framework for encoding fuzzy linguistic guidelines into clinical DSSs with the aim of providing intuitive solutions specifically devised to: i) define block of rules pertaining the positive evidence of the same abnormal situation; ii) compose ELSE rules for modeling the negative evidence associated to a block of rules; iii) customize the rules inside a block of rules through a common configuration for the inference; iv) simulate an actual DSS for testing the fuzzy rules inserted; v) automatically encode into a machine executable language the fuzzy clinical knowledge that could be functional in the context of clinical DSSs.