Application of Knowledge-Engineering Methods in Medical Knowledge Management

This paper deals with Knowledge Engineering (KE), Clinical Decision Support Systems (CDSS), and Expert Systems (ES) as essential methods and tools supporting the Knowledge Management (KM) process in medicine. Specifically, we focus on the main component of the CDSS, knowledge base (KB). We demonstrate a hybrid approach to the creation, modification, verification, and validation of KB, which combines a fuzzy rule system with data mining. We describe the design and implementation of KB for two CDSS systems. The first system, which supports the evaluation of clinical depression, uses a combination of three methods: (1) creation of fuzzy rules based on expert clinicians’ knowledge and standard guidelines, (2) construction of Artificial Neural Networks (ANN) based on patients’ data, and (3) implementation of a CAKE (Computer Aided Knowledge Engineering) tool. The second system, which supports the diagnosis of obstructive sleep apnea, uses a combination of two methods: (1) creation of fuzzy rules derived from the medical literature and the expert clinicians’ knowledge and (2) induction of decision trees from large clinical data sets. Based on these two clinical studies, we demonstrate that KE methods should be regarded as valuable methods and tools which can be successfully used in medical KM for the creation, validation, and maintenance of KB.