Information Technology Reference
In-Depth Information
14
Application of Knowledge-Engineering Methods
in Medical Knowledge Management
von Krzysztof Michalik, Mila Kwiatkowska, and Krzysztof Kielan
Abstract.
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 demon-
strate 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) cre-
ation 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 med-
ical literature and the expert clinicians' knowledge and (2) induction of decision
trees from large clinical data sets. Based on these two clinical studies, we demon-
strate 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.
14.1
Introduction
Knowledge Management (KM) and Knowledge Engineering (KE) are two related
disciplines that share several methods and tools. Most importantly, these two dis-
ciplines have the same subject - knowledge. On the other hand, KE and KM have
diverse origins, and often use different definitions of the term “knowledge.”
Knowledge Engineering (KE) is an engineering discipline, which is concerned
with the development of knowledge-based information systems, and, thus, it is
closely related to software engineering and Artificial Intelligence (AI). KE cre-
ates computational methods, languages, and tools for knowledge representation and
problem solving by computers.
The focus of KE has changed from “knowledge
