Krzysztof Michalik

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.

Computational Representation of Medical Concepts: A Semiotic and Fuzzy Logic Approach

Medicine and biology are among the fastest growing application areas of computer-based systems. Nonetheless, the creation of a computerized support for the health systems presents manifold challenges. One of the major problems is the modeling and interpretation of heterogeneous concepts used in medicine. The medical concepts such as, for example, specific symptoms and their etiologies, are described using terms from diverse domains - some concepts are described in terms of molecular biology and genetics, some concepts use models from chemistry and physics; yet some, for example, mental disorders, are defined in terms of particular feelings, behaviours, habits, and life events. Moreover, the computational representation of medical concepts must be (1) formally or rigorously specified to be processed by a computer, (2) human-readable to be validated by humans, and (3) sufficiently expressive to model concepts which are inherently complex, multi-dimensional, goal-oriented, and, at the same time, evolving and often imprecise. In this chapter, we present a meta-modeling framework for computational representation of medical concepts. Our framework is based on semiotics and fuzzy logic to explicitly model two important characteristics of medical concepts: changeability and imprecision. Furthermore, the framework uses a multi-layered specification linking together three domains: medical, computational, and implementational. We describe the framework using an example of mental disorders, specifically, the concept of clinical depression. To exemplify the changeable character of medical concepts, we discuss the evolution of the diagnostic criteria for depression. We discuss the computational representation for polythetic and categorical concepts and for multi-dimensional and noncategorical concepts. We demonstrate how the proposed modeling framework utilizes (1) a fuzzy-logic approach to represent the non-categorical (continuous) nature of the symptoms and (2) a semiotic approach to represent the contextual interpretation and dimensional nature of the symptoms.

Validation and verification of temporal knowledge as an important aspect of implementing a temporal knowledge base system supporting organizational creativity

The paper is devoted to the problem of temporal knowledge validation and verification during the process of implementing a system supporting organizational creativity. The motivation for implementing a temporal knowledge base system is presented, the implementation methodology is outlined, and the V&V (validation&verification) process is described in detail, using an example of the Logos reasoning tool. The main achievements of the paper are: elaborating a new implementation methodology for a temporal knowledge base system, and elaborating detailed V&V steps.