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

  • Mila Kwiatkowska
  • Krzysztof Michalik
  • Krzysztof Kielan
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 273)


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.


Fuzzy Logic Depressed Mood Knowledge Representation Linguistic Variable Computational Representation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mila Kwiatkowska
    • 1
  • Krzysztof Michalik
    • 2
  • Krzysztof Kielan
    • 3
  1. 1.Computing Science DepartmentThompson Rivers University (TRU)KamloopsCanada
  2. 2.Department of Knowledge EngineeringUniversity of EconomicsKatowicePoland
  3. 3.Department of PsychiatryUniversity of EconomicsGrimsbyUK

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