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

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

Abstract

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.

Keywords

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adlassnig, K.-P.: A fuzzy logical model of computer-assisted medical diagnosis. Methods of Information in Medicine 19, 141–148 (1980)Google Scholar
  2. 2.
    Adlassnig, K.-P.: Fuzzy set theory in medical diagnosis. IEEE Trans. on Systems, Man, and Cybernetics SMC-16, 260–265 (1986)CrossRefGoogle Scholar
  3. 3.
    American Psychological Association DSM-5 Development, http://www.dsm5.org/Pages/Default.aspx (accessed May 10, 2010)
  4. 4.
    Barsalou, L.W.: Context-independent and context-dependent information in concepts. Memory and Cognition 10(1), 82–93 (1982)CrossRefGoogle Scholar
  5. 5.
    Bates, J.H.T., Young, M.P.: Applying fuzzy logic to medical decision making in the intensive care unit. American Journal of Respiratory and Critical Care Medicine 167, 948–952 (2003)CrossRefGoogle Scholar
  6. 6.
    Bruner, J.S., Goodnow, J.J., Austin, G.A.: A Study of Thinking. Wiley, New York (1956)Google Scholar
  7. 7.
    Chandler, D.: Semiotics: The Basics. Routledge, London (2002)CrossRefGoogle Scholar
  8. 8.
    Coriera, E.: Guide to Health Informatics, 2nd edn. Hodder Arnold, London (2003)Google Scholar
  9. 9.
    Ernest, C., Leski, J.: Entropy and energy measures of fuzziness in ECG signal processing. In: Szczepaniak, P., Kacprzyk, J. (eds.) Fuzzy Systems in Medicine, pp. 227–245. Physica-Verlag, Heidelberg (2000)Google Scholar
  10. 10.
    Davis, R., Schrobe, H., Szolovits, P.: What is a knowledge representation? AI Magazine 14(1), 17–33 (1993)Google Scholar
  11. 11.
    Ludwik, F.: Genesis and Development of a Scientific Fact. The University of Chicago Press, Chicago (1979)Google Scholar
  12. 12.
    Gruenberg, A.M., Goldstein, R.D., Pincus, H.A.: Classification of Depression: Research and Diagnostic Criteria: DSM-IV and ICD-10. In: Licincio, J., Wong, M.-L. (eds.) Biology of Depression. From Novel Insights to Therapeutic Strategies. WIILEY-VCH, Weinheim (2005)Google Scholar
  13. 13.
    Hamilton, M.: A rating scale for depression. Journal of Neurology, Neurosurgery and Psychiatry 23(1), 56–61 (1960)CrossRefGoogle Scholar
  14. 14.
    Hunter, P., Nielsen, P.: A Strategy for Integrative Computational Physiology. Physiology 20, 316–325 (2005)CrossRefGoogle Scholar
  15. 15.
    Kalali, A., Williams, J.B.W., Kobak, K.A., et al.: The new GRID HAM-D: pilot testing and international field trials. International Journal of Neuropsychopharmacology 5, 147–148 (2002)Google Scholar
  16. 16.
    Kielan, K.: The Salomon advisory system supports a depressive episode therapy. Polish Journal of Pathology 54(3), 215–218 (2003)Google Scholar
  17. 17.
    Kitano, H.: Computational systems biology. Nature 420, 206–210 (2002), doi:10.1038/nature01254CrossRefGoogle Scholar
  18. 18.
    Kola, J.(Subbarao)., Harris, J., Lawrie, S., Rector, A., Goble, C., Martone, M.: Towards an ontology for psychosis. Cognitive Systems Research (2008), doi:10.1016/j.cogsys, 08.005Google Scholar
  19. 19.
    Kruger, R.F., Bezdjian, S.: Enhancing research and treatment of mental disorders with dimensional concepts: toward DSM-V and ICD-11. World Psychiatry 8, 3–6 (2009)Google Scholar
  20. 20.
    Nosofsky, R.M.: Exemplars, prototypes, and similarity rules. In: Healy, A., Kosslyn, S., Shiffrin, R. (eds.) From Learning Theory to Connectionist Theory: Essays in Honour of William K. Estes, vol. 1. Erlbaum, Hillsdale (1992)Google Scholar
  21. 21.
    Medin, D.L., Schaffer, M.M.: Context theory of classification learning. Psychological Review 85, 207–238 (1978)CrossRefGoogle Scholar
  22. 22.
    Minda, J.P., Smith, J.D.: The effects of category size, category structure and stimulus complexity. Journal of Experimental Psychology: Learning, Memory and Cognition 27, 755–799 (2001)CrossRefGoogle Scholar
  23. 23.
    Modai, I., Kuperman, J., Goldberg, I., Goldish, M., Mendel, S.: Fuzzy logic detection of medically serious suicide attempt records in major psychiatric disorders. The Journal of Nervous and Mental Disease 192(10), 708–710 (2004)CrossRefGoogle Scholar
  24. 24.
    Ohayon, M.M.: Improving decision making processes with the fuzzy logic approach in the epidemiology of sleep disorders. Journal of Psychosomatic Research 47(4), 297–311 (1999)CrossRefGoogle Scholar
  25. 25.
    Ontology Lookup Service (OLS). http://www.ebi.ac.uk/ontology-lookup/browse.do?ontName=DOID (Accessed May 2, (2010)
  26. 26.
    Reed, S.K.: Cognition. Theory and Applications, 4th edn. Brooks/Cole Publishing, Pacific Grove (1996)Google Scholar
  27. 27.
    Rosch, E., Mervis, C.B.: Family Resemblances: Studies in the Internal Structure of Categories. Cognitive Psychology 7, 573–605 (1975)CrossRefGoogle Scholar
  28. 28.
    Rosch, E., Mervis, C.B., Gray, W.D., Johnsen, D.M., Penny, B.-B.: Basic objects in natural categories. Cognitive Psychology 8, 382–440 (1976)CrossRefGoogle Scholar
  29. 29.
    Rothenfluh, T.E., Bögl, K., Adlassnig, K.-P.: Representation and acquisition of knowledge for a fuzzy medical consultation system. In: Szczepaniak, P.S., Kacprzyk, J. (eds.) Fuzzy Systems in Medicine, pp. 636–651. Physica-Verlag, Heidelberg (2000)Google Scholar
  30. 30.
    Sadegh-Zadeh, K.: Fuzzy health, illness, and disease. The Journal of Medicine and Philosophy 25, 605–638 (2000)CrossRefGoogle Scholar
  31. 31.
    Sebeok, T.A.: Signs: An introduction to semiotics. University of Toronto Press (1999)Google Scholar
  32. 32.
    Sebeok, T.A., Danesi, M.: The Forms of Meaning: Modeling Systems Theory and Semiotic Analysis. Mounton de Gruyter, Berlin (2000)Google Scholar
  33. 33.
    Seising, R.: From vagueness in medical thought to the foundations of fuzzy reasoning in medical diagnosis. Artificial Intelligence in Medicine 38, 237–256 (2006)CrossRefGoogle Scholar
  34. 34.
    Sheng-Cheng, H.: A semiotic view of information: semiotics as a foundation of LIS research in information behavior. Proceedings of the American Society for Information Science and Technology 43(1), 66 (2006)Google Scholar
  35. 35.
    Sowa John, F.: Knowledge Representation: Logical, Philosophical, and Computational Foundations. Brooks/Cole (2000)Google Scholar
  36. 36.
    Wierzbicki, A.P.: Modelling as a way of organising knowledge. European Journal of Operational Research 176(1), 610–635 (2007)MATHCrossRefGoogle Scholar
  37. 37.
    Wittgenstein, L.: Philosophical Investigations. Blackwell, Oxford (1953)Google Scholar
  38. 38.
    Yin, T.-K., Chiu, N.-T.: A computer-aided diagnosis for distinguishing Tourette’s syndrome from chronic tic disorder in children by a fuzzy system with a two-step minimization approach. IEEE Transactions on Biomedical Engineering 51(7), 1286–1295 (2004)CrossRefGoogle Scholar
  39. 39.
    Zadeh, L.A.: Fuzzy Sets. Information and Control 8(3), 338–353 (1965)MathSciNetMATHCrossRefGoogle Scholar
  40. 40.
    Zadeh, L.A.: A note on prototype theory and fuzzy sets. Cognition, 291–297 (1982)Google Scholar

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

Personalised recommendations