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Soft Computing in Humanities and Social Sciences pp 401–420Cite as

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

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Part of the book series: Studies in Fuzziness and Soft Computing ((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.

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References

  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. Adlassnig, K.-P.: Fuzzy set theory in medical diagnosis. IEEE Trans. on Systems, Man, and Cybernetics SMC-16, 260–265 (1986)

    Article  Google Scholar 

  3. American Psychological Association DSM-5 Development, http://www.dsm5.org/Pages/Default.aspx (accessed May 10, 2010)

  4. Barsalou, L.W.: Context-independent and context-dependent information in concepts. Memory and Cognition 10(1), 82–93 (1982)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  6. Bruner, J.S., Goodnow, J.J., Austin, G.A.: A Study of Thinking. Wiley, New York (1956)

    Google Scholar 

  7. Chandler, D.: Semiotics: The Basics. Routledge, London (2002)

    Book  Google Scholar 

  8. Coriera, E.: Guide to Health Informatics, 2nd edn. Hodder Arnold, London (2003)

    Google Scholar 

  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. Davis, R., Schrobe, H., Szolovits, P.: What is a knowledge representation? AI Magazine 14(1), 17–33 (1993)

    Google Scholar 

  11. Ludwik, F.: Genesis and Development of a Scientific Fact. The University of Chicago Press, Chicago (1979)

    Google Scholar 

  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. Hamilton, M.: A rating scale for depression. Journal of Neurology, Neurosurgery and Psychiatry 23(1), 56–61 (1960)

    Article  Google Scholar 

  14. Hunter, P., Nielsen, P.: A Strategy for Integrative Computational Physiology. Physiology 20, 316–325 (2005)

    Article  Google Scholar 

  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. Kielan, K.: The Salomon advisory system supports a depressive episode therapy. Polish Journal of Pathology 54(3), 215–218 (2003)

    Google Scholar 

  17. Kitano, H.: Computational systems biology. Nature 420, 206–210 (2002), doi:10.1038/nature01254

    Article  Google Scholar 

  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, 08.005

    Google Scholar 

  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. 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. Medin, D.L., Schaffer, M.M.: Context theory of classification learning. Psychological Review 85, 207–238 (1978)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  25. Ontology Lookup Service (OLS). http://www.ebi.ac.uk/ontology-lookup/browse.do?ontName=DOID (Accessed May 2, (2010)

  26. Reed, S.K.: Cognition. Theory and Applications, 4th edn. Brooks/Cole Publishing, Pacific Grove (1996)

    Google Scholar 

  27. Rosch, E., Mervis, C.B.: Family Resemblances: Studies in the Internal Structure of Categories. Cognitive Psychology 7, 573–605 (1975)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Sadegh-Zadeh, K.: Fuzzy health, illness, and disease. The Journal of Medicine and Philosophy 25, 605–638 (2000)

    Article  Google Scholar 

  31. Sebeok, T.A.: Signs: An introduction to semiotics. University of Toronto Press (1999)

    Google Scholar 

  32. Sebeok, T.A., Danesi, M.: The Forms of Meaning: Modeling Systems Theory and Semiotic Analysis. Mounton de Gruyter, Berlin (2000)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. Sowa John, F.: Knowledge Representation: Logical, Philosophical, and Computational Foundations. Brooks/Cole (2000)

    Google Scholar 

  36. Wierzbicki, A.P.: Modelling as a way of organising knowledge. European Journal of Operational Research 176(1), 610–635 (2007)

    Article  MATH  Google Scholar 

  37. Wittgenstein, L.: Philosophical Investigations. Blackwell, Oxford (1953)

    Google Scholar 

  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)

    Article  Google Scholar 

  39. Zadeh, L.A.: Fuzzy Sets. Information and Control 8(3), 338–353 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  40. Zadeh, L.A.: A note on prototype theory and fuzzy sets. Cognition, 291–297 (1982)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computing Science Department, Thompson Rivers University (TRU), Kamloops, Canada

    Mila Kwiatkowska

  2. Department of Knowledge Engineering, University of Economics, Katowice, Poland

    Krzysztof Michalik

  3. Department of Psychiatry, University of Economics, Harrison House, Peaks Lane, Grimsby, UK

    Krzysztof Kielan

Authors
  1. Mila Kwiatkowska
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  2. Krzysztof Michalik
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  3. Krzysztof Kielan
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Editors and Affiliations

  1. C/ Carlos Asensio Bretones 5 -- 8F, Oviedo, 33009, Spain

    Rudolf Seising

  2. Soto del Barco 2, 6°C. Esc.B, Oviedo, 33012, Spain

    Veronica Sanz González

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Kwiatkowska, M., Michalik, K., Kielan, K. (2012). Computational Representation of Medical Concepts: A Semiotic and Fuzzy Logic Approach. In: Seising, R., Sanz González, V. (eds) Soft Computing in Humanities and Social Sciences. Studies in Fuzziness and Soft Computing, vol 273. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24672-2_21

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  • DOI: https://doi.org/10.1007/978-3-642-24672-2_21

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