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Uncertainty and imprecision in medical diagnosis support

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper concerns methods of representation of uncertainty and imprecision in successful medical support applications. Advantages of the methods are pointed out and some of their drawbacks are explained. A method of simultaneous representation of imprecision of symptoms and uncertainty of diagnostic rules is proposed. The method suggests an extension of the Dempster-Sahfer theory for fuzzy focal elements. An example of the method is given and their links as well differences from previous approaches are discussed. Conclusions about uncertainty and imprecision representation in medical diagnosis support are provided.
Rocznik
Tom
Strony
11--22
Opis fizyczny
Bibliogr. 37 poz., rys., tab.
Twórcy
  • Institute of Electronics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice
Bibliografia
  • [1] ADLASSNIG K.P., KOLARZ G., CADIAG-2 computer assisted medical diagnosis using fuzzy subsets, in: GUPTA M.M., SANCHEZ E. (eds.), Application Reasoning in Decision Analysis, North Holland Publ. Co., Amsterdam, 1982, pp. 219–247.
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  • [3] BEYNON M., CURRY B., MORGAN P., The Dempster-Sahfer theory of evidence: an alternative approach to multicriteria decision modelling, Omega 28, 2000, pp. 37–50.
  • [4] BOEGL K., ADLASSNIG K.P., HAYASHI Y., ROTHENFLUH T.E., LEITICH H., Knowledge acquisition in the fuzzy knowledge representation framework of a medical consultation systems, Artificial Intelligence in Medicine 30, 2004, pp. 1–26.
  • [5] BOLC L., BORODZIEWICZ W., WÓJCIK M., Foundations of processing uncertain and imprecise information, Warsaw, PWN, 1991, (in Polish).
  • [6] CIOS K.J., GOODENDAY L.S., SZTANDERA M., Hybrid Intelligence System for Diagnosing Coronary Stenosis, IEEE Engineering in Medicine and Biology, 1994, Vol. 13, No. 5, pp. 723–729.
  • [7] DAN Q., DUDECK J., Some problems related with probabilistic interpretations for certainty factors, Proc. 5-th Annual IEEE Symposium on Computer-Based Medical Systems, 1992, pp. 538–544.
  • [8] DANIEL M., HAJEK P., NGUYEN P.H., CADIAG-2 and MYCIN-like systems, Artificial Intelligence in Medicine, 1997, Vol. 9, pp. 241–259.
  • [9] DEMPSTER A.P., A generalisation of Bayesian inference, J. Royal Stat. Soc., 1968, pp. 205–247.
  • [10] FISHBURN P.C., The axioms of subjective probability, Statistical Science, 1986, Vol. 1, No. 3, pp. 335–358.
  • [11] GORZAŁCZANY M.B., DEUTSCH-MCLEISH M., Combination of neural networks and fuzzy sets as a basis for medical expert systems, Proc. 5th Annual IEEE Symposium on Computer-Based Medical Systems, Durham, NC, USA, 1992, pp. 412–420.
  • [12] GÓROWSKI T., Thyroid Gland Diseases, PZWL, Warsaw, 1988, (in Polish).
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  • [14] HORN K.A., COMPTON P., LAZARUS L., QUINLAN R., A expert system for the interpretation of thyroid assays in a clinical laboratory, The Australian Computer Journal, 1985, Vol. 17, No. 1, pp. 7–11.
  • [15] Iliad, Windows-Based Diagnostic Decision Support Tools for Internal Medicine, User Manual, Applied Medical Informatics, Salt Lake City, UT, 1994.
  • [16] JACKSON P., Introduction to Expert systems, Addison-Wesley Longman Limited, Harlow, England, 1999.
  • [17] KACPRZYK J., FEDRIZZI M. (eds.), Advances in Dempster-Shafer Theory of Evidence, J. Wiley, New York, 1994.
  • [18] KENTEL E., ARAL M.M., Probabilistic-fuzzy health risk modeling, Stochastic Environment and Risk Assessment, 2004, Vol. 18, pp. 324–338.
  • [19] KUECHMEISTER H., Clinical functional diagnostics, PZWL, Warsaw, 1972, (in Polish).
  • [20] LEITICH H., BOEGL K., KOLOUSEK G., ROTHENFLUH T.E., ADLASSNIG K.P., A fuzzy model of data interpretation for the medical expert sytem MedFrame/CADIAG-4, Proc. 13-th Europ. Meeting on Cybernetics and Systems Research, TRAPPL R. (ed.), Vienna, Austria, 1996, pp. 300–3203.
  • [21] MEDASANI S., KIM J., KRISHNAPURAM R., An overview of membership function generation techniques for pattern recognition, Int. J. of Approximate Reasoning, 1998, Vol. 19, pp. 391–417.
  • [22] MILLER R.A., Medical diagnostic decision support systems - past, present and future: a threaded bibliography and brief commentary, Journal of the American Medical Informatics Association, 1994, Vol. 1, No. 1, pp. 8–27.
  • [23] MYSZKOROWSKI K., ZADROŻNY S., SZCZEPANIAK P.S., Classical and fuzzy databases: models, queries and summaries, Akademicka Oficyna Wydawnicza EXIT, Warsaw, 2008, (in Polish).
  • [24] NAUCK D., KRUSE R., NEFCLASS-X - a soft computing tool to build readable fuzzy classifiers, BT Technol. J., 1998, Vol. 16, No. 3, pp. 180–190.
  • [25] D. NIKOVSKI, Constructing Bayesian networks for Medical diagnosis from incomplete and partially correct statistics, IEEE Trans. on Knowledge and Data Engineering, 2000, Vol. 12, No. 4, pp. 509–516.
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  • [28] SCHUERZ M., HIPF G., GRABNER G., An assessment of different approaches to defining fuzzy membership functions semi-automatically, Proc. ERUDIT-Workshop, Vienna, Austria, 2000, pp. 129–137.
  • [29] SHEN J., SHEN W., SUN H.J., YANG J.Y., Fuzzy neural nets with non-symmetric [[membership functions and applications in signal processing and image analysis, Signal Processing 80, 2000, pp. 965–983.
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  • [31] STRASZECKA E., Building membership functions for medical knowledge representation, Journal of Applied Computer Science, 2003, Vol. 11, No. 2, pp. 55–66.
  • [32] STRASZECKA E., Measures of uncertainty and imprecision in medical diagnosis support, Wyd. Politechniki Śląskiej, 2010.
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  • [34] WEISS S.M., KULIKOWSKI C.A., AMAREL S., SAFIR A., A model-based method for computer-aided medical decision making, Artificial Intelligence, 1987, Vol. 11, pp. 145–172.
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  • [37] ZADEH L.A., Fuzzy Logic, Computer, 1988, Vol. 21, No. 14, pp. 83–93.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA4-0026-0001
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