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Tytuł artykułu

The heuristic nature of medical decision making

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Medical knowledge-based systems are increasingly accepted as diagnostic and therapeutic decision making support tools in clinical practice. These systems deal with a particularly complicated problem domain area. For any medical domain encompassing more than a small number of diseases, manifestations, or therapeutic alternatives, such systems will necessarily embody a high degree of complexity. In addition, medical knowledge is uncertain and incomplete in nature, and the concepts used to denote medical entities are vague. To encompass these invariants of medical knowledge, a number of different formal and heuristic models have been developed. This paper provides a brief survey of formal and heuristic approaches including commercially available medical decision support systems applied to knowledge modeling and problem-solving under uncertainty in medicine. As will be demonstrated, also many of these systems involve heuristic principles. In their own research, the authors have focussed on fuzzy-based approaches. It could be demonstrated that fuzzy set theory and fuzzy logic are appropriate tools to deal with the uncertainty, vagueness, and even incompleteness of medical knowledge. The paper describes the basic concepts of fuzzy set theory and gives an update on some recent enhancements and refinements of the fuzzy knowledge and reasoning model. The rationale for these refinements will be discussed.
Rocznik
Strony
5--26
Opis fizyczny
Bibliogr. 38 poz.
Twórcy
autor
  • Section on Medical Expert and Knowledge-Based Systems, Departament of Medical Computer Sciences, University of Vienna, Spitalgasse 23, A-1090 Vienna, Austria
  • Section on Medical Expert and Knowledge-Based Systems, Departament of Medical Computer Sciences, University of Vienna, Spitalgasse 23, A-1090 Vienna, Austria
Bibliografia
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  • [5] Adlassnig К.-P., Kolarz G., Scheithauer W., Grabner H. (1986) Approach to a Hospital-Based Application of a Medical Expert System, Medical Informatics 11,205-223.
  • [6] Adlassnig К.-P., Leitich H., Kolarz G. (1993) On the Applicability of Diagnostic Criteria for the Diagnosis of Rheumatoid Arthritis in an Expert System, Expert Systems with Applications 14,441-448.
  • [7] Aliferis C.F., Miller R.A. (1995) On the Heuristic Nature of Medical Decision Support Systems, Methods of Information in Medicine 34, 5-14.
  • [8] Amaya Cruz G.P., Beliakov G. (1996) On the Interpretation of Certainty Factors in Expert Systems, Artificial Intelligence in Medicine 8, 1-14.
  • [9] Boegl K. (1997) Design and implementation of a web-based knowledge acquisition toolkit for medical expert consultation systems, Doctoral Thesis, Technical University of Vienna, Austria.
  • [10] Boegl K., Kainberger F., Adlassnig K.-P., Kolarz G., Kolousek G., Leitich H., Imhof H. (1995) CADIAG-2/Rheuma-Radio: An Expert System to Assist in Establishing Radiological Diagnoses in Rheumatology. In: Lemke H.U., Inamura K., Jaffe C.C., Vannier M.W. (Eds.) Computer Assisted Radiology: CAR'95, Springer-Verlag Berlin, 330-335.
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  • [12] Brein L., Adlassnig К.-P., Kolousek G. (1997). Rule Base and Inference Process of the Medical Expert System CADIAG-IV. In: Trappl R. (ed.) Proceedings of the European Meeting on Cybernetics and Systems Research - Cybernetics and Systems '98, Austrian Society for Cybernetic Studies, 155-159.
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  • [19] Heckerman D.E., Horvitz E.J., Nathwani B.N. (1992) Toward Normative Expert Systems: Part II- Probability-Based Representations for Efficient Knowledge Acquisition and Inference, Methods of Information in Medicine 31, 106-116.
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  • [24] Pearl J. (1988) Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, Menlo Park.
  • [25] Popie H.E. (1982) Heuristic Methods for Imposing Structure on Ill-Structured Problems: The Structuring of Medical Diagnostics, In: Szolovits P. (ed.) Artificial Intelligence in Medicine, Westview Press, Boulder, 119-190.
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  • [27] Schuh Ch., Koller W., Zelenka Ch., Kolb M., Hiesmayr M., Adlassnig K.-P. (2000) Knowledge Acquisition for Crisp- and Fuzzy-Controlled Weaning in Intensive Care Units. In: Jamshidi M., Borne P., Maciejewski A., Nahavandi S., Lumia R., Fahti M., Furuhashi T. (Eds.) Proc. (CD-ROM-Version) of the World Automation Congress (WAC’2000), Maui, Hawaii, U.S.A., ISSCI045.pdf.
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  • [31] Spies M. (1993) Unsicheres Wissen—Wahrscheinlichkeit, Fuzzy-Logik, neuronale Netze und menschliches Denken, Spektrum Verlag, Heidelberg.
  • [32] Szolovits P. (1995) Uncertainty and Decisions in Medical Informatics, Methods of Information in Medicine 34, 111-121.
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  • [34] Van Bemmel J., Musen M.A. (1998) Handbook of Medical Informatics, Springer Verlag, Berlin.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-LOD7-0028-0009
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