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Medical diagnosis support by the application of associational cognitive maps

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The objective of the presented research is to construct a model of a patient's health that is based on the idea of cognitive map, a graphical knowledge-representation tool. The application of the proposed model for medical diagnosis is the practical goal of the research. Initially, we provide a brief review of the related works on medical decision support systems and cognitive maps. Afterwards, we sketch the general idea of the conceptual approach to the representation of medical knowledge and provide a new formulation of the medical diagnosis problem. Then, we define our model based on associational cognitive maps and show how it can be applied to diagnosis support. Due to the relative ease of understanding of cognitive map, the model can be easily interpreted and used, thereby making medical knowledge widely available through computer consultation systems. The application example presented is based on a relatively simple, real medical case.
Słowa kluczowe
Rocznik
Strony
439--456
Opis fizyczny
Bibliogr. 26 poz., rys.
Twórcy
autor
  • Institute of Computer Science, Silesian University, Sosnowiec, Poland
Bibliografia
  • ADLASSNIG, K.-P., COMBI, C., DAS, A.K., KERAVNOU, E.T. and POZZI,G. (2006) Temporal representation and reasoning in medicine: Research directions and challenges. Artificial Intelligence in Medicine 38 (2), 101-113.
  • AGRAWAL, R. and IMIELINSKI, T. (1993) Mining association rules between sets of items in large databases. In: Proc. of 1993 ACM-SIGMOD Int. Conference on Management of Data. ACM Press, 207-216.
  • AGUILAR, J. (2005) Survey about Fuzzy Cognitive Maps Papers. International Journal of Computational Cognition 3 (2).
  • AXELROD, R. (1976) Structure of Decision - The Cognitive Maps of Political Elites. Princeton University Press.
  • BEALE, T. and HEARD, S., eds. (2007) Open EHR, architecture overview. http://www. openehr.org/svn/specification/TA GS/Release- I.O.I/ publishing/architecture/overview.pdf.
  • BUCHANAN, B.C. and SHORTLIFFE, E.H. (1984) Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. AAAI Press.
  • CHEN, H., FULLER, S.S. and FRIEDMAN, C.P. eds. (2005) Medical Informatics: Knowledge Management and Data Mining in Biomedicine. Integrated Series in Information Systems, Springer.
  • GEORGOPOULOS, V.C., MALANDRAKI, G.A. and STYLIOS, C.D. (2003) A fuzzy cognitive map approach to differential diagnosis of specific language impairment. Artificial Intelligence in Medicine, 29(3), 261-278.
  • GEORGOPOULOS, V.C. and STYLIOS, C.D. (2005) Augmented Fuzzy Cognitive Maps Supplemented with Case Base Reasoning for Advanced Medical Decision Support. In: M. Nikravesh, L.A. Zadeh and J. Kacprzyk, eds., Soft Computing for Information Processing. Springer, 389-398.
  • HUERGA, A.V. (2002) A balanced differential learning algorithm in fuzzy cognitive maps. In: Proceedings of the 16th International Workshop on Qualitative Reasoning.
  • INNOCENT, P.R. and JOHN, R.I. (2004) Computer aided fuzzy medical diagnosis. Inf. Sci. 162 (2), 81-104.
  • KOSKO, B. (1986) Fuzzy Cognitive Maps. International Journal of Man-Machine Studies 24(1), 65-75.
  • KUNCHEVA, L., ZLATEV, R.Z., NESHKOVA, S.N. and CAMPER, J. (1993) A combination Scheme of Artificial Intelligence and Fuzzy Pattern Recognition in Medical Diagnosis. In: FLAI ‘93: Proceedings of the 8th Austrian Artificial Intelligence Conference on Fuzzy Logic in Artificial Intelligence. Springer-Verlag, London, UK, 157-164.
  • LIN, F., YING, H., MACARTHUR, R.D., COHN, J.A., BARTH-JONES, D.C. and CRANE, L.R. (2007) Decision making in fuzzy discrete event systems. Inf. Sci., 177 (18), 3749-3763.
  • McCRAY, A.T. (2003) An Upper Level Ontology for the Biomedical Domain. Comp. Fund. Genom. I (4), 80-84.
  • OKEEF’E, J. and NADEL, L. (1978) The Hippocampus as Cognitive Map. Clarendon Press, Oxford.
  • PARK, K.S. and KIM, S.H. (1995) Fuzzy cognitive maps considering time re-lationships. International Journal Human-Computer Studies 42, 157-168.
  • PEARL, J. (2000) Causality, Model Reasoning and Inference. Cambridge University Press.
  • STACK, W., KURGAN,L.A., PEDRYCZ,W. and REFORMAT, M. (2006) Higher-order Fuzzy Cognitive Maps. In: NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society, 166-171.
  • STEIMANN, F. (1997) Fuzzy set theory in medicine. Artificial Intelligence in Medicine, 11(1), 1-7.
  • STEIMANN, F. (2001) Survey on the use and usefulness of fuzzy sets in medical AI. Artificial Intelligence in Medicine 21 (1), 131-137.
  • STRASZECKA, E. (2006) Combining uncertainty and imprecision in models of medical diagnosis. Inf. Sci., 176(20), 3026-3059.
  • STYLIOS, C.D. and GEORGOPOULOS, V.C. (2008) Fuzzy Cognitive Maps Structure for Medical Decision Support Systems. Studies in Fuzziness and Soft Computing 218, 151-174.
  • TAMIR, D.E. and KANDEL, A. (1995) Fuzzy Semantic Analysis and Formal Specification of Conceptual Knowledge. Inf. Sci. 82 (3-4), 181-196.
  • TOLMAN, E.G. (1948) Cognitive Maps in Rats and Men. The Psychological Review 55 (4), 189-208.
  • ZHOU, L. and HRIPCSAK, G. (2007) Temporal reasoning with medical data-A review with emphasis on medical natural language processing. Journal of Biomedical Informatics 40 (2), 183-202.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT5-0055-0011
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