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Diagnostic Rules of Increased Reliability for Critical Medical Applications

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Artificial Intelligence in Medicine (AIMDM 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1620))

Abstract

This paper presents a novel approach to the construction of reliable diagnostic rules from the available cases with known diagnoses. It proposes a simple and general framework based on the generation of the so-called confirmation rules. A property of a system of confirmation rules is that it allows for indecisive answers, which, as a consequence, enables that all decisive answers proposed by the system are reliable. Moreover, the consensus of two or more confirmation rules additionally increases the reliability of diagnostic answers. Experimental results in the problem of coronary artery disease diagnosis illustrate the approach.

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References

  1. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen and A.I. Verkamo (1996) Fast discovery of association rules. In U.M. Fayyad, G. Piatetski-Shapiro, P. Smyth and R. Uthurusamy (Eds.) Advances in Knowledge Discovery and Data Mining, 307–328. AAAI Press.

    Google Scholar 

  2. D. Gamberger (1995) A minimization approach to propositional inductive learning. In Proc. Eighth European Conference on Machine Learning, 151–160, Springer Lecture Notes in AI 912.

    Google Scholar 

  3. M. Gams (1989) New measurements highlight the importance of redundant knowledge. In Proc. European Working Session on Learning, 71–80. Pitman.

    Google Scholar 

  4. C. Grošelj, M. Kukar, J.J. Fetich and I. Kononenko (1997) Machine learning improves the accuracy of coronary artery disease diagnostic methods. Computers in Cardiology, 24:57–60.

    Google Scholar 

  5. R.S. Michalski, I. Mozetič, J. Hong, and N. Lavrač (1986) The multi-purpose incremental learning system AQ15 and its testing application on three medical domains. In Proc. Fifth National Conference on Artificial Intelligence, 1041–1045, Morgan Kaufmann.

    Google Scholar 

  6. J.R. Quinlan (1996) Boosting, bagging, and C4.5. In Proc. Thirteenth National Conference on Artificial Intelligence, 725–730, AAAI Press.

    Google Scholar 

  7. R.L. Rivest and R. Sloan (1988) Learning complicated concepts reliably and use-fully. In Proc. Workshop on Computational Learning Theory, 69–79, Morgan Kaufman.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Rudjer Bošković Institute, Bijenička 54, 10000, Zagreb, Croatia

    Dragan Gamberger

  2. Jožef Stefan Institute, Jamova 39, 1000, Ljubljana, Slovenia

    Nada Lavrač

  3. Nuclear Medicine Department, University Medical Centre Ljubljana, Zaloška 7, 1000, Ljubljana, Slovenia

    Ciril Grošelj

Authors
  1. Dragan Gamberger
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  2. Nada Lavrač
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  3. Ciril Grošelj
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Editor information

Editors and Affiliations

  1. Austrian Research Institute for Artificial Intelligence, Schottengasse 3, A-1010, Vienna, Austria

    Werner Horn

  2. Department of Medicine and Computer Science, Stanford University, 251 Campus Drive, Stanford, CA, 94305-5479, USA

    Yuval Shahar

  3. Department of Medicine Karolinska Institutet, Huddinge University Hospital, Sweden

    Greger Lindberg

  4. Department of Medical Informatics and Image Analysis, Aalborg University, Fredrik Bajers Vej 7D, DK-9000, Aalborg, Denmark

    Steen Andreassen

  5. School of Public Policy, University College London, 29/30 Tavistock Square, London, WC1H 9EZ, UK

    Jeremy Wyatt

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© 1999 Springer-Verlag Berlin Heidelberg

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Gamberger, D., Lavrač, N., Grošelj, C. (1999). Diagnostic Rules of Increased Reliability for Critical Medical Applications. In: Horn, W., Shahar, Y., Lindberg, G., Andreassen, S., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIMDM 1999. Lecture Notes in Computer Science(), vol 1620. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48720-4_39

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  • DOI: https://doi.org/10.1007/3-540-48720-4_39

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66162-7

  • Online ISBN: 978-3-540-48720-3

  • eBook Packages: Springer Book Archive

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