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Extension of the Hepar II Model to Multiple-Disorder Diagnosis

  • Agnieszka Oniśko
  • Marek J. Druzdzel
  • Hanna Wasyluk
Part of the Advances in Soft Computing book series (AINSC, volume 4)

Abstract

The Hepar II system is based on a Bayesian network model of a subset of the domain of hepatology in which the structure of the network is elicited from an expert diagnostician and the parameters are learned from a database of medical cases. The model follows the assumption made in the database that each patient case is diagnosed with a single disorder, i.e., disorders are mutually exclusive.

In this paper, we describe an extension of the Hepar II system to multiple-disorder diagnosis. We show that our network transforms readily to a network that can perform multiple-disorder diagnosis with some benefits to the quality of numerical parameters learned from the database. We demonstrate empirically that the diagnostic performance in terms of single-disorder diagnosis improves under this transformation. The new model is more realistic and we expect that it will be of higher value in clinical practice.

Keywords

Bayesian Network Numerical Parameter Liver Disorder Conditional Probability Distribution Bayesian Network Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Bobrowski, L. (1992): Hepar: Computer system for diagnosis support and data analysis. Prace IBIB 31, Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, PolandGoogle Scholar
  2. 2.
    Diez, F. J. (1993): Parameter adjustment in Bayes networks. The generalized Noisy-OR gate. In: Proceedings of the 9th Annual Conference on Uncertainty in Artificial Intelligence (UAI-93), Washington, D.C., 99–105Google Scholar
  3. 3.
    Henrion, M. (1989): Some practical issues in constructing belief networks. In: Kanal, L. N., Levitt, T. S., Lemmer J. F., editors, Uncertainty in Artificial Intelligence 3, Elsevier Science Publishers B.V., North Holland, 161–173Google Scholar
  4. 4.
    Howard, R. A., Matheson, J. E. (1984): Influence diagrams. In: Howard, R. A., Matheson, J. E., editors, The Principles and Applications of Decision Analysis, Strategic Decisions Group, Menlo Park, CA, 719–762Google Scholar
  5. 5.
    Moore A. W., Lee M. S. (1994): Efficient algorithms for minimizing cross validation error. In: Proceedings of the 11th International Conference on Machine Learning, Morgan Kaufmann, San FranciscoGoogle Scholar
  6. 6.
    Oniśko, A., Druzdzel, M. J., Wasyluk H. (1997): Application of Bayesian belief networks to diagnosis of liver disorders. In: Proceedings of the 3rd Conference on Neural Networks and Their Applications, Kule, Poland, 730–736Google Scholar
  7. 7.
    Oniśko, A., Druzdzel, M. J., Wasyluk H. (1998): A probabilistic causal model for diagnosis of liver disorders. In: Proceedings of the 7th International Symposium on Intelligent Information Systems (IIS-98), Malbork, Poland, 379–387Google Scholar
  8. 8.
    Oniśko, A., Druzdzel, M. J., Wasyluk H. (1999): A Bayesian network model for diagnosis of liver disorders. In: Proceedings of the 11th Conference on Biocybernetics and Biomedical Engineering, volume 2, Warszawa, Poland, 842-846Google Scholar
  9. 9.
    Pearl J. (1988): Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, Inc., San Mateo, CAGoogle Scholar
  10. 10.
    Wasyluk, H. (1995): The four year’s experience with HEPAR-computer assisted diagnostic program. In: Proceedings of the 8th World Congress on Medical Informatics (MEDINFO-95), Vancouver, BC, Canada, 1033–1034Google Scholar

Copyright information

© Physica-Verlag Heidelberg 2000

Authors and Affiliations

  • Agnieszka Oniśko
    • 1
  • Marek J. Druzdzel
    • 2
  • Hanna Wasyluk
    • 3
  1. 1.Institute of Computer ScienceBiałystok University of TechnologyBiałystokPoland
  2. 2.Decision Systems Laboratory, School of Information Sciences, Intelligent Systems Program, and Center for Biomedical InformaticsUniversity of PittsburghPittsburghUSA
  3. 3.The Medical Center of Postgraduate Education, and Institute of Biocybernetics and Biomedical EngineeringPolish Academy of SciencesWarsawPoland

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