An information-based bayesian approach to history taking

  • Giuseppe Carenini
  • Stefano Monti
  • Gordon Banks
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 934)


Effective history-taking systems need to dynamically reduce the number of questions to ask. This can be done either categorically or probabilistically, by exploiting previous patient's answers. In this paper, we propose a probabilistic information-based history-taking strategy that combines synergistically two information-content measures for reducing the number of questions asked. We have applied this strategy to an existing history-taking system and some preliminary results seem to confirm our initial intuitions.


Disease Node History Taker Tension Headache Ambiguous Case Initial Intuition 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    B. G. Buchanan, J. Moore, D. Forsythe, G. Carenini, G. Banks, and S. Ohlsson. Using medical informatics for explanation in the clinical setting. Technical Report CS-93-16, University of Pittsburgh, 1993. (To appear in the AI in Medicine journal).Google Scholar
  2. 2.
    G.F. Cooper. Current Research Direction in the Development of Expert Systems based on Belief Networks. Applied Stochastic Models and Data Analysis, 5:39–52, 1989.Google Scholar
  3. 3.
    G.F. Cooper. Bayesian belief-network inference using recursive decomposition. Technical Report KSL-90-05, Section of Medical Informatics, Stanford University, 1990.Google Scholar
  4. 4.
    D.E. Heckerman, E.J. Horwitz, and B.N. Nathwani. Toward Normative Expert Systems: Part I The Pathfinder Project. Methods of Information in Medicine, 31(2):90–105, 1992.PubMedGoogle Scholar
  5. 5.
    J. Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufman Publishers, Inc., 1988.Google Scholar
  6. 6.
    A.D. Poon, K.B. Johnson, and L.M Fagan. Augmented Transition Networks as a Representation for Knowledge-Based History-Taking Systems. In Proceedings of the 16th Symposium of Computer Applications in Medical Care, pages 762–766, 1992.Google Scholar
  7. 7.
    M. Pradhan et al. Knowledge Engineering for Large Belief Networks. In Proceedings of the 10th Conference on Uncertainty in Artificial Intelligence, pages 484–490, San Francisco, California, 1994. Morgan Kaufmann Publishers.Google Scholar
  8. 8.
    J.R. Saper et al. Handbook of Headache Management. Williams and Wilkins, 1993.Google Scholar
  9. 9.
    W.V. Slack. A history of Computerized Medical Interviews. M.D. Computing, 1(5):53–59, 1984.Google Scholar
  10. 10.
    S. Solomon and S. Fraccaro. The Headache Book. Consumers Union of United States, 1991.Google Scholar
  11. 11.
    S. Srinivas. A Generalization of the Noisy-OR Model. In Proceedings of the 9th Conference on Uncertainty in Artificial Intelligence, pages 208–215, Washington D.C., 1993. Morgan Kaufmann Publishers.Google Scholar
  12. 12.
    H.R. Warner, B. Rutheford, and B. Houtchens. A Sequential Bayesian Approach to History Taking and Diagnosis. Computers and Biomedical Research, 5:256–262, 1972.PubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Giuseppe Carenini
    • 1
  • Stefano Monti
    • 1
  • Gordon Banks
    • 2
  1. 1.Intelligent Systems ProgramUniversity of PittsburghPittsburgh
  2. 2.Department of NeurologyUniversity of PittsburghPittsburgh

Personalised recommendations