Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
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).
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.
G.F. Cooper. Bayesian belief-network inference using recursive decomposition. Technical Report KSL-90-05, Section of Medical Informatics, Stanford University, 1990.
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.
J. Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufman Publishers, Inc., 1988.
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.
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.
J.R. Saper et al. Handbook of Headache Management. Williams and Wilkins, 1993.
W.V. Slack. A history of Computerized Medical Interviews. M.D. Computing, 1(5):53–59, 1984.
S. Solomon and S. Fraccaro. The Headache Book. Consumers Union of United States, 1991.
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.
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.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1995 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Carenini, G., Monti, S., Banks, G. (1995). An information-based bayesian approach to history taking. In: Barahona, P., Stefanelli, M., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIME 1995. Lecture Notes in Computer Science, vol 934. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60025-6_131
Download citation
DOI: https://doi.org/10.1007/3-540-60025-6_131
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-60025-1
Online ISBN: 978-3-540-49407-2
eBook Packages: Springer Book Archive