Abstract
Probabilistic models such as Bayesian networks [6] are well suited for medical decision support and are the basis of many successful applications [1],[3],[4],[8],[9],[10]. Bayesian networks provide a rigorous and efficient framework for inference, i.e. for calculating the probability of each stochastic variable given a set of observations. However, knowledge acquisition and generation of the network are still demanding tasks when large medical domains have to be modelled.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
S. Andreassen, M. Woldbye, B. Falck, S. K. Andersen: “MUNIN — A Causal Probabilistic Network for Interpretation of Electromyographic Findings”. Proceedings of the Tenth International Joint Conference on Artificial Intelligence, Milan, Italy, August 1987, pp. 366–372.
T. Birkhölzer, M. Haft, R. Hofmann, J. Horn, M. Pellegrino, V. Tresp: “Intelligent Communication in Medical Care”. Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making (AIMDM 99), Aalborg, Denmark, June 1999, p. 4.
D. E. Heckerman, E. J. Horvitz, B. N. Nathwani: “Toward Normative Expert Systems: Part I. The Pathfinnder Project”. Methods of Information in Medicine, Vol. 31, 1992, pp. 90–105.
D. E. Heckerman, B. N. Nathwani: “Toward Normative Expert Systems: Part II. Probability-Based Representations for Efficient Knowledge Acquisition and Inference”. Methods of Information in Medicine, Vol. 31, 1992, pp. 106–116.
J. Horn: HealthMan Bayesian Network Description: Enhancing and Inhibiting Factors of Diseases. Siemens AG, ZT IK 4, Internal Report, 1999.
F. V. Jensen: An Introduction to Bayesian Networks. UCL Press, 1996.
R. Lupas Scheiterer: HealthMan Bayesian Network Description: Disease to Symptom Layer. Siemens AG, ZT IK 4, Internal Report, 1999.
B. Middleton, M. A. Shwe, D. E. Heckerman, M. Henrion, E. J. Horvitz, H. P. Lehmann, G. F. Cooper: “Probabilistic Diagnosis Using a Reformulation of the INTERNIST-1/QMR Knowledge Base. II. Evaluation of Diagnostic Performance”. Methods of Information in Medicine, Vol. 30, 1991, pp. 256–267.
K. G. Olesen, U. Kjaerulff, F. Jensen, F. V. Jensen, B. Flack, S. Andreassen, S. K. Andersen: “A MUNIN Network for the Median Nerve — A Case Study on Loops”. Applied Artificial Intelligence, Vol. 3, 1989, pp. 385–403.
M. A. Shwe, B. Middleton, D. E. Heckerman, M. Henrion, E. J. Horvitz, H. P. Lehmann, G. F. Cooper: “Probabilistic Diagnosis Using a Reformulation of the INTERNIST-1/QMR Knowledge Base. I. The Probabilistic Model and Inference Algorithms”. Methods of Information in Medicine, Vol. 30, 1991, pp. 241–250.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Horn, J. et al. (2001). Knowledge Acquisition and Automated Generation of Bayesian Networks for a Medical Dialogue and Advisory System. In: Quaglini, S., Barahona, P., Andreassen, S. (eds) Artificial Intelligence in Medicine. AIME 2001. Lecture Notes in Computer Science(), vol 2101. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48229-6_28
Download citation
DOI: https://doi.org/10.1007/3-540-48229-6_28
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42294-5
Online ISBN: 978-3-540-48229-1
eBook Packages: Springer Book Archive