Abstract
Almost two decades after the introduction of probabilistic expert systems, their theoretical status, practical use, and experiences are matching those of rule-based expert systems. Since both types of systems are in wide use, it is more than ever important to understand their advantages and drawbacks. We describe a study in which we compare rule-based systems to systems based on Bayesian networks. We present two expert systems for diagnosis of liver disorders that served as the inspiration and vehicle of our study and discuss problems related to knowledge engineering using the two approaches. We finally present the results of a simple experiment comparing the diagnostic performance of each of the systems on a subset of their domain.
The following grants supported our work: KBN 8T11E02917, W/II/1/00, AFOSR F49620-00-1-0112, NSF IRI-9624629, NATO PST.CLG.976167.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
B.G. Buchanan and E.H. Shortliffe (Eds.). Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Addison-Wesley, Reading, MA, 1984.
D. Heckerman. Probabilistic interpretations for Mycin’s certainty factors. In: L.N. Kanal and J.F. Lemmer (Eds.). UAI 1 (Elsevier, NY, 1986) 167–196.
M. Henrion. Some practical issues in constructing belief networks. In: L.N. Kanal, T.S. Levitt, and J.F. Lemmer (Eds.). UAI 3 (Elsevier, NY, 1989) 161–173.
P.J.F. Lucas. Refinement of the HEPAR expert system: tools and techniques. Artificial Intelligence in Medicine 6(2) (1994) 175–188.
P.J.F. Lucas. Symbolic diagnosis and its formalisation. The Knowledge Engineering Review 12(2) (1997) 109–146.
P.J.F. Lucas. Certainty-like structures in Bayesian belief networks. Knowledge-based Systems (2001).
P.J.F. Lucas and A.R. Janssens. Development and validation of HEPAR. Medical Informatics 16(3) (1991) 259–270.
P.J.F. Lucas, R.W. Segaar, and A.R. Janssens. HEPAR: an expert system for diagnosis of disorders of the liver and biliary tract. Liver 9 (1989) 266–275.
A. Newell and H.A. Simon. Human Problem Solving. Prentice-Hall, Englewood Cliffs, NJ, 1972.
A. Oniésko, M.J. Druzdzel, and H. Wasyluk. Extension of the Hepar II model to multiple-disorder diagnosis. In: S.T. Wierzchoén M. Kłopotek, M. Michalewicz (Eds.). Intelligent Information Systems. Advances in Soft Computing Series (Physica-Verlag Heidelberg, 2000) 303–313.
J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo, CA, 1988.
M. Peot and R. Shachter. Learning from what you don’t observe. In: UAI-98 (Morgan Kaufmann, San Francisco, CA, 1998) 439–446.
L.C. van der Gaag. Probability-Based Models for Plausible Reasoning. PhD thesis, University of Amsterdam, Amsterdam, The Netherlands, 1990.
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
Oniésko, A., Lucas, P., Druzdzel, M.J. (2001). Comparison of Rule-Based and Bayesian Network Approaches in Medical Diagnostic Systems. 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_40
Download citation
DOI: https://doi.org/10.1007/3-540-48229-6_40
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