Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Extension of the HEPAR II Model to Multiple-Disorder Diagnosis
Proceedings of the IIS'2000 Symposium on Intelligent Information Systems
Certainty-Factor-Like Structures in Bayesian Networks
AI*IA '99 Proceedings of the 6th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Symbolic diagnosis and its formalisation
The Knowledge Engineering Review
Human Problem Solving
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
Learning from what you don't observe
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Methodological Review: A review of causal inference for biomedical informatics
Journal of Biomedical Informatics
Clinical reasoning learning with simulated patients
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
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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.