Fusion, propagation, and structuring in belief networks
Artificial Intelligence
A theory of diagnosis from first principles
Artificial Intelligence
Artificial Intelligence
Medical diagnosis using a probabilistic causal network
Applied Artificial Intelligence
HUGIN: a shell for building Bayesian belief universes for expert systems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Expert Systems with Applications: An International Journal
A model-based approach to the diagnosis of the cardiac arrhythmias
Artificial Intelligence in Medicine
Paper: Graphical knowledge acquisition for medical diagnostic expert systems
Artificial Intelligence in Medicine
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This paper reports on the Heart Failure Program, which uses multiple models and multiple reasoning operators to provide patient management information for physicians. The program uses a causal probabilistic knowledge base of pathophysiology for reasoning diagnostically, a quantitative physiologic model for reasoning about the effects of interventions, and a case base for an alternate form of diagnostic reasoning. Using these knowledge bases are reasoning operators to turn patient data into evidence for causal reasoning, using the evidence to assert specific physiologic states, generating a diagnosis or differential from the causal knowledge base or from the case base, using the current diagnostic state to determine what further information would be useful, using the diagnostic state to suggest therapies, predicting the possible effects of therapies, and using the diagnostic hypotheses or the effect predictions to generate graphical explanations for the user. By combining the models and reasoning methods, each potential use of the program benefits because each operator provides conclusions that simplify the task of other operators. The result is a program with uses in many phases of the patient management process.