Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A Mathematical Theory of Communication
A Mathematical Theory of Communication
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
European research efforts in medical knowledge-based systems
Artificial Intelligence in Medicine
Editorial: Bayesian networks in biomedicine and health-care
Artificial Intelligence in Medicine
Paper: Specification of models in large expert systems based on causal probabilistic networks
Artificial Intelligence in Medicine
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Causal Probabilistic Networks (CPNs) provide a common framework that unifies the various tasks involved in medical reasoning. These tasks include causal and diagnostic reasoning, simulations, planning of tests and therapies and automatic updating of knowledge. While most of these tasks are quite well understood and have already been described in the literature, planning of therapy and tests is still a relatively unexplored area. In this paper we suggest planning methods that can be directly integrated in the CPN formalism. The methods are based on decision theory, and are realized by associating utilities with selected nodes in the CPN. The methods are simpler than planning methods based on influence diagrams and can be applied to planning problems of non-trivial size. This is illustrated by applying the proposed formalism to planning of insulin therapy in insulin-dependent diabetic patients. Planning of tests is illustrated by a small test/treat example.