Influence Diagrams for Neonatal Jaundice Management
AIMDM '99 Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making
Knowledge Organisation in a Neonatal Jaundice Decision Support System
ISMDA '01 Proceedings of the Second International Symposium on Medical Data Analysis
Efficient non-myopic value-of-information computation for influence diagrams
International Journal of Approximate Reasoning
Dealing with complex queries in decision-support systems
Data & Knowledge Engineering
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In this paper, we introduce evidence propagation operations on influence diagrams, a concept of the value of evidence to measure the impact/value of new observations/experimentation, and a concept of the value of revelation. Evidence propagation operations are critical for the computation of the value of evidence, general update and inference operations in normative expert systems that are based on the influence diagram (generalized Bayesian network) paradigm. The value of evidence allows us to compute the outcome sensitivity directly defined as the maximum difference among the values of evidence, and the value of perfect information, as the expected value of the values of evidence. We define the value of revelation as the optimal value of the values of evidence. We discuss the relationship between the value of revelation and the value of control. We also discuss implementation issues related to computation of the value of evidence and the value of perfect information.