Abstraction in Belief Networks: The Role of Intermediate States in Diagnostic Reasoning
This work addresses diagnostic reasoning in medical AI, but it appears incremental as it builds on existing belief network methods.
The paper investigates how including intermediate pathophysiological states in Bayesian belief networks affects diagnostic performance for liver and bile disease, finding that networks with intermediate nodes show differences compared to abstracted two-level networks.
Bayesian belief networks are bing increasingly used as a knowledge representation for diagnostic reasoning. One simple method for conducting diagnostic reasoning is to represent system faults and observations only. In this paper, we investigate how having intermediate nodes-nodes other than fault and observation nodes affects the diagnostic performance of a Bayesian belief network. We conducted a series of experiments on a set of real belief networks for medical diagnosis in liver and bile disease. We compared the effects on diagnostic performance of a two-level network consisting just of disease and finding nodes with that of a network which models intermediate pathophysiological disease states as well. We provide some theoretical evidence for differences observed between the abstracted two-level network and the full network.