Abstract:
We address practical issues concerning the construction and use of decision-theoretic
or normative expert systems for diagnosis. In particular, we examine Pathfinder, a
normative expert system that assists surgical pathologists with the diagnosis of lymph-node
diseases, and discuss the representation of dependencies among pieces of evidence
within this system. We describe the belief network, a graphical representation of
probabilistic dependencies. We see how Pathfinder uses a belief network to construct
differential diagnosis efficiently, even when there are dependencies among pieces
of evidence. In addition, we introduce an extension of the belief-network representation
called a similarity network, a tool for constructing large and complex belief networks.
The representation allows a user to construct independent belief networks for subsets
of a given domain. A valid belief network for the entire domain can then be constructed
from the individual belief networks. We also introduce the partition, a graphical
representation that facilitates the assessment of probabilities associated with a
belief network. We show that the similarity-network and partition representations
made practical the construction of Pathfinder.
Key-Words
Expert Systems - Diagnosis - Probability Theory - Decision Theory - Artificial Intelligence
- Belief Networks - Similarity Networks - Partitions - Pathology
