1992/01/01 by E. D. Heckerman, N. B. Nathwani
Biochemistry, Genetics and Molecular Biology · Computer Science · #AI-based Problem Solving and Planning #Bayesian Modeling and Causal Inference #Biomedical Text Mining and Ontologies
paper · doi:10.1055/s-0038-1634868
crossref issued 1992/01/01 · crossref published 1992/01/01 · crossref published-print 1992/01/01 · openalex publication_date 1992/01/01 · openalex created_date 2016/06/24 · crossref published-online 2018/02/08 · crossref created 2018/03/21 · crossref deposited 2018/03/21 · crossref indexed 2026/07/28 · openalex updated_date 2026/08/04
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.