{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T17:55:31Z","timestamp":1777917331504,"version":"3.51.4"},"reference-count":37,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[1997,7,1]],"date-time":"1997-07-01T00:00:00Z","timestamp":867715200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Med Decis Making"],"published-print":{"date-parts":[[1997,7]]},"abstract":"<jats:p>Influence diagrams are a powerful graphic representation for decision models, complementary to decision trees. Influence diagrams and decision trees are different graphic representations for the same underlying mathematical model and operations. This article describes the elements of an influence diagram, and shows several familiar decision problems represented as decision trees and as influence diagrams. The authors also contrast the information highlighted in each graphic representation, demonstrate how to calculate the expected utilities of decision alternatives modeled with an influence diagram, provide an overview of the conceptual basis of the solution algorithms that have been developed for influence diagrams, discuss the strengths and limitations of influence diagrams relative to decision trees, and describe the mathematical operations that are used to evaluate both decision trees and influence diagrams. They use clinical examples to illustrate the mathematical operations of the influence-diagram-evaluation algorithm; these operations are arc reversal, chance node removal by averaging, and decision node removal by policy determination. Influence diagrams may be helpful when problems have a high degree of conditional independence, when large models are needed, when communication of the probabilistic relationships is important, or when the analysis requires extensive Bayesian updating. The choice of graphic representation should be governed by convenience, and will depend on the problem being analyzed, on the experience of the analyst, and on the background of the consumers of the analysis. Key words: decision analysis; influence diagrams; cost-effectiveness analysis; Bayesian updating; graphic representation. (Med Decis Making 1997;17:241-262)<\/jats:p>","DOI":"10.1177\/0272989x9701700301","type":"journal-article","created":{"date-parts":[[2007,3,4]],"date-time":"2007-03-04T20:41:53Z","timestamp":1173040913000},"page":"241-262","source":"Crossref","is-referenced-by-count":65,"title":["Representation and Analysis of Medical Decision Problems with Influence Diagrams"],"prefix":"10.1177","volume":"17","author":[{"given":"Douglas K.","family":"Owens","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ross D.","family":"Shachter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"suffix":"JR","given":"Robert F.","family":"Nease","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[1997,7,1]]},"reference":[{"key":"atypb1","doi-asserted-by":"publisher","DOI":"10.21236\/ADA026379"},{"key":"atypb2","unstructured":"Shachter RD, Eddy DM, Hasselblad V. An influence diagram approach to the confidence profile method for health technology assessment Proceedings of the Conference on Influence Diagrams for Decision Analysis, Inference and Prediction. University of California, Berkeley, CA, 1988:299-306."},{"key":"atypb3","volume-title":"Meta-Analysis by the Confidence Profile Method The Statistical Synthesis of Evidence","author":"Eddy DM","year":"1992"},{"key":"atypb4","unstructured":"Lehmann HP, Shortliffe EH. THOMAS Building Bayesian statistical expert systems to aid in clinical decision making In. Miller RA (ed) Proceedings of the Symposium on Computer Applications in Medical Care Los Alamitos, CA: IEEE Computer Society Press, 1990:58-64."},{"key":"atypb5","volume-title":"Computational formulation of Bayesian statistical models for interpreting clinical research literature [PhD thesis] Section on Medical Informatics","author":"Lehmann HP","year":"1991"},{"key":"atypb6","volume-title":"A representation for gaining insight into clinical decision models [PhD thesis] Section on Medical Informatics","author":"Jimison HB.","year":"1990"},{"key":"atypb7","doi-asserted-by":"publisher","DOI":"10.1016\/S1070-3241(16)30005-0"},{"key":"atypb8","volume-title":"Explanation in Bayesian belief networks [PhD thesis] Section on Medical Informatics","author":"Suermondt HJ.","year":"1991"},{"key":"atypb9","doi-asserted-by":"publisher","DOI":"10.1177\/0272989X9701700302"},{"key":"atypb10","first-page":"1","volume":"44","author":"Centers for Disease Control.","year":"1995","journal-title":"MMWR"},{"key":"atypb11","first-page":"50","author":"Charniak E.","year":"1991","journal-title":"AI Magazine"},{"key":"atypb12","doi-asserted-by":"publisher","DOI":"10.1055\/s-0038-1634846"},{"key":"atypb13","doi-asserted-by":"publisher","DOI":"10.1055\/s-0038-1634847"},{"key":"atypb14","doi-asserted-by":"publisher","DOI":"10.1001\/jama.1996.03530410056034"},{"key":"atypb15","doi-asserted-by":"publisher","DOI":"10.1001\/archinte.1996.00440040066007"},{"key":"atypb16","doi-asserted-by":"publisher","DOI":"10.7326\/0003-4819-122-9-199505010-00001"},{"key":"atypb17","volume-title":"Decision Analysis","author":"Raiffa H","year":"1968"},{"key":"atypb18","doi-asserted-by":"publisher","DOI":"10.1002\/net.3230200505"},{"key":"atypb19","unstructured":"Howard RA, Matheson JE. 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