Conference Proceeding Article
Dynamic decision analysis concerns decision problems in which both time and uncertainty are explicitly considered. We present a new dynamic decision analysis framework, called DynamoL, that supports graphical presentation of the decision factors in multiple perspectives. To alleviate the difficulty in assessing conditional probabilities over time in dynamic decision models, DynaMoL incorporates a Bayesian learning system to automatically learn the probabilistic parameters from large medical databases. We describe the DynaMoL modeling and learning architecture through a medical decision problem on the optimal follow-up schedule for patients after curative colorectal cancer surgery. We also show that the modeling experience and results indicate practical promise for the framework. © 1998 IMIA. All rights reserved.
Bayesian Learning, Dynamic Decision Analysis, Multiple Perspective Modeling
Computer Sciences | Health Information Technology
Data Management and Analytics
MedInfo' 98: 9th World Congress on Medical Informatics
City or Country
Tze-Yun LEONG and Cao, Cungen.
Modelling medical decisions in dynamol: A new general framework of dynamic decision analysis. (1998). MedInfo' 98: 9th World Congress on Medical Informatics. 52, 483-487. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3026
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