Dynamic decision analysis in medicine: A data-driven approach
Dynamic decision analysis concerns decision problems in which both time and uncertainty are explicitly considered. Two major challenges in dynamic decision analysis are on proper formulation of a model for the problem and effective elicitation of the numerous time-dependent conditional probabilities for the model. Based on a new, general dynamic decision modeling framework called DynaMoL (Dynamic decision Modeling Language), we propose a data-driven approach to addressing these issues. Our approach uses available problem data from large medical databases, guides the decision modeling at a proper level of abstraction and establishes a Bayesian learning method for automatic extraction of the probabilistic parameters. We demonstrate the theoretical implications and practical promises of this new approach to dynamic decision analysis in medicine through a comprehensive case study in the optimal follow-up of patients after curative colorectal cancer surgery.
Abstraction, Bayesian learning, Databases, Dynamic decision analysis, Modeling
Databases and Information Systems | Health Information Technology
Intelligent Systems and Decision Analytics
International Journal of Medical Informatics
Cao C., Tze-Yun LEONG, Leong A., and Seow F..
Dynamic decision analysis in medicine: A data-driven approach. (1998). International Journal of Medical Informatics. 51, (1), 13-28. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3012
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