Dynamic decision analysis in medicine: A data-driven approach
Publication Type
Journal Article
Publication Date
7-1998
Abstract
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.
Keywords
Abstraction, Bayesian learning, Databases, Dynamic decision analysis, Modeling
Discipline
Databases and Information Systems | Health Information Technology
Publication
International Journal of Medical Informatics
Volume
51
Issue
1
First Page
13
Last Page
28
ISSN
1386-5056
Identifier
10.1016/S1386-5056(98)00085-9
Publisher
Elsevier
Citation
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.
Available at: https://ink.library.smu.edu.sg/sis_research/3012