Please use this identifier to cite or link to this item: https://doi.org/10.1016/S1386-5056(98)00085-9
Title: Dynamic decision analysis in medicine: A data-driven approach
Authors: Cao, C. 
Leong, T.-Y. 
Leong, A.P.K.
Seow, F.C.
Keywords: Abstraction
Bayesian learning
Databases
Dynamic decision analysis
Modeling
Issue Date: Jul-1998
Citation: Cao, C., Leong, T.-Y., Leong, A.P.K., Seow, F.C. (1998-07). Dynamic decision analysis in medicine: A data-driven approach. International Journal of Medical Informatics 51 (1) : 13-28. ScholarBank@NUS Repository. https://doi.org/10.1016/S1386-5056(98)00085-9
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.
Source Title: International Journal of Medical Informatics
URI: http://scholarbank.nus.edu.sg/handle/10635/99250
ISSN: 13865056
DOI: 10.1016/S1386-5056(98)00085-9
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