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.
Cao
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Department of Information Systems and Computer Science, National University of Singapore, Singapore.
Leong
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Leong
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Seow
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D001499:Bayes Theorem; D003110:Colonic Neoplasms / Q000601:surgery*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D003658:Decision Making, Computer-Assisted; D006801:Humans; D010347:Patient Care Planning