Dynamic decision analysis in medicine: A data-driven approach - Cao C. (Institutional Knowledge at Singapore Management University, 1998)
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.