Dynamic decision analysis in medicine: A data-driven approach

• Main Authors: Cao C., Tze-Yun LEONG, Leong A., Seow F.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 1998
• Subjects: Abstraction; Bayesian learning; Databases; Dynamic decision analysis; Modeling; Databases and Information Systems; Health Information Technology
• Online Access: https://ink.library.smu.edu.sg/sis_research/3012
• Institution: Singapore Management University

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.