Title

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

Research Areas

Intelligent Systems and Decision Analytics

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

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