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Dynamic decision analysis in medicine: a data-driven approach.

C Cao·T Y Leong·A P Leong·F C Seow
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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.

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Bayes TheoremColonic NeoplasmsComputer SimulationData Interpretation, StatisticalDatabases, FactualDecision Making, Computer-AssistedHumansPatient Care PlanningJournal ArticleResearch Support, Non-U.S. Gov't

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C Cao, T Y Leong, A P Leong, F C Seow. 1998. Dynamic decision analysis in medicine: a data-driven approach.. https://doi.org/10.1016/s1386-5056(98)00085-9

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