Modelling medical decisions in dynamol: A new general framework of dynamic decision analysis

• Main Authors: Tze-Yun LEONG, Cao, Cungen
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 1998
• Subjects: Bayesian Learning; Dynamic Decision Analysis; Multiple Perspective Modeling; Computer Sciences; Health Information Technology
• Online Access: https://ink.library.smu.edu.sg/sis_research/3026; https://ink.library.smu.edu.sg/context/sis_research/article/4026/viewcontent/LEONGty_1998_DyanMoL.pdf
• Institution: Singapore Management University

Dynamic decision analysis concerns decision problems in which both time and uncertainty are explicitly considered. We present a new dynamic decision analysis framework, called DynamoL, that supports graphical presentation of the decision factors in multiple perspectives. To alleviate the difficulty in assessing conditional probabilities over time in dynamic decision models, DynaMoL incorporates a Bayesian learning system to automatically learn the probabilistic parameters from large medical databases. We describe the DynaMoL modeling and learning architecture through a medical decision problem on the optimal follow-up schedule for patients after curative colorectal cancer surgery. We also show that the modeling experience and results indicate practical promise for the framework. © 1998 IMIA. All rights reserved.