Please use this identifier to cite or link to this item: https://doi.org/10.3233/978-1-60750-896-0-483
Title: Modelling medical decisions in dynamol: A new general framework of dynamic decision analysis
Authors: Leong, T.-Y. 
Cao, C. 
Keywords: Bayesian Learning
Dynamic Decision Analysis
Multiple Perspective Modeling
Issue Date: 1998
Citation: Leong, T.-Y.,Cao, C. (1998). Modelling medical decisions in dynamol: A new general framework of dynamic decision analysis. Studies in Health Technology and Informatics 52 : 483-487. ScholarBank@NUS Repository. https://doi.org/10.3233/978-1-60750-896-0-483
Abstract: 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.
Source Title: Studies in Health Technology and Informatics
URI: http://scholarbank.nus.edu.sg/handle/10635/99557
ISBN: 9051994079
ISSN: 09269630
DOI: 10.3233/978-1-60750-896-0-483
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