Publication Type

Conference Proceeding Article

Publication Date

8-1998

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.

Keywords

Bayesian Learning, Dynamic Decision Analysis, Multiple Perspective Modeling

Discipline

Computer Sciences | Health Information Technology

Research Areas

Data Management and Analytics

Publication

MedInfo' 98: 9th World Congress on Medical Informatics

Volume

52

First Page

483

Last Page

487

ISBN

9789051994070

Identifier

10.3233/978-1-60750-896-0-483

Publisher

IOS Press

City or Country

Amsterdam

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Share

COinS