Constructing influence views from data to support dynamic decision making in medicine
Conference Proceeding Article
A dynamic decision model can facilitate the complicated decision-making process in medicine, in which both time and uncertainty are explicitly considered. In this paper, we address the problem of automatic construction of a dynamic decision model from a large medical database. Within the DynaMoL (a dynamic decision modeling language) framework, a model can be represented in influence view. Thus, our proposed approach first learns the structures of the influence view based on the minimal description length (MDL) principle, and then obtains the conditional probabilities of the model by Bayesian method. The experiment results demonstrate that our system can efficiently construct the influence views from data with high fidelity.
Bayesian network, Branch and Bound, Dynamic Decision Making, Influence View, Minimal Description Length Principle
Computer Sciences | Health Information Technology
Intelligent Systems and Decision Analytics
10th World Congress on Medical Informatics, MEDINFO 2001
City or Country
Qi X. and Tze-Yun LEONG.
Constructing influence views from data to support dynamic decision making in medicine. (2001). 10th World Congress on Medical Informatics, MEDINFO 2001. 84, 1389-1393. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3008