Please use this identifier to cite or link to this item:
Title: Constructing influence views from data to support dynamic decision making in medicine
Authors: Qi, X. 
Leong, T.-Y. 
Keywords: Bayesian network
Branch and Bound
Dynamic Decision Making
Influence View
Minimal Description Length Principle
Issue Date: 2001
Citation: Qi, X.,Leong, T.-Y. (2001). Constructing influence views from data to support dynamic decision making in medicine. Studies in Health Technology and Informatics 84 : 1389-1393. ScholarBank@NUS Repository.
Abstract: 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. © 2001 IMIA. All right reserved.
Source Title: Studies in Health Technology and Informatics
ISBN: 1586031945
ISSN: 09269630
DOI: 10.3233/978-1-60750-928-8-1389
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

checked on Oct 27, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.