Title

Constructing influence views from data to support dynamic decision making in medicine

Publication Type

Conference Proceeding Article

Publication Date

12-2001

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.

Keywords

Bayesian network, Branch and Bound, Dynamic Decision Making, Influence View, Minimal Description Length Principle

Discipline

Computer Sciences | Health Information Technology

Research Areas

Intelligent Systems and Decision Analytics

Publication

10th World Congress on Medical Informatics, MEDINFO 2001

Volume

84

First Page

1389

Last Page

1393

ISBN

9781586031947

Identifier

10.3233/978-1-60750-928-8-1389

City or Country

London, UK

This document is currently not available here.

Share

COinS