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

• Main Authors: Qi X., Tze-Yun LEONG
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2001
• Subjects: Bayesian network; Branch and Bound; Dynamic Decision Making; Influence View; Minimal Description Length Principle; Computer Sciences; Health Information Technology
• Online Access: https://ink.library.smu.edu.sg/sis_research/3008
• Institution: Singapore Management University

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.