Learning Conditional Probabilities for Dynamic Influence Structures in Medical Decision Models
Publication Type
Conference Proceeding Article
Publication Date
10-1997
Abstract
Based on the DynaMoL (a Dynamic decision Modeling Language) framework, we examine the critical issues in automated learning of numerical parameters from large medical databases; present a Bayesian method for learning conditional probabilities from data; analyze how to elicit prior probabilities from the domain expert; and examine several important issues on pre-processing raw data for application in dynamic decision modeling.
Discipline
Artificial Intelligence and Robotics | Computer Sciences | Medicine and Health Sciences
Publication
Proceedings of the 1997 American Medical Informatics Association Annual Fall Symposium: October 25-29, Nashville, TN
First Page
848
Last Page
848
Publisher
Hanley and Belfus
City or Country
Philadelphia, PA
Citation
CAO, Cungen and Tze-Yun LEONG.
Learning Conditional Probabilities for Dynamic Influence Structures in Medical Decision Models. (1997). Proceedings of the 1997 American Medical Informatics Association Annual Fall Symposium: October 25-29, Nashville, TN. 848-848.
Available at: https://ink.library.smu.edu.sg/sis_research/3060
Additional URL
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2233501/