Learning Conditional Probabilities for Dynamic Influence Structures in Medical Decision Models

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...

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Main Authors:	CAO, Cungen, Tze-Yun LEONG
Format:	text
Language:	English
Published:	Institutional Knowledge at Singapore Management University 1997
Subjects:	Artificial Intelligence and Robotics Computer Sciences Medicine and Health Sciences
Online Access:	https://ink.library.smu.edu.sg/sis_research/3060
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Institution:	Singapore Management University
Language:	English

id	sg-smu-ink.sis_research-4060
record_format	dspace
spelling	sg-smu-ink.sis_research-40602016-02-05T06:30:05Z Learning Conditional Probabilities for Dynamic Influence Structures in Medical Decision Models CAO, Cungen Tze-Yun LEONG, 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. 1997-10-25T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/3060 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Artificial Intelligence and Robotics Computer Sciences Medicine and Health Sciences
institution	Singapore Management University
building	SMU Libraries
continent	Asia
country	Singapore Singapore
content_provider	SMU Libraries
collection	InK@SMU
language	English
topic	Artificial Intelligence and Robotics Computer Sciences Medicine and Health Sciences
spellingShingle	Artificial Intelligence and Robotics Computer Sciences Medicine and Health Sciences CAO, Cungen Tze-Yun LEONG, Learning Conditional Probabilities for Dynamic Influence Structures in Medical Decision Models
description	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.
format	text
author	CAO, Cungen Tze-Yun LEONG,
author_facet	CAO, Cungen Tze-Yun LEONG,
author_sort	CAO, Cungen
title	Learning Conditional Probabilities for Dynamic Influence Structures in Medical Decision Models
title_short	Learning Conditional Probabilities for Dynamic Influence Structures in Medical Decision Models
title_full	Learning Conditional Probabilities for Dynamic Influence Structures in Medical Decision Models
title_fullStr	Learning Conditional Probabilities for Dynamic Influence Structures in Medical Decision Models
title_full_unstemmed	Learning Conditional Probabilities for Dynamic Influence Structures in Medical Decision Models
title_sort	learning conditional probabilities for dynamic influence structures in medical decision models
publisher	Institutional Knowledge at Singapore Management University
publishDate	1997
url	https://ink.library.smu.edu.sg/sis_research/3060
_version_	1770572799015911424

Learning Conditional Probabilities for Dynamic Influence Structures in Medical Decision Models

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