Automating dynamic decision model construction to support clinical practice guideline development

The role of decision models in supporting automated clinical practice guideline development is gaining importance in recent years. Constructing decision models from scratch, however, is laborious, time-consuming, and knowledge-intensive. This paper presents a new practical methodology to facilitate...

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Main Authors: Zhu A., Tze-Yun LEONG
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2001
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Online Access:https://ink.library.smu.edu.sg/sis_research/2997
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-39972016-02-05T06:30:05Z Automating dynamic decision model construction to support clinical practice guideline development Zhu A., Tze-Yun LEONG, The role of decision models in supporting automated clinical practice guideline development is gaining importance in recent years. Constructing decision models from scratch, however, is laborious, time-consuming, and knowledge-intensive. This paper presents a new practical methodology to facilitate effective dynamic decision model construction for clinical practice guideline development, updating and customization. The proposed approach uses existing paper-based guidelines in sharable formats as the main information sources, characterizes the information inherent in the decision models and the clinical practice guidelines, establishes the relevant correspondences, reduces the effort needed in decision model construction, and increases the reusability and flexibility of the knowledge captured in the resulting guidelines. 2001-12-01T08:00:00Z text https://ink.library.smu.edu.sg/sis_research/2997 info:doi/10.3233/978-1-60750-927-1-133 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Computer Sciences Health Information Technology
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Computer Sciences
Health Information Technology
spellingShingle Computer Sciences
Health Information Technology
Zhu A.,
Tze-Yun LEONG,
Automating dynamic decision model construction to support clinical practice guideline development
description The role of decision models in supporting automated clinical practice guideline development is gaining importance in recent years. Constructing decision models from scratch, however, is laborious, time-consuming, and knowledge-intensive. This paper presents a new practical methodology to facilitate effective dynamic decision model construction for clinical practice guideline development, updating and customization. The proposed approach uses existing paper-based guidelines in sharable formats as the main information sources, characterizes the information inherent in the decision models and the clinical practice guidelines, establishes the relevant correspondences, reduces the effort needed in decision model construction, and increases the reusability and flexibility of the knowledge captured in the resulting guidelines.
format text
author Zhu A.,
Tze-Yun LEONG,
author_facet Zhu A.,
Tze-Yun LEONG,
author_sort Zhu A.,
title Automating dynamic decision model construction to support clinical practice guideline development
title_short Automating dynamic decision model construction to support clinical practice guideline development
title_full Automating dynamic decision model construction to support clinical practice guideline development
title_fullStr Automating dynamic decision model construction to support clinical practice guideline development
title_full_unstemmed Automating dynamic decision model construction to support clinical practice guideline development
title_sort automating dynamic decision model construction to support clinical practice guideline development
publisher Institutional Knowledge at Singapore Management University
publishDate 2001
url https://ink.library.smu.edu.sg/sis_research/2997
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