Suppr超能文献

用于编码美国国家胆固醇教育计划(NCEP)胆固醇指南的三种知识表示形式的比较。

Comparison of three Knowledge Representation formalisms for encoding the NCEP Cholesterol Guidelines.

作者信息

Starren J, Xie G

机构信息

Center for Medical Informatics, Columbia University College of Physicians and Surgeons, New York, New York 10032.

出版信息

Proc Annu Symp Comput Appl Med Care. 1994:792-6.

Abstract

Although many Knowledge Representation (KR) formalisms have been used to encode care guidelines, there are few direct comparisons among different formalisms. In order to compare their suitability for encoding care guidelines, three different KR formalisms were used to encode the National Cholesterol Education Panel (NCEP) guideline. PROLOG, a First Order Logic system, CLASSIC, a frame-based representation system, and CLIPS, a production rule system, were used in the comparison. All three representations allowed accurate encoding of the guideline. PROLOG produced the most compact representation, but proved the most difficult to debug. The lack of arbitrary disjunction in CLASSIC greatly increased the complexity of the encoding. Overall, the CLIPS representation was the most intuitive and easiest to use.

摘要

尽管许多知识表示(KR)形式体系已被用于编码护理指南,但不同形式体系之间的直接比较却很少。为了比较它们对编码护理指南的适用性,使用了三种不同的KR形式体系来编码美国国家胆固醇教育计划(NCEP)指南。比较中使用了PROLOG(一种一阶逻辑系统)、CLASSIC(一种基于框架的表示系统)和CLIPS(一种产生式规则系统)。所有这三种表示都能准确地对指南进行编码。PROLOG产生的表示最为紧凑,但事实证明最难调试。CLASSIC中缺乏任意析取极大地增加了编码的复杂性。总体而言,CLIPS表示最为直观且最易于使用。

相似文献

9
Prediction in annotation based guideline encoding.
AMIA Annu Symp Proc. 2006;2006:314-8.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验