Intelligent Clinical Decision Support Systems Based on SNOMED CT

被引：6

作者：

Ciolko, Ewelina ^{[1
]}

Lu, Fletcher ^{[1
]}

Joshi, Amardeep ^{[1
]}

机构：

[1] Univ Ontario, Inst Technol, Fac Hlth Sci, Oshawa, ON, Canada

来源：

2010 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) | 2010年

关键词：

D O I：

10.1109/IEMBS.2010.5625982

中图分类号：

R318 [生物医学工程];

学科分类号：

100103 [病原生物学];

摘要：

The decision support systems that have been developed to assist physicians in the diagnostic process often are based on static data which may be out of date. We present a comprehensive analysis of artificial intelligent methods which could be applied to documents encoded by SNOMED CT. By mining information directly from SNOMED CT encoded documents, a decision support system could contain timely updated diagnostic information, which is of significant value in fast changing situations such as minimally understood emerging diseases and epidemics. Through a high level comparison of many AI methods it is found that a TANBayesian method could be the most suitable to apply to SNOMED CT data.

引用

页码：6781 / 6784

页数：4

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