High Throughput Modularized NLP System for Clinical Text
High Throughput Modularized NLP System for Clinical Text
复制标题
用于临床文本的高通量模块化 NLP 系统
DOI：
10.3115/1225753.1225760
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发表时间：
2005
影响因子：
3.5
通讯作者：
Patrick H. Duffy
中科院分区：
文献类型：
--
作者：
Serguei V. S. Pakhomov;J. Buntrock;Patrick H. Duffy
本文介绍了一个高吞吐量，真实的时间模块化的文本分析和信息检索系统，识别临床相关的实体在临床笔记，映射到几个标准化的术语，并使它们可用于后续的信息检索和数据挖掘的发展的结果。该系统的性能进行了验证的一个小集合的351个文件划分为4个查询主题，并手动检查3名医生和3名护士摘要的相关性查询主题。我们发现，简单的关键词搜索结果在73%的召回率和77%的精度。NLP方法的组合索引将召回率提高到92%，同时将精度降低到67%。
This paper presents the results of the development of a high throughput, real time modularized text analysis and information retrieval system that identifies clinically relevant entities in clinical notes, maps the entities to several standardized nomenclatures and makes them available for subsequent information retrieval and data mining. The performance of the system was validated on a small collection of 351 documents partitioned into 4 query topics and manually examined by 3 physicians and 3 nurse abstractors for relevance to the query topics. We find that simple key phrase searching results in 73% recall and 77% precision. A combination of NLP approaches to indexing improve the recall to 92%, while lowering the precision to 67%.
DOI：
--
发表时间：
2001
期刊：
Proceedings. AMIA Symposium
影响因子：
--
作者：
W. Chapman;Will Bridewell;P. Hanbury;G. Cooper;B. Buchanan
通讯作者：
W. Chapman;Will Bridewell;P. Hanbury;G. Cooper;B. Buchanan