UMLS content views appropriate for NLP processing of the biomedical literature vs. clinical text.

UMLS content views appropriate for NLP processing of the biomedical literature vs. clinical text.
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DOI:
10.1016/j.jbi.2010.02.005
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发表时间:
2010-08
影响因子:
4.5
通讯作者:
Aronson, Alan R.
Aronson, Alan R.
中科院分区:
医学3区
文献类型:
--
作者:
Demner-Fushman, Dina;Mork, James G.;Shooshan, Sonya E.;Aronson, Alan R.

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识别自由文本中的医学术语是自然语言处理(NLP)任务的第一步,这些任务包括生物医学文献的自动索引和从临床笔记文本中提取患者的问题列表。为执行这些任务而开发的许多工具使用统一医学语言系统(UMLS)元词库中编码的生物医学知识。我们继续探索自动创建子集(UMLS内容视图)的方法,这些方法可以支持生物医学文献或临床文本的NLP处理。我们发现,在保守的AutoFilter内容视图中抑制高度模糊的术语可以部分取代文献应用程序的手动过滤,并且在相同内容视图中抑制两个字符映射在临床应用程序中实现了89.5%的精确度和78.6%的召回率。
Identification of medical terms in free text is a first step in such Natural Language Processing (NLP) tasks as automatic indexing of biomedical literature and extraction of patients’ problem lists from the text of clinical notes. Many tools developed to perform these tasks use biomedical knowledge encoded in the Unified Medical Language System (UMLS) Metathesaurus. We continue our exploration of automatic approaches to creation of subsets (UMLS content views) which can support NLP processing of either the biomedical literature or clinical text. We found that suppression of highly ambiguous terms in the conservative AutoFilter content view can partially replace manual filtering for literature applications, and suppression of two character mappings in the same content view achieves 89.5% precision at 78.6% recall for clinical applications.
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