Lister Hill National Center for Biomedical Communications (LHNCBC), U.S. National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.
J Biomed Inform. 2010 Aug;43(4):587-94. doi: 10.1016/j.jbi.2010.02.005. Epub 2010 Feb 10.
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.
在自然语言处理（NLP）任务中，例如自动索引生物医学文献和从临床记录的文本中提取患者的问题列表，识别自由文本中的医学术语是第一步。许多用于执行这些任务的工具都使用统一医学语言系统（UMLS）Metathesaurus 中编码的生物医学知识。我们继续探索自动方法来创建子集（UMLS 内容视图），这些子集可以支持 NLP 处理生物医学文献或临床文本。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替代文献应用程序的手动过滤，并且在同一内容视图中抑制两个字符映射可以实现临床应用程序 78.6%召回率时达到 89.5%的精度。
