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An Experiment Comparing Lexical and Statistical Methods for Extracting MeSH Terms from Clinical Free Text

机译:比较词汇和统计方法以从临床免费文本中提取MeSH术语的实验

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摘要

>Abstract Objective: A primary goal of the University of Pittsburgh's 1990-94 UMLS-sponsored effort was to develop and evaluate PostDoc (a lexical indexing system) and Pindex (a statistical indexing system) comparatively, and then in combination as a hybrid system. Each system takes as input a portion of the free text from a narrative part of a patient's electronic medical record and returns a list of suggested MeSH terms to use in formulating a Medline search that includes concepts in the text. This paper describes the systems and reports an evaluation. The intent is for this evaluation to serve as a step toward the eventual realization of systems that assist healthcare personnel in using the electronic medical record to construct patient-specific searches of Medline.>Design: The authors tested the performances of PostDoc, Pindex, and a hybrid system, using text taken from randomly selected clinical records, which were stratified to include six radiology reports, six pathology reports, and six discharge summaries. They identified concepts in the clinical records that might conceivably be used in performing a patient-specific Medline search. Each system was given the free text of each record as an input. The extent to which a system-derived list of MeSH terms captured the relevant concepts in these documents was determined based on blinded assessments by the authors.>Results: PostDoc output a mean of approximately 19 MeSH terms per report, which included about 40% of the relevant report concepts. Pindex output a mean of approximately 57 terms per report and captured about 45% of the relevant report concepts. A hybrid system captured approximately 66% of the relevant concepts and output about 71 terms per report.>Conclusion: The outputs of PostDoc and Pindex are complementary in capturing MeSH terms from clinical free text. The results suggest possible approaches to reduce the number of terms output while maintaining the percentage of terms captured, including the use of UMLS semantic types to constrain the output list to contain only clinically relevant MeSH terms.
机译:>摘要目标:匹兹堡大学(University of Pittsburgh)1990-94年度UMLS赞助的主要目标是比较地开发和评估PostDoc(一个词法索引系统)和Pindex(一个统计索引系统),然后组合为混合系统。每个系统从患者电子病历的叙述部分中获取一部分自由文本作为输入,并返回建议的MeSH术语列表,以用于制定包括文本概念的Medline搜索。本文介绍了系统并报告了评估。此评估的目的是迈向最终实现可帮助医疗保健人员使用电子病历来构建Medline患者特定搜索的系统的步骤。>设计:作者测试了性能使用从随机选择的临床记录中摘录的文本对PostDoc,Pindex和混合系统进行了分类,其分层包括六份放射学报告,六份病理学报告和六份出院摘要。他们确定了临床记录中可能在执行患者特定的Medline搜索中使用的概念。每个系统都获得了每个记录的自由文本作为输入。系统衍生的MeSH术语列表在多大程度上捕获了相关 这些文件中的概念是根据 作者。>结果:PostDoc平均每个文档大约输出19个MeSH术语 报告,其中包括约40%的相关报告概念。指数 每份报告平均输出约57个字词,约占45% 相关的报告概念。混合动力系统约占66% 相关概念和每份报告约71个术语的输出。>结论:PostDoc和Pindex的输出在以下方面是互补的 从临床免费文本中捕获MeSH术语。结果表明可能 减少术语输出数量,同时保持 捕获的术语的百分比,包括使用UMLS语义类型 将输出列表限制为仅包含临床相关的MeSH术语。

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