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Identification of concepts from emergency department text using natural language processing techniques and the Unified Medical Language System RTM.

机译:使用自然语言处理技术和Unified Medical Language System RTM从急诊科文本中识别概念。

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摘要

This research is part of a larger project to develop a thesaurus for emergency department (ED) chief complaint (CC) information. The CC is the patient's reason for visiting the ED, and is determined by the triage nurse in the initial minutes of the visit. The nature and severity of the CC directly influence many aspects of the patient's ED visit. CC data are also vital for public health surveillance activities. Despite the significance of the CC, there is no standard terminology for CC.; The goals of this research were to identify the concepts that comprise the domain of ED CC, and to develop a modular natural language processing (NLP) system for use in processing clinical text. The resulting Emergency Medical Text Processor (EMT-P) system is a series of modules that extracts standardized terms from clinical text using NLP and the Unified Medical Language System®. After applying EMT-P to a corpus of CC data representing all visits to three EDs during a one-year period of time, 83% of the original CC entries matched a UMLS concept. Samples of text/UMLS concept matches and non-matches were evaluated to determine the accuracy of EMT-P. 96% of the matches were rated equivalent or related, and 38% of the non-matches were found to match UMLS concepts. The results show that EMT-P Version 1 is relatively accurate; areas needing improvement in future versions of EMT-P were identified.; In the course of this study, a modular NLP system called EMT-P was developed and used to process a corpus of clinical text and extract standardized terms from the majority of entries. 3898 ED CC concepts were identified for possible inclusion in an ED CC Thesaurus, and produced a model of the domain of ED CC. A set of recommendations for developing the ED CC Thesaurus was also compiled, and included further validation of EMT-P, some specific areas of content to be included, the need to address CC-related data, and operational issues regarding design and implementation. Future plans include application of EMT-P to other types of clinical text, including triage nurses' notes, and clinical reports.
机译:该研究是一个大型项目的一部分,该项目旨在开发一个用于急诊科(ED)主诉(CC)信息的同义词库。 CC是患者去急诊室的原因,由分诊护士在就诊的最初几分钟确定。 CC的性质和严重性直接影响患者ED访视的许多方面。 CC数据对于公共卫生监视活动也至关重要。尽管CC有重要意义,但CC没有标准术语。这项研究的目标是确定构成ED CC领域的概念,并开发用于处理临床文本的模块化自然语言处理(NLP)系统。最终的紧急医疗文本处理器(EMT-P)系统是一系列模块,可以使用NLP和统一医疗语言系统®从临床文本中提取标准化术语。在将EMT-P应用于代表一年中所有对三个ED的所有访问的CC数据集之后,原始CC条目的83%符合UMLS概念。评估了文本/ UMLS概念匹配和不匹配的样本,以确定EMT-P的准确性。 96%的匹配项被评为同等或相关,并且发现38%的不匹配项符合UMLS概念。结果表明,EMT-P版本1相对准确;确定了将来的EMT-P版本中需要改进的地方。在本研究过程中,开发了一种称为EMT-P的模块化NLP系统,该系统用于处理临床文本语料库并从大多数条目中提取标准化术语。确定了3898个ED CC概念,可能将其包含在ED CC词库中,并产生了ED CC领域的模型。还编制了一套有关开发ED CC同义词库的建议,其中包括对EMT-P的进一步验证,要包含的某些特定内容领域,处理与CC相关的数据的需求以及有关设计和实施的操作问题。未来的计划包括将EMT-P应用于其他类型的临床文本,包括分诊护士的笔记和临床报告。

著录项

  • 作者

    Travers, Debbie.;

  • 作者单位

    The University of North Carolina at Chapel Hill.;

  • 授予单位 The University of North Carolina at Chapel Hill.;
  • 学科 Information Science.; Health Sciences Health Care Management.; Language Modern.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 224 p.
  • 总页数 224
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 信息与知识传播;预防医学、卫生学;语言学;
  • 关键词

  • 入库时间 2022-08-17 11:45:30

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