Automatically Detecting Medications and the Reason for their Prescription in Clinical Narrative Text Documents

Automatically Detecting Medications and the Reason for their Prescription in Clinical Narrative Text Documents
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自动检测临床叙述文本文档中的药物及其处方原因

DOI:
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发表时间:
2010
期刊:
Medinfo
影响因子:
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通讯作者:
B. South
B. South
中科院分区:
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文献类型:
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作者:
S. Meystre;J. Thibault;Shuying Shen;John F. Hurdle;B. South

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关于患者正在服用的药物的重要部分信息仅在电子健康记录的叙述性文本中提及。自动化信息提取可以使这些信息可用于决策支持、研究或任何其他自动化处理。在“i2 b2药物提取挑战”的背景下,我们开发了一个新的NLP应用程序,称为Textractor,可以自动提取药物及其细节(例如,剂量、频率、处方原因)。本申请及其评价与部分参考标准的这一“挑战”在这里介绍,沿着的发展,这一参考标准的分析。在这次评估中,Textractor达到了系统级的整体F1测量,这是这次挑战的参考指标,精确匹配率约为77%。最好的性能与药物途径(F1-措施86.4%),最差的处方原因(F1-措施29%)。这些结果与人类注释者在开发参比标准品时观察到的一致性以及其他已发表的研究一致。
An important proportion of the information about the medications a patient is taking is mentioned only in narrative text in the electronic health record. Automated information extraction can make this information accessible for decision support, research, or any other automated processing. In the context of the "i2b2 medication extraction challenge," we have developed a new NLP application called Textractor to automatically extract medications and details about them (e.g., dosage, frequency, reason for their prescription). This application and its evaluation with part of the reference standard for this "challenge" are presented here, along with an analysis of the development of this reference standard. During this evaluation, Textractor reached a system-level overall F1-measure, the reference metric for this challenge, of about 77% for exact matches. The best performance was measured with medication routes (F1-measure 86.4%), and the worst with prescription reasons (F1-measure 29%). These results are consistent with the agreement observed between human annotators when developing the reference standard, and with other published research.
DOI: --
发表时间: 1995
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