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
复制标题
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:
影响因子:
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通讯作者:
B. South
中科院分区:
文献类型:
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作者:
S. Meystre;J. Thibault;Shuying Shen;John F. Hurdle;B. South
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.
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DOI:
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1995
期刊:
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