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一种在电子健康记录中计算他克莫司每日剂量的自动化方法。

An automated approach to calculating the daily dose of tacrolimus in electronic health records.

作者信息

Xu Hua, Doan Son, Birdwell Kelly A, Cowan James D, Vincz Andrew J, Haas David W, Basford Melissa A, Denny Joshua C

机构信息

Department of Biomedical Informatics;

出版信息

Summit Transl Bioinform. 2010 Mar 1;2010:71-5.

Abstract

Clinical research often requires extracting detailed drug information, such as medication names and dosages, from Electronic Health Records (EHR). Since medication information is often recorded as both structured and unstructured formats in the EHR, extracting all the relevant drug mentions and determining the daily dose of a medication for a selected patient at a given date can be a challenging and time-consuming task. In this paper, we present an automated approach using natural language processing to calculate daily doses of medications mentioned in clinical text, using tacrolimus as a test case. We evaluated this method using data sets from four different types of unstructured clinical data. Our results showed that the system achieved precisions of 0.90-1.00 and recalls of 0.81-1.00.

摘要

临床研究常常需要从电子健康记录(EHR)中提取详细的药物信息,例如药物名称和剂量。由于药物信息在EHR中常常以结构化和非结构化格式记录,提取所有相关的药物提及并确定特定日期选定患者的药物每日剂量可能是一项具有挑战性且耗时的任务。在本文中,我们提出一种使用自然语言处理的自动化方法,以他克莫司作为测试案例来计算临床文本中提及药物的每日剂量。我们使用来自四种不同类型非结构化临床数据的数据集对该方法进行了评估。我们的结果表明,该系统的精确率为0.90 - 1.00,召回率为0.81 - 1.00。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63de/3041548/33fe7852fc6b/amia-s2010_cri_071f1.jpg

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