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

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

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电子健康记录中计算他克莫司日剂量的自动方法。
临床研究通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。由于药物信息通常以结构化和非结构化格式记录在EHR中,因此提取所有相关药物提及并确定特定患者在给定日期的药物日剂量可能是一项具有挑战性且耗时的任务。在本文中,我们提出了一种使用自然语言处理的自动化方法来计算临床文本中提到的药物的每日剂量,使用他克莫司作为测试案例。我们使用来自四种不同类型的非结构化临床数据集来评估这种方法。结果表明,该系统的精密度为0.90 ~ 1.00,召回率为0.81 ~ 1.00。
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