Department of Biomedical Informatics, Vanderbilt University, School of Medicine, Nashville, Tennessee 37232, USA.
J Am Med Inform Assoc. 2010 Jan-Feb;17(1):19-24. doi: 10.1197/jamia.M3378.
Medication information is one of the most important types of clinical data in electronic medical records. It is critical for healthcare safety and quality, as well as for clinical research that uses electronic medical record data. However, medication data are often recorded in clinical notes as free-text. As such, they are not accessible to other computerized applications that rely on coded data. We describe a new natural language processing system (MedEx), which extracts medication information from clinical notes. MedEx was initially developed using discharge summaries. An evaluation using a data set of 50 discharge summaries showed it performed well on identifying not only drug names (F-measure 93.2%), but also signature information, such as strength, route, and frequency, with F-measures of 94.5%, 93.9%, and 96.0% respectively. We then applied MedEx unchanged to outpatient clinic visit notes. It performed similarly with F-measures over 90% on a set of 25 clinic visit notes.
药物信息是电子病历中最重要的临床数据类型之一。它对于医疗保健的安全性和质量，以及使用电子病历数据的临床研究至关重要。然而，药物数据通常在临床记录中以自由文本的形式记录。因此，它们无法被其他依赖编码数据的计算机化应用程序访问。我们描述了一种新的自然语言处理系统（MedEx），它可以从临床记录中提取药物信息。MedEx 最初是使用出院小结开发的。使用 50 份出院小结的数据集进行的评估表明，它不仅可以很好地识别药物名称（F 度量值为 93.2%），还可以很好地识别签名信息，如强度、途径和频率，其 F 度量值分别为 94.5%、93.9%和 96.0%。然后，我们将 MedEx 不变地应用于门诊就诊记录。在一组 25 份就诊记录上，其 F 度量值超过 90%，性能相似。
