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Abstract：
The adoption of electronic health records has increased the volume of clinical data, which has opened an opportunity for healthcare research. There are several biomedical annotation systems that have been used to facilitate the analysis of clinical data. However, there is a lack of clinical annotation comparisons to select the most suitable tool for a specific clinical task. In this work, we used clinical notes from the MIMIC-III database and evaluated three annotation systems to identify four types of entities: (1) procedure, (2) disorder, (3) drug, and (4) anatomy. Our preliminary results demonstrate that BioPortal performs well when extracting disorder and drug. This can provide clinical researchers with real-clinical insights into patient\'s health patterns and it may allow to create a first version of an annotated dataset.
摘要：
电子健康档案的采用增加了临床数据量,这为医疗保健研究提供了机会。有几种生物医学注释系统已用于促进临床数据分析。然而,缺乏临床注释比较来选择最适合特定临床任务的工具。在这项工作中,我们使用MIMIC-III数据库中的临床注释，并评估了三个注释系统以识别四种类型的实体：（1）程序，（2）混乱，（3）药物，（4）解剖学。我们的初步结果表明，BioPortal在提取疾病和药物时表现良好。这可以为临床研究人员提供对患者健康模式的真实临床见解，并且可以创建注释数据集的第一个版本。
