论文标题
亚马逊理解的评估医学：药物信息提取
Assessment of Amazon Comprehend Medical: Medication Information Extraction
论文作者
论文摘要
2018年11月27日，亚马逊网络服务（AWS）发布了亚马逊理解（ACM），这是一种基于深度学习的系统，该系统自动从临床文本注释中自动提取临床概念（包括解剖学，医疗状况，受保护的健康信息（PH）I，测试名称，治疗名称和医疗程序和药物）。对任何新数据产品的吸收和信任都依赖于基准数据集和工具的独立验证来建立和确认结果的预期质量。这项工作着重于药物提取任务，尤其是使用2009 I2B2药物提取挑战和2018 N2C2轨道2：EHR中的不良药物事件和药物提取的官方测试集评估了ACM。总体而言，ACM的F得分为0.768和0.828。与各自挑战中的三个最佳系统相比，这些分数排名最低。为了进一步确定其药物提取性能的普遍性，这项工作还包括了NYU Langone医疗中心的一组随机内部临床文本注释。在这个语料库中，ACM获得了0.753的F-评分。
In November 27, 2018, Amazon Web Services (AWS) released Amazon Comprehend Medical (ACM), a deep learning based system that automatically extracts clinical concepts (which include anatomy, medical conditions, protected health information (PH)I, test names, treatment names, and medical procedures, and medications) from clinical text notes. Uptake and trust in any new data product relies on independent validation across benchmark datasets and tools to establish and confirm expected quality of results. This work focuses on the medication extraction task, and particularly, ACM was evaluated using the official test sets from the 2009 i2b2 Medication Extraction Challenge and 2018 n2c2 Track 2: Adverse Drug Events and Medication Extraction in EHRs. Overall, ACM achieved F-scores of 0.768 and 0.828. These scores ranked the lowest when compared to the three best systems in the respective challenges. To further establish the generalizability of its medication extraction performance, a set of random internal clinical text notes from NYU Langone Medical Center were also included in this work. And in this corpus, ACM garnered an F-score of 0.753.
