Lependu Paea, Iyer Srinivasan V, Bauer-Mehren Anna, Harpaz Rave, Ghebremariam Yohannes T, Cooke John P, Shah Nigam H
Stanford University, Stanford, CA.
AMIA Jt Summits Transl Sci Proc. 2013 Mar 18;2013:109. eCollection 2013.
The current state of the art in post-marketing drug surveillance utilizes voluntarily submitted reports of suspected adverse drug reactions. We present data mining methods that transform unstructured patient notes taken by doctors, nurses and other clinicians into a de-identified, temporally ordered, patient-feature matrix using standardized medical terminologies. We demonstrate how to use the resulting high-throughput data to monitor for adverse drug events based on the clinical notes in the EHR.
目前上市后药品监测的技术水平是利用自愿提交的疑似药品不良反应报告。我们提出了数据挖掘方法,该方法使用标准化医学术语将医生、护士和其他临床医生记录的非结构化患者笔记转换为去识别化、按时间顺序排列的患者特征矩阵。我们展示了如何基于电子健康记录中的临床笔记,利用由此产生的高通量数据来监测药品不良事件。