Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA.
J Am Med Inform Assoc. 2014 Mar-Apr;21(2):353-62. doi: 10.1136/amiajnl-2013-001612. Epub 2013 Oct 24.
Electronic health records (EHRs) are increasingly being used to complement the FDA Adverse Event Reporting System (FAERS) and to enable active pharmacovigilance. Over 30% of all adverse drug reactions are caused by drug-drug interactions (DDIs) and result in significant morbidity every year, making their early identification vital. We present an approach for identifying DDI signals directly from the textual portion of EHRs.
We recognize mentions of drug and event concepts from over 50 million clinical notes from two sites to create a timeline of concept mentions for each patient. We then use adjusted disproportionality ratios to identify significant drug-drug-event associations among 1165 drugs and 14 adverse events. To validate our results, we evaluate our performance on a gold standard of 1698 DDIs curated from existing knowledge bases, as well as with signaling DDI associations directly from FAERS using established methods.
Our method achieves good performance, as measured by our gold standard (area under the receiver operator characteristic (ROC) curve >80%), on two independent EHR datasets and the performance is comparable to that of signaling DDIs from FAERS. We demonstrate the utility of our method for early detection of DDIs and for identifying alternatives for risky drug combinations. Finally, we publish a first of its kind database of population event rates among patients on drug combinations based on an EHR corpus.
It is feasible to identify DDI signals and estimate the rate of adverse events among patients on drug combinations, directly from clinical text; this could have utility in prioritizing drug interaction surveillance as well as in clinical decision support.
电子健康记录（EHRs）越来越多地被用于补充 FDA 不良事件报告系统（FAERS），并实现主动药物警戒。超过 30%的药物不良反应是由药物相互作用（DDI）引起的，每年都会导致严重的发病率，因此早期识别至关重要。我们提出了一种直接从 EHR 的文本部分识别 DDI 信号的方法。
我们从两个地点的超过 5000 万份临床记录中识别药物和事件概念的提及，为每个患者创建概念提及的时间线。然后，我们使用调整后的不相称比来识别 1165 种药物和 14 种不良事件之间的显著药物-药物-事件关联。为了验证我们的结果，我们使用从现有知识库中精心挑选的 1698 个 DDI 的黄金标准以及使用 FAERS 中建立的方法直接从信号 DDI 关联来评估我们在性能。
我们的方法在两个独立的 EHR 数据集上实现了良好的性能，表现为我们的黄金标准（接收器操作特征曲线下的面积（ROC 曲线）>80%），性能可与 FAERS 中信号 DDI 的性能相媲美。我们证明了我们的方法在早期检测 DDI 和识别风险药物组合的替代方案方面的实用性。最后，我们根据 EHR 语料库发布了第一个基于人群的药物组合患者不良事件发生率数据库。
直接从临床文本中识别 DDI 信号并估计药物组合患者的不良事件发生率是可行的；这在优先监测药物相互作用以及临床决策支持方面可能具有实用性。
