Ventola C Lee
P T. 2018 Jun;43(6):340-351.
Adverse drug events (ADEs), including drug interactions, have a tremendous impact on patient health and generate substantial health care costs. A "big data" approach to pharmacovigilance involves the identification of drug-ADE associations by data mining various electronic sources, including: adverse event reports, the medical literature, electronic health records, and social media. This approach has been useful in assisting the Food and Drug Administration and other regulatory agencies in monitoring and decision-making regarding drug safety. Data mining can also assist pharmaceutical companies in drug safety surveillance efforts, adhering to risk management plans, and gathering real-world evidence to supplement clinical trial data. The use of data mining for pharmacovigilance purposes provides many unique benefits; however, it also presents many challenges. This paper explores the methods and sources of "big data" and how this is contributing to pharmacovigilance efforts.
药物不良事件(ADEs),包括药物相互作用,对患者健康有巨大影响,并产生大量医疗费用。药物警戒的“大数据”方法涉及通过挖掘各种电子来源的数据来识别药物与药物不良事件的关联,这些来源包括:不良事件报告、医学文献、电子健康记录和社交媒体。这种方法在协助美国食品药品监督管理局和其他监管机构进行药物安全性监测和决策方面很有用。数据挖掘还可以帮助制药公司进行药物安全性监测工作、遵守风险管理计划以及收集真实世界证据以补充临床试验数据。将数据挖掘用于药物警戒目的有许多独特的好处;然而,它也带来了许多挑战。本文探讨了“大数据”的方法和来源,以及它如何促进药物警戒工作。