Big Data and Pharmacovigilance: Data Mining for Adverse Drug Events and Interactions

Abstract

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.