ABSTRACT
Traditional pharmacovigilance systems rely on adverse event reports received by regulatory authorities such as the United States Food and Drug Administration (FDA). These traditional systems suffer from underreporting and are not timely due to their reliance on third-party sentinels. To address these issues, the MedWatcher Social system for monitoring adverse events through automated processing of digital social media data and crowdsourcing was launched in 2012 by Boston Children’s Hospital and the FDA. The system is rooted in the well-established FDA MedWatch system.
MedWatcher Social uses an indicator score approach to identify adverse events. This study evaluates the MedWatcher Social adverse event classifier’s performance on Twitter data and proposes an enhancement to the indicator score method that results in improved adverse event identification.
Our research suggests that automatic pharmacovigilance systems using the original indicator score approach should be updated. Careful consideration of modeling assumptions is critical when designing algorithms for computational epidemiology, and algorithms should be regularly reevaluated to identify enhancements and to remedy concept drift.
Footnotes
Nguyen_Andre{at}bah.com, Raff_Edward{at}bah.com, Lien_Julia{at}bah.com, Mekaru_Sumiko{at}bah.com
