Don't Mention It? Analyzing User-Generated Content Signals for Early Adverse Drug Event Warnings

Abstract

Post-marketing surveillance entails monitoring, assessing, and detecting adverse events once drugs are approved and released into the market. User-generated content channels such as social media and search query logs are increasingly being leveraged as complementary data sources to traditional databases for post-marketing drug surveillance. However, the existing body of knowledge has leveraged diverse sets of channels, adverse event types, and modeling methods, resulting in varying results and diverging conclusions regarding the viability and efficacy of various online user-generated channels and accompanying modeling methods. The objective of this study is to examine the efficacy and impact of different online user-generated content channels, event characteristics, and event modeling strategies on early detection of adverse drug events. We incorporate a large test bed encompassing millions of tweets, forums postings, and search query logs pertaining to 143 adverse events. We also propose a novel heuristic-based event modeling method capable of improving precision, recall, and timeliness of alerts. Preliminary results shed light on the interplay between user-generated channels and event types, as well as the potential for more robust event modeling methods that go beyond basic mention models. Several current and future research directions are also discussed. The preliminary results reported have important implications for various stakeholder groups, including regulatory agencies, post-marketing monitoring teams, healthcare hedge fund managers, and third-party consumer advocacy groups.

Keywords: Signal Detection, Social Media, Data Mining, Smart Health, Predictive Analytics