Towards Internet-Age Pharmacovigilance: Extracting Adverse Drug Reactions from User Posts in Health-Related Social Networks

TLDR

Adverse drug reactions are a leading cause of death, and traditional pharmacovigilance relies on voluntary professional reporting, but patient self‑reports from online health sites are increasingly valuable. The authors propose a system that mines drug–adverse reaction relationships from patient comments on health‑related websites. The system was evaluated on a manually annotated corpus of user comments, showing promising performance. The extracted ADR frequencies correlated with documented ADRs, and the study demonstrates that user comments, while NLP‑challenging, provide valuable extractable information.

Abstract

Adverse reactions to drugs are among the most common causes of death in industrialized nations. Expensive clinical trials are not sufficient to uncover all of the adverse reactions a drug may cause, necessitating systems for post-marketing surveillance, or pharmacovigilance. These systems have typically relied on voluntary reporting by health care professionals. However, self-reported patient data has become an increasingly important resource, with efforts such as MedWatch from the FDA allowing reports directly from the consumer. In this paper, we propose mining the relationships between drugs and adverse reactions as reported by the patients themselves in user comments to health-related websites. We evaluate our system on a manually annotated set of user comments, with promising performance. We also report encouraging correlations between the frequency of adverse drug reactions found by our system in unlabeled data and the frequency of documented adverse drug reactions. We conclude that user comments pose a significant natural language processing challenge, but do contain useful extractable information which merits further exploration.