Automatic drug side effect discovery from online patient-submitted reviews: focus on statin drugs

Abstract—In recent years, consumers have become empowered to share personal experiences regarding prescription drugs via Web page discussion groups. This paper describes our recent research involving automatically identifying adverse reactions from patient-provided drug reviews on health-related web sites. We focus on the statin class of cholesterol-lowering drugs. We extract a complete set of side effect expressions from patient-submitted drug reviews, and construct a hierarchical ontology of side effects. We use log-likely ratio estimation to detect biases in word distributions when comparing reviews of statin drugs with age-matched reviews of a broad spectrum of other drugs. We find a highly significant correlation between statins and a wide range of disorders and conditions, including diabetes, amyotrophic lateral sclerosis (ALS), rhabdomyolysis, neuropathy, Parkinson’s disease, arthritis, memory loss, and heart failure. A review of the research literature on statin side effects corroborates many of our findings. Keywords- medicine data mining; drug side effect discovery I