Detection of adverse drug reactions from online health communities' data: a case study of anti-epileptic drugs

This paper investigates the problem of detecting adverse drug reactions of anti-epileptic drugs from patients' reviews in online health communities. A lexicon-based methodology is proposed and applied to a dataset of patients' reviews collected from two online health communities. The dataset is cleaned and the adverse reactions of anti-epileptic drugs are extracted with the aid of consumer health vocabulary and a lexicon of adverse drug reactions. A proportional reporting ratio is then applied to quantify the correlation between each drug and adverse reactions and thus identify the adverse reactions of each drug. The results are validated quantitatively against a database of adverse drug reactions, called side effect resource, and qualitatively against the extant knowledge related to the common adverse reactions and drug-drug similarities of anti-epileptic drugs. The validation results provide evidences on the effectiveness of the proposed methodology and the validity of online health communities' data for adverse drug reactions detection.