LAUSR

Recently, an active area of research in pharmacovigilance is to use social media such as Twitter as an alternative data source to gather patient-generated information pertaining to medication use. Most of thr published work focuses on identifying mentions of adverse effects in social media data but rarely investigating the relationship between a mentioned medication and any mentioned effect expressions. In this study, we treated this relation extraction task as a classification problem, and represented the Twitter text with neural embedding which was fed to a recurrent neural network classifier. The classification performance of our method was investigated in comparison with 4 baseline word embedding methods on a corpus of 9516 annotated tweets.