Exploring Linguistic and Graph Based Features for the Automatic Classification and Extraction of Adverse Drug Effects

Adverse drug effects (ADEs) are known to be one of the leading causes of post-therapeutic death. Thus, their identification constitutes an important challenge as the effects of ADEs are often underreported. However, the recent popularity of different social media sources has make it a promising source for ADE extraction. In this paper, we have explored different linguistic and graph topological features to automatically classify short sentences or tweets into ADEs or Non-ADEs. We have further represented the ADE knowledge base into an bipartite network structure of drugs and their side effects to model drug-side effect relationships. The proposed model can also be used to discover implicit ADEs that are not represented in the source data. We have evaluated our proposed models with two openly available ADE dataset. Our evaluation results shows that the proposed model have surpasses the performance of the existing baseline systems.