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The automation of adverse drug reaction (ADR) detection in social media wouldrevolutionize the practice of pharmacovigilance, supporting drug regulators,the pharmaceutical industry and the general public in ensuring the safety ofthe drugs prescribed in daily practice. Following from the publishedproceedings of the Social Media Mining for Health (SMM4H) Applications Workshop& Shared Task in August 2019, we aimed to develop a deep learning model toclassify ADRs within Twitter tweets that contain drug mentions. Our approachinvolved fine-tuning $BERT_{LARGE}$ and two domain-specific BERTimplementations, $BioBERT$ and $Bio + clinicalBERT$, applying a domain-specificpreprocessor, and developing a max-prediction ensembling approach. Our finalmodel resulted in state-of-the-art performance on both $F_1$-score (0.6681) andrecall (0.7700) outperforming all models submitted in SMM4H 2019 and duringpost-evaluation to date.

Detecting Adverse Drug Reactions from Twitter through Domain-Specific Preprocessing and BERT Ensembling