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