  Healthcare predictive analytics aids medical decision-making, diagnosis
prediction and drug review analysis. Therefore, prediction accuracy is an
important criteria which also necessitates robust predictive language models.
However, the models using deep learning have been proven vulnerable towards
insignificantly perturbed input instances which are less likely to be
misclassified by humans. Recent efforts of generating adversaries using
rule-based synonyms and BERT-MLMs have been witnessed in general domain, but
the ever increasing biomedical literature poses unique challenges. We propose
BBAEG (Biomedical BERT-based Adversarial Example Generation), a black-box
attack algorithm for biomedical text classification, leveraging the strengths
of both domain-specific synonym replacement for biomedical named entities and
BERTMLM predictions, spelling variation and number replacement. Through
automatic and human evaluation on two datasets, we demonstrate that BBAEG
performs stronger attack with better language fluency, semantic coherence as
compared to prior work.
