  Preventable adverse events as a result of medical errors present a growing
concern in the healthcare system. As drug-drug interactions (DDIs) may lead to
preventable adverse events, being able to extract DDIs from drug labels into a
machine-processable form is an important step toward effective dissemination of
drug safety information. In this study, we tackle the problem of jointly
extracting drugs and their interactions, including interaction outcome, from
drug labels. Our deep learning approach entails composing various intermediate
representations including sequence and graph based context, where the latter is
derived using graph convolutions (GC) with a novel attention-based gating
mechanism (holistically called GCA). These representations are then composed in
meaningful ways to handle all subtasks jointly. To overcome scarcity in
training data, we additionally propose transfer learning by pre-training on
related DDI data. Our model is trained and evaluated on the 2018 TAC DDI
corpus. Our GCA model in conjunction with transfer learning performs at 39.20%
F1 and 26.09% F1 on entity recognition (ER) and relation extraction (RE)
respectively on the first official test set and at 45.30% F1 and 27.87% F1 on
ER and RE respectively on the second official test set corresponding to an
improvement over our prior best results by up to 6 absolute F1 points. After
controlling for available training data, our model exhibits state-of-the-art
performance by improving over the next comparable best outcome by roughly three
F1 points in ER and 1.5 F1 points in RE evaluation across two official test
sets.
