Log In Sign Up

Attention-Gated Graph Convolutions for Extracting Drug Interaction Information from Drug Labels

by   Tung Tran, et al.

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 respectively on the first official test set and at 45.30 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.


page 7

page 16


Attention-Gated Graph Convolution for Extracting Drugs and Their Interactions from Drug Labels

Preventable adverse events as a result of medical errors present a growi...

A Multi-Task Learning Framework for Extracting Drugs and Their Interactions from Drug Labels

Preventable adverse drug reactions as a result of medical errors present...

Extracting Adverse Drug Events from Clinical Notes

Adverse drug events (ADEs) are unexpected incidents caused by the admini...

A Sui Generis QA Approach using RoBERTa for Adverse Drug Event Identification

Extraction of adverse drug events from biomedical literature and other t...

NER Models Using Pre-training and Transfer Learning for Healthcare

In this paper, we present our approach to extract structured information...

Toward Robust Drug-Target Interaction Prediction via Ensemble Modeling and Transfer Learning

Drug-target interaction (DTI) prediction plays a crucial role in drug di...

View Distillation with Unlabeled Data for Extracting Adverse Drug Effects from User-Generated Data

We present an algorithm based on multi-layer transformers for identifyin...