An Attentive Sequence Model for Adverse Drug Event Extraction from Biomedical Text

TL;DR

This paper introduces an attentive sequence model that leverages self-attention mechanisms to improve the extraction of adverse drug events from biomedical texts by modeling the problem as a question-answering task.

Contribution

It proposes a novel model inspired by machine reading comprehension techniques, focusing on intra-sequence interaction for better ADE extraction.

Findings

Effective use of self-attention for intra-sequence interaction

Enhanced visualization of model decision-making process

Improved accuracy in classifying drug and disease entities

Abstract

Adverse reaction caused by drugs is a potentially dangerous problem which may lead to mortality and morbidity in patients. Adverse Drug Event (ADE) extraction is a significant problem in biomedical research. We model ADE extraction as a Question-Answering problem and take inspiration from Machine Reading Comprehension (MRC) literature, to design our model. Our objective in designing such a model, is to exploit the local linguistic context in clinical text and enable intra-sequence interaction, in order to jointly learn to classify drug and disease entities, and to extract adverse reactions caused by a given drug. Our model makes use of a self-attention mechanism to facilitate intra-sequence interaction in a text sequence. This enables us to visualize and understand how the network makes use of the local and wider context for classification.