Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers

TL;DR

This paper introduces a recurrent neural network with multiple attention layers for extracting drug-drug interactions from biomedical texts, outperforming previous NLP and deep learning methods on a standard dataset.

Contribution

The paper presents a novel deep learning model with multiple attention layers specifically designed for DDI classification, reducing reliance on feature engineering and NLP tools.

Findings

Outperforms existing NLP and deep learning methods on SemEval DDIExtraction dataset

Classifies most drug pairs into correct DDI categories

Demonstrates effectiveness of attention mechanisms in biomedical text analysis

Abstract

Drug-drug interaction (DDI) is a vital information when physicians and pharmacists intend to co-administer two or more drugs. Thus, several DDI databases are constructed to avoid mistakenly combined use. In recent years, automatically extracting DDIs from biomedical text has drawn researchers' attention. However, the existing work utilize either complex feature engineering or NLP tools, both of which are insufficient for sentence comprehension. Inspired by the deep learning approaches in natural language processing, we propose a recur- rent neural network model with multiple attention layers for DDI classification. We evaluate our model on 2013 SemEval DDIExtraction dataset. The experiments show that our model classifies most of the drug pairs into correct DDI categories, which outperforms the existing NLP or deep learning methods.