Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers
This work addresses a critical need for physicians and pharmacists to avoid harmful drug combinations by improving automated extraction from text, though it appears incremental in method.
The authors tackled the problem of automatically extracting drug-drug interactions from biomedical text by proposing a recurrent neural network with multiple attention layers, which outperformed existing methods on the 2013 SemEval dataset.
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.