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

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 drug 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 recurrent 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.

[1]  J. Wagner,et al.  Enhanced Theophylline Clearance Secondary to Phenytoin Therapy , 1985, Drug intelligence & clinical pharmacy.

[2]  P. Corey,et al.  Incidence of Adverse Drug Reactions in Hospitalized Patients , 2012 .

[3]  Dekang Lin,et al.  WordNet: An Electronic Lexical Database , 1998 .

[4]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[5]  Tuomas Korhonen,et al.  SFINX—a drug-drug interaction database designed for clinical decision support systems , 2009, European Journal of Clinical Pharmacology.

[6]  Chitta Baral,et al.  Discovering drug–drug interactions: a text-mining and reasoning approach based on properties of drug metabolism , 2010, Bioinform..

[7]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[8]  Susumu Goto,et al.  Network-Based Analysis and Characterization of Adverse Drug-Drug Interactions , 2011, J. Chem. Inf. Model..

[9]  Alberto Lavelli,et al.  FBK-irst : A Multi-Phase Kernel Based Approach for Drug-Drug Interaction Detection and Classification that Exploits Linguistic Information , 2013, *SEMEVAL.

[10]  Paloma Martínez,et al.  The DDI corpus: An annotated corpus with pharmacological substances and drug-drug interactions , 2013, J. Biomed. Informatics.

[11]  Mariana L. Neves,et al.  WBI-DDI: Drug-Drug Interaction Extraction using Majority Voting , 2013, *SEMEVAL.

[12]  Alberto Lavelli,et al.  Exploiting the Scope of Negations and Heterogeneous Features for Relation Extraction: A Case Study for Drug-Drug Interaction Extraction , 2013, HLT-NAACL.

[13]  Majid Rastegar-Mojarad,et al.  Extraction and classification of drug-drug interaction from biomedical text using a twostage classifier , 2013 .

[14]  Mikhail P. Melnikov,et al.  Retrieval of Drug-Drug Interactions Information from Biomedical Texts: Use of TF-IDF for Classification , 2014, JCKBSE.

[15]  Jun Zhao,et al.  Relation Classification via Convolutional Deep Neural Network , 2014, COLING.

[16]  Peter M. A. Sloot,et al.  A novel feature-based approach to extract drug-drug interactions from biomedical text , 2014, Bioinform..

[17]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[18]  Yi-Cheng Tu,et al.  A novel algorithm for analyzing drug-drug interactions from MEDLINE literature , 2015, Scientific Reports.

[19]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[20]  Ming Yang,et al.  Bidirectional Long Short-Term Memory Networks for Relation Classification , 2015, PACLIC.

[21]  Ralph Grishman,et al.  Relation Extraction: Perspective from Convolutional Neural Networks , 2015, VS@HLT-NAACL.

[22]  Angélique Stéphanou,et al.  Towards the Design of a Patient-Specific Virtual Tumour , 2016, Comput. Math. Methods Medicine.

[23]  Kai Chen,et al.  Dependency-based convolutional neural network for drug-drug interaction extraction , 2016, 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[24]  Zhiyuan Liu,et al.  Neural Relation Extraction with Selective Attention over Instances , 2016, ACL.

[25]  Wei Shi,et al.  Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification , 2016, ACL.

[26]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[27]  Hongfei Lin,et al.  Drug drug interaction extraction from biomedical literature using syntax convolutional neural network , 2016, Bioinform..

[28]  Zhiyuan Liu,et al.  Relation Classification via Multi-Level Attention CNNs , 2016, ACL.

[29]  Xiaolong Wang,et al.  Drug-Drug Interaction Extraction via Convolutional Neural Networks , 2016, Comput. Math. Methods Medicine.

[30]  Sunil Kumar Sahu,et al.  Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory Network , 2017, J. Biomed. Informatics.