An attention-based effective neural model for drug-drug interactions extraction

BMC Bioinformatics

Research Article

Zhihao Yang¹  Ling Luo¹  Yijia Zhang¹  Jian Wang¹  Zhehuan Zhao¹  Hongfei Lin¹  Zhengguang Li²  Wei Zheng² 

[1] College of Computer Science and Technology, Dalian University of Technology, Dalian, China;College of Computer Science and Technology, Dalian University of Technology, Dalian, China;College of Software, Dalian JiaoTong University, Dalian, China;

关键词: Attention;    Recurrent neural network;    Long short-term memory;    Drug-drug interactions;    Text mining;   

DOI  :  10.1186/s12859-017-1855-x

 received in 2017-04-28, accepted in 2017-10-02,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundDrug-drug interactions (DDIs) often bring unexpected side effects. The clinical recognition of DDIs is a crucial issue for both patient safety and healthcare cost control. However, although text-mining-based systems explore various methods to classify DDIs, the classification performance with regard to DDIs in long and complex sentences is still unsatisfactory.MethodsIn this study, we propose an effective model that classifies DDIs from the literature by combining an attention mechanism and a recurrent neural network with long short-term memory (LSTM) units. In our approach, first, a candidate-drug-oriented input attention acting on word-embedding vectors automatically learns which words are more influential for a given drug pair. Next, the inputs merging the position- and POS-embedding vectors are passed to a bidirectional LSTM layer whose outputs at the last time step represent the high-level semantic information of the whole sentence. Finally, a softmax layer performs DDI classification.ResultsExperimental results from the DDIExtraction 2013 corpus show that our system performs the best with respect to detection and classification (84.0% and 77.3%, respectively) compared with other state-of-the-art methods. In particular, for the Medline-2013 dataset with long and complex sentences, our F-score far exceeds those of top-ranking systems by 12.6%.ConclusionsOur approach effectively improves the performance of DDI classification tasks. Experimental analysis demonstrates that our model performs better with respect to recognizing not only close-range but also long-range patterns among words, especially for long, complex and compound sentences.

【 授权许可】

CC BY   
© The Author(s). 2017

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