1. Drug–drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths
- Author
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Michel Dumontier, Hongfei Lin, Zhihao Yang, Wei Zheng, Jian Wang, Yijia Zhang, Institute of Data Science, and RS: FSE DACS IDS
- Subjects
0301 basic medicine ,Statistics and Probability ,Dependency (UML) ,Computer science ,Data Mining/methods ,computer.software_genre ,Biochemistry ,03 medical and health sciences ,Pharmacovigilance ,0302 clinical medicine ,Feature (machine learning) ,Data Mining ,Humans ,Drug Interactions ,030212 general & internal medicine ,Representation (mathematics) ,Molecular Biology ,Sequence ,Artificial neural network ,business.industry ,Publications ,Neural Networks (Computer) ,Syntax ,Original Papers ,Computer Science Applications ,Computational Mathematics ,030104 developmental biology ,Recurrent neural network ,Computational Theory and Mathematics ,Path (graph theory) ,Embedding ,Artificial intelligence ,Neural Networks, Computer ,Data and Text Mining ,business ,computer ,Sentence ,Natural language processing - Abstract
Motivation Adverse events resulting from drug-drug interactions (DDI) pose a serious health issue. The ability to automatically extract DDIs described in the biomedical literature could further efforts for ongoing pharmacovigilance. Most of neural networks-based methods typically focus on sentence sequence to identify these DDIs, however the shortest dependency path (SDP) between the two entities contains valuable syntactic and semantic information. Effectively exploiting such information may improve DDI extraction. Results In this article, we present a hierarchical recurrent neural networks (RNNs)-based method to integrate the SDP and sentence sequence for DDI extraction task. Firstly, the sentence sequence is divided into three subsequences. Then, the bottom RNNs model is employed to learn the feature representation of the subsequences and SDP, and the top RNNs model is employed to learn the feature representation of both sentence sequence and SDP. Furthermore, we introduce the embedding attention mechanism to identify and enhance keywords for the DDI extraction task. We evaluate our approach using the DDI extraction 2013 corpus. Our method is competitive or superior in performance as compared with other state-of-the-art methods. Experimental results show that the sentence sequence and SDP are complementary to each other. Integrating the sentence sequence with SDP can effectively improve the DDI extraction performance. Availability and implementation The experimental data is available at https://github.com/zhangyijia1979/hierarchical-RNNs-model-for-DDI-extraction. Supplementary information Supplementary data are available at Bioinformatics online.
- Published
- 2017