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Entropy 2019, 21(1), 37; https://doi.org/10.3390/e21010037

Drug-Drug Interaction Extraction via Recurrent Hybrid Convolutional Neural Networks with an Improved Focal Loss

1
Department of Information Science and Technology, Northwest University, Xi’an 710127, China
2
Department of Computer Science, Xi’an Jiaotong University City College, Xi’an 710069, China
3
Department of Computer Information Science and Engineering, University of Florida, Gainesville, FL 32608, USA
*
Authors to whom correspondence should be addressed.
Received: 8 December 2018 / Revised: 2 January 2019 / Accepted: 3 January 2019 / Published: 8 January 2019
(This article belongs to the Special Issue Information Theoretic Learning and Kernel Methods)
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Abstract

Drug-drug interactions (DDIs) may bring huge health risks and dangerous effects to a patient’s body when taking two or more drugs at the same time or within a certain period of time. Therefore, the automatic extraction of unknown DDIs has great potential for the development of pharmaceutical agents and the safety of drug use. In this article, we propose a novel recurrent hybrid convolutional neural network (RHCNN) for DDI extraction from biomedical literature. In the embedding layer, the texts mentioning two entities are represented as a sequence of semantic embeddings and position embeddings. In particular, the complete semantic embedding is obtained by the information fusion between a word embedding and its contextual information which is learnt by recurrent structure. After that, the hybrid convolutional neural network is employed to learn the sentence-level features which consist of the local context features from consecutive words and the dependency features between separated words for DDI extraction. Lastly but most significantly, in order to make up for the defects of the traditional cross-entropy loss function when dealing with class imbalanced data, we apply an improved focal loss function to mitigate against this problem when using the DDIExtraction 2013 dataset. In our experiments, we achieve DDI automatic extraction with a micro F-score of 75.48% on the DDIExtraction 2013 dataset, outperforming the state-of-the-art approach by 2.49%. View Full-Text
Keywords: drug-drug interaction; convolutional neural network; dilated convolutions; cross-entropy; focal loss; relation extraction drug-drug interaction; convolutional neural network; dilated convolutions; cross-entropy; focal loss; relation extraction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Sun, X.; Dong, K.; Ma, L.; Sutcliffe, R.; He, F.; Chen, S.; Feng, J. Drug-Drug Interaction Extraction via Recurrent Hybrid Convolutional Neural Networks with an Improved Focal Loss. Entropy 2019, 21, 37.

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