scholarly journals Drug drug interaction extraction from biomedical literature using syntax convolutional neural network

2016 ◽  
pp. btw486 ◽  
Author(s):  
Zhehuan Zhao ◽  
Zhihao Yang ◽  
Ling Luo ◽  
Hongfei Lin ◽  
Jian Wang
Keyword(s):  
Neural Network ◽  
Drug Interaction ◽  
Convolutional Neural Network ◽  
Biomedical Literature ◽  
Interaction Extraction ◽  
Drug Drug Interaction

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

Entropy ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 37 ◽  
Author(s):  
Xia Sun ◽  
Ke Dong ◽  
Long Ma ◽  
Richard Sutcliffe ◽  
Feijuan He ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Loss Function ◽  
Contextual Information ◽  
Imbalanced Data ◽  
Biomedical Literature ◽  
Local Context ◽  
Automatic Extraction ◽  
Sentence Level ◽  
Interaction Extraction

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%.

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