Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory Network

@article{Sahu2017DrugDrugIE,
  title={Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory Network},
  author={Sunil Kumar Sahu and Ashish Anand},
  journal={Journal of biomedical informatics},
  year={2017},
  volume={86},
  pages={
          15-24
        }
}
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104 Citations
Highly Influential Citations
9
Background Citations
21
Methods Citations
22

104 Citations

Drug-drug interaction extraction from biomedical literature using support vector machine and long short term memory networks

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Position-aware deep multi-task learning for drug-drug interaction extraction

Extracting drug-drug interactions from texts with BioBERT and multiple entity-aware attentions

Drug-Drug interaction extraction using a position and similarity fusion-based attention mechanism

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

Inspired by the deep learning approaches in natural language processing, a recurrent neural network model with multiple attention layers for DDI classification is proposed that outperforms the existing NLP or deep learning methods.

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

A novel recurrent hybrid convolutional neural network (RHCNN) for DDI extraction from biomedical literature and applies an improved focal loss function to mitigate against the defects of the traditional cross-entropy loss function when dealing with class imbalanced data.

Deep Convolution Neural Networks for Drug-Drug Interaction Extraction

A deep convolutional neural network (DCNN) based on DDI extraction method is presented, which obtains a better result than other state-of-the-art methods and increases as the network gets deeper and hits its peak at depth 16.

Leveraging Biomedical Resources in Bi-LSTM for Drug-Drug Interaction Extraction

A new bidirectional long–short-term memory (L STM) network-based method, namely, biomedical resource LSTM (BR-LSTM), which combines biomedical resource with lexical information and entity position information together to extract DDI from the biomedical literature is proposed.

Extracting Drug-drug Interactions with a Dependency-based Graph Convolution Neural Network

This article proposed a model that combines the graph convolution neural network (GCNN) and bidirectional long short-term memory (BiLSTM) to extract DDI interactions from entire dependency graphs of sentences to improve DDI extraction.
...

References

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