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

A drug can affect the activity of other drugs, when administered together, in both synergistic or antagonistic ways. In one hand synergistic effects lead to improved therapeutic outcomes, antagonistic consequences can be life-threatening, leading to increased healthcare cost, or may even cause death. Thus, identification of unknown drug-drug interaction (DDI) is an important concern for efficient and effective healthcare. Although there exist multiple resources for DDI, they often unable to keep pace with rich amount of information available in fast growing biomedical texts including literature. Most existing methods model DDI extraction from text as classification problem and mainly rely on handcrafted features. Some of these features further depends on domain specific tools. Recently neural network models using latent features has shown to be perform similar or better than the other existing models using handcrafted features. In this paper, we present three models namely, B-LSTM, ABLSTM and Joint AB-LSTM based on long short-term memory (LSTM) network. All three models utilize word and position embedding as latent features and thus do not rely on feature engineering. Further use of bidirectional long short-term memory (Bi-LSTM) networks allow to extract optimal features from the whole sentence. The two models, AB-LSTM and Joint AB-LSTM also use attentive pooling in the output of Bi-LSTM layer to assign weights to features. Our experimental results on the SemEval-2013 DDI extraction dataset shows that the Joint AB-LSTM model outperforms all the existing methods, including those relying on handcrafted features. The other two proposed models also perform competitively with state-of-the-art methods.