Drug-Drug Interaction Classification Using Attention Based Neural Networks

D. Zaikis and I. Vlahavas, "Drug-Drug Interaction Classification Using Attention Based Neural Networks", In Proceedings of the 11th Hellenic Conference on Artificial Intelligence (SETN '20), Athens, Greece, 2-4 Sep 2020, pp. 34–40, Association for Computing Machinery, 2020

Drug-drug interaction, Machine Learning, Neural Networks, Natural Language Processing, Attention

Drug-drug interaction (DDI) identification is the task of identifying potential interactions between drugs when administered simultaneously. The interactions can be synergetic or antagonistic as one drug can affect the other. Adverse drug reactions caused by antagonistic DDI can pose a serious threat to health and potentially lead to greater increase in health care expenditure. Multiple excellent resources for DDI already exist, although unable to keep up with the exponential increase in published biomedical literature. Most existing systems rely on handcrafted features to extract and classify the relationships between drugs. In this paper, we present a deep learning method of stacked bidirectional Long Short Term Memory (Bi-LSTM) and Convolutional neural (CNN) networks that utilize word embeddings, part-of-speech tags and distance embeddings respectively to perform the DDI extraction task and aid the drug development cycle and drug repurposing. Furthermore, the model uses attention mechanism to better focus on importance of all the hidden states of the Bi-LSTM layers. Experimental results show that our method can better avoid misclassifications of instances with a minimal preprocessing.