Abstract
Extracting drug-drug interaction (DDI) in the text is the process of identifying how two target drugs in a given sentence interact. Previous methods, which were limited to conventional machine learning techniques, we are susceptible to issues such as “vocabulary gap” and unattainable automation processes in feature extraction. Inspired by deep learning in natural language preprocessing, we addressed the aforementioned problems based on dynamic transfer matrix and memory networks. A TM-RNN method is proposed by adding the transfer weight matrix in multilayer bidirectional LSTM to improve robustness and introduce a memory network for feature fusion. We evaluated the TM-RNN model on the DDIExtraction 2013 Task. The proposed model achieved an overall F-score of 72.43, which outperforms the latest methods based on support vector machine and other neural networks. Meanwhile, the experimental results also indicated that the proposed model is more stable and less affected by negative samples.
Highlights
Drug–drug interaction (DDI) is a situation where one drug increases or decreases the effect of another drug entity [1], [2]
Inspired by deep learning in natural language preprocessing, we propose a drug–drug interaction extraction method based on transfer weight matrix and memory network
In this study, we propose a multilayer bidirectional long short-term memory (LSTM) with transfer weight matrix and memory network to solve the DDI classification problem
Summary
Drug–drug interaction (DDI) is a situation where one drug increases or decreases the effect of another drug entity [1], [2]. According to the survey of [3], the number of individuals who take multiple drugs simultaneously has considerably increased. The interactions amongst these drugs may be harmful to the human body. Building a reliable DDI system or database is necessary to avoid certain drug abuse medical accidents. With the rapid growth of biomedical scientific publications (for example, the MedLine database has doubled in size in the past ten years), the need for an automatic DDI extraction system is urgent. DDI extraction can be considered a classification problem, that is, a decision should be made whether a relation
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