One stage versus two stages deep learning approaches for the extraction of drug-drug interactions from texts

Abstract

Drug-drug interactions (DDI) are a cause of adverse drug reactions. They occur when a drug has an impact on the effect of another drug. There is not a complete, up to date database where health care professionals can consult the interactions of any drug because most of the knowledge on DDI is hidden in unstructured text. In last years, deep learning has been succesfully applied to the extraction of DDI from texts, which requires the detection and later classification of DDI. Most of the deep learning systems for DDI extraction developed so far have addressed the detection and classification in one single step. In this study, we compare the performance of one-stage and two-stage architectures for DDI extraction. Our architectures are based on a bidirectional recurrent neural network layer composed of Gated Recurrent Units. The two-stage system obtained a 67.45 % micro-average F1 score on the test set.