Drug-Drug Interaction Extraction from Biomedical Texts via Relation BERT

There is a large number of drugs introduced every year and a number of interactions between drugs also has quick growth. As a result, biomedical texts following new drugs and interactions expand [15]. Several published studies of drug safety have revealed that drug-drug interactions (DDIs) may be detected too late, when millions of patients have already been exposed [25]. Therefore, the management of drug drug interactions is critical issue since the importance of known drug drug interaction and the giant amount of available information around them [5]. Thus, the issue creates an imperative need for the development of high-reliable automatic DDI extraction methods while manual DDI extraction is time-consuming and could lead to out-of-date information. However, the accuracy of the current automatic DDI extraction method is still insufficient for the practical application. In this research, we explore the Relation Bidirectional Encoder Representations from Transformers (Relation BERT) architecture [32] to detect and classify DDIs from biomedical texts using the DDI extraction 2013 corpus [5] and present three proposed models namely R-BERT∗, R-BioBERT1, and R-BioBERT2. From our knowledge, we are the first to investigate the potential of Relation BERT for the aim of accuracy improvement in DDI extraction. By using the state-of-the-art word representation method, three models produce macro-average F1-score of over 79%. Moreover, the accuracy of extracting Advice and Mechanism interaction achieves 90.63% and 97% respectively in terms of F1-score. The high accuracy of the model in Advice and Mechanism interaction creates motivation for wide application of automatic DDI extraction to the practice with high-reliable and humanless.