When one drug interacts with another, it is known as a drug-drug interaction. This could change how one or both drugs work in the body, or induce unforeseen adverse effects. The performance of a combination can be effectively degraded or improved by mixing different medications. In some cases, it can adversely affect the patient's health. So, it's of great importance and time demanding to classify the interaction of drugs. Drug-drug interaction is amongst the top challenging and far-reaching applications of natural language processing. In this work, we have presented drug-drug interaction using the BERT (Bidirectional Encoder Representations from Transformers) model. The previous state of the art performance on the DDI Extraction 2013 corpus was using different variations of convolutional neural networks and LSTMs. In order to validate our proposed model, well-known benchmark data set are used and our BERT-based classification has achieved a much higher score than previous methods at 90.69% accuracy and 81.97% f1-score.
Authors: Tanmoy Tapos Datta, Pintu Chandra Shill, Zabir Al Nazi
DOI: https://doi.org/10.1109/iconat53423.2022.9725979
Publish Year: 2022
