Drug–Drug Interaction Relation Extraction Based on Deep Learning: A Review

Abstract

Drug–drug interaction (DDI) is an important part of drug development and pharmacovigilance. At the same time, DDI is an important factor in treatment planning, monitoring effects of medicine and patient safety, and has a significant impact on public health. Therefore, using deep learning technology to extract DDI from scientific literature has become a valuable research direction to researchers. In existing DDI datasets, the number of positive instances is relatively small. This makes it difficult for existing deep learning models to obtain sufficient feature information directly from text data. Therefore, existing deep learning models mainly rely on multiple feature supplementation methods to collect sufficient feature information from different types of data. In this study, the general process of DDI relation extraction based on deep learning is introduced first for comprehensive analysis. Next, we summarize the various feature supplement methods and analyze their merits and demerits. We then review the state-of-the-art literature related to DDI extraction from the deep neural network perspective. Finally, all the feature supplement methods are compared, and some suggestions are given to approach the current problems and future research directions. The purpose of this article is to give researchers a more complete understanding of the feature complementation methods used in DDI extraction to be able to rapidly design and implement custom DDI relation extraction methods.