Asian Journal of Complementary and Alternative Medicine

Asian Journal of Complementary and Alternative Medicine

Publisher Home

Short Review on srBERT: Automatic Article Classification Model for Systematic Review Using BERT

Seon Choe1, Sungmin Aum2,3, Ju Han Kim1*

1Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul, 03080, South Korea 

2Korea Institute of Science and Technology (KIST), Robotics and Media Institute 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul

3University of Science and Technology (UST), Division of Nano and Information Technology, 34113, Gajeong-ro, Yuseong-gu, Daejeon, Republic of Korea  


Systematic reviews (SRs) have been recognized as the most rigorous and reliable approach to enable evidence-based medicine. However, the considerable workload required to create SRs prevents reflecting the latest knowledge. This study automated the classification of included articles by adopting the Bidirectional Encoder Representations from Transformers (BERT) algorithm. By pretraining with abstracts of articles and a generated vocabulary fine-tuned with titles of articles, the proposed srBERTmy overcomes the training data insufficiency while improving performance in both classification and relation-extraction tasks. Despite the limitation of model vulnerabilities based on training dataset quality, the results demonstrated the feasibility of automatic article classification using machine-learning (ML) approaches to support SR tasks

Systematic review, process automation, deep learning, text mining
Powered By

Subscribe to our newsletter and stay up to date with the latest news and deals!

Connect via