Bat4RCT: A suite of benchmark data and baseline methods for text classification of randomized controlled trials

• Published in: PloS one 2023-03, Vol.18 (3), p.e0283342-e0283342
• Main Authors: Kim, Jenna, Kim, Jinmo, Lee, Aejin, Kim, Jinseok
• Format: Article
• Language: English
• Subjects: Analysis; Benchmarking; Biology and Life Sciences; Biomedical research; Classification; Clinical trials; Computational linguistics; Computer and Information Sciences; Crowdsourcing; Data mining; Datasets; Deep learning; Evaluation; Information management; Information retrieval; Language; Language processing; Learning algorithms; Machine Learning; Medical research; Medicine and Health Sciences; Medicine, Experimental; MEDLINE; Modelling; Natural language interfaces; Natural Language Processing; Neural networks; Randomized Controlled Trials as Topic; Research and Analysis Methods; Social Sciences; Support vector machines; Tagging; Text categorization
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0283342

Randomized controlled trials (RCTs) play a major role in aiding biomedical research and practices. To inform this research, the demand for highly accurate retrieval of scientific articles on RCT research has grown in recent decades. However, correctly identifying all published RCTs in a given domain is a non-trivial task, which has motivated computer scientists to develop methods for identifying papers involving RCTs. Although existing studies have provided invaluable insights into how RCT tags can be predicted for biomedicine research articles, they used datasets from different sources in varying sizes and timeframes and their models and findings cannot be compared across studies. In addition, as datasets and code are rarely shared, researchers who conduct RCT classification have to write code from scratch, reinventing the wheel. In this paper, we present Bat4RCT, a suite of data and an integrated method to serve as a strong baseline for RCT classification, which includes the use of BERT-based models in comparison with conventional machine learning techniques. To validate our approach, all models are applied on 500,000 paper records in MEDLINE. The BERT-based models showed consistently higher recall scores than conventional machine learning and CNN models while producing slightly better or similar precision scores. The best performance was achieved by the BioBERT model when trained on both title and abstract texts, with the F1 score of 90.85%. This infrastructure of dataset and code will provide a competitive baseline for the evaluation and comparison of new methods and the convenience of future benchmarking. To our best knowledge, our study is the first work to apply BERT-based language modeling techniques to RCT classification tasks and to share dataset and code in order to promote reproducibility and improvement in text classification in biomedicine research.