Transformer-Based Language Models for Group Randomized Trial Classification in Biomedical Literature: Model Development and Validation.

Background: For the public health community, monitoring recently published articles is crucial for staying informed about the latest research developments. However, identifying publications about studies with specific research designs from the extensive body of public health publications is a challenge with the currently available methods.

Objective: Our objective is to develop a fine-tuned pretrained language model that can accurately identify publications from clinical trials that use a group- or cluster-randomized trial (GRT), individually randomized group-treatment trial (IRGT), or stepped wedge group- or cluster-randomized trial (SWGRT) design within the biomedical literature.

Methods: We fine-tuned the BioMedBERT language model using a dataset of biomedical literature from the Office of Disease Prevention at the National Institute of Health. The model was trained to classify publications into three categories of clinical trials that use nested designs. The model performance was evaluated on unseen data and demonstrated high sensitivity and specificity for each class.

Results: When our proposed model was tested for generalizability with unseen data, it delivered high sensitivity and specificity for each class as follows: negatives (0.95 and 0.93), GRTs (0.94 and 0.90), IRGTs (0.81 and 0.97), and SWGRTs (0.96 and 0.99), respectively.

Conclusions: Our work demonstrates the potential of fine-tuned, domain-specific language models to accurately identify publications reporting on complex and specialized study designs, addressing a critical need in the public health research community. This model offers a valuable tool for the public health community to directly identify publications from clinical trials that use one of the three classes of nested designs.