RoBIn: A Transformer-based model for risk of bias inference with machine reading comprehension

Scientific publications are essential for uncovering insights, testing new drugs, and informing healthcare policies. Evaluating the quality of these publications often involves assessing their Risk of Bias (RoB), a task traditionally performed by human reviewers. The goal of this work is to create a dataset and develop models that allow automated RoB assessment in clinical trials.

We use data from the Cochrane Database of Systematic Reviews (CDSR) as ground truth to label open-access clinical trial publications from PubMed. This process enabled us to develop training and test datasets specifically for machine reading comprehension and RoB inference. Additionally, we created extractive (RoBIn^Ext) and generative (RoBIn^Gen) Transformer-based approaches to extract relevant evidence and classify the RoB effectively.

RoBIn was evaluated across various settings and benchmarked against state-of-the-art methods, including large language models (LLMs). In most cases, the best-performing RoBIn variant surpasses traditional machine learning and LLM-based approaches, achieving a AUROC of 0.83.

This work addresses RoB assessment in clinical trials by introducing RoBIn, two Transformer-based models for RoB inference and evidence retrieval, which outperform traditional models and LLMs, demonstrating its potential to improve efficiency and scalability in clinical research evaluation. We also introduce a public dataset that is automatically annotated and can be used to enable future research to enhance automated RoB assessment.