SECRET: Semi-supervised Clinical Trial Document Similarity Search
Journal:
arXiv
Published Date:
May 16, 2025
Abstract
Clinical trials are vital for evaluation of safety and efficacy of new
treatments. However, clinical trials are resource-intensive, time-consuming and
expensive to conduct, where errors in trial design, reduced efficacy, and
safety events can result in significant delays, financial losses, and damage to
reputation. These risks underline the importance of informed and strategic
decisions in trial design to mitigate these risks and improve the chances of a
successful trial. Identifying similar historical trials is critical as these
trials can provide an important reference for potential pitfalls and challenges
including serious adverse events, dosage inaccuracies, recruitment
difficulties, patient adherence issues, etc. Addressing these challenges in
trial design can lead to development of more effective study protocols with
optimized patient safety and trial efficiency. In this paper, we present a
novel method to identify similar historical trials by summarizing clinical
trial protocols and searching for similar trials based on a query trial's
protocol. Our approach significantly outperforms all baselines, achieving up to
a 78% improvement in recall@1 and a 53% improvement in precision@1 over the
best baseline. We also show that our method outperforms all other baselines in
partial trial similarity search and zero-shot patient-trial matching,
highlighting its superior utility in these tasks.
