SECRET: Semi-supervised Clinical Trial Document Similarity Search

TL;DR

This paper introduces SECRET, a semi-supervised method for identifying similar clinical trials by summarizing protocols, significantly improving search accuracy and utility in trial design and patient matching.

Contribution

The paper presents a novel semi-supervised approach for clinical trial similarity search that outperforms existing baselines in multiple evaluation metrics.

Findings

Up to 78% improvement in recall@1

Up to 53% improvement in precision@1

Outperforms baselines in partial similarity and zero-shot matching

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.