用摘要匹配历史临床试验，提升设计成功率。
SECRET: Semi-supervised Clinical Trial Document Similarity Search
- 将临床试验协议摘要化后进行相似性搜索。
- 召回率最高提升78%，精确率最高提升53%。
- 适合药物研发人员快速参考历史经验。
临床试验对新疗法的安全性和有效性评估至关重要，但其成本高、耗时长且易出错，可能导致延误、损失和声誉损害。因此，科学决策在试验设计中尤为关键。识别相似的历史试验可为潜在问题（如严重不良事件、剂量错误、招募困难、患者依从性差等）提供重要参考，从而优化研究方案，提升患者安全与试验效率。本文提出一种新方法：通过总结临床试验方案，并基于查询试验的协议搜索相似历史试验。实验表明，该方法显著优于所有基线模型，在recall@1上最高提升78%，precision@1上最高提升53%。此外，在部分试验相似性搜索和零样本患者-试验匹配任务中也全面领先，展现出更强实用性。
原文摘要 · Abstract (English)
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.
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