The process of matching patients with suitable clinical trials is essential for advancing medical research and providing optimal care. However, current approaches face challenges such as data standardization, ethical considerations, and a lack of interoperability between Electronic Health Records (EHRs) and clinical trial criteria. In this paper, we explore the potential of large language models (LLMs) to address these challenges by leveraging their advanced natural language generation capabilities to improve compatibility between EHRs and clinical trial descriptions. We propose an innovative privacy-aware data augmentation approach for LLM-based patient-trial matching (LLM-PTM), which balances the benefits of LLMs while ensuring the security and confidentiality of sensitive patient data. Our experiments demonstrate a 7.32% average improvement in performance using the proposed LLM-PTM method, and the generalizability to new data is improved by 12.12%. Additionally, we present case studies to further illustrate the effectiveness of our approach and provide a deeper understanding of its underlying principles.

翻译：将患者与合适的临床试验进行匹配对于推动医学研究和提供最佳护理至关重要。然而，当前的方法面临数据标准化、伦理考量以及电子健康记录（EHRs）与临床试验标准之间缺乏互操作性等挑战。本文通过利用大型语言模型（LLMs）先进的自然语言生成能力来改善EHRs与临床试验描述之间的兼容性，探讨了LLMs应对这些挑战的潜力。我们提出了一种创新的隐私感知数据增强方法，用于基于LLM的患者-试验匹配（LLM-PTM），该方法在平衡LLMs优势的同时，确保敏感患者数据的安全性和机密性。实验表明，采用所提出的LLM-PTM方法后，性能平均提升7.32%，且对新数据的泛化能力提高了12.12%。此外，我们通过案例研究进一步展示了该方法的有效性，并深入阐释了其基本原理。