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