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Title: LLM for Patient-Trial Matching: Privacy-Aware Data Augmentation Towards Better Performance and Generalizability
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.  more » « less
Award ID(s):
2124789
NSF-PAR ID:
10448809
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
American Medical Informatics Association (AMIA) Annual Symposium
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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