Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching.

ABSTRACT: 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.

SUBMITTER: Yuan J 

PROVIDER: S-EPMC10785941 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature