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

TL;DR

This paper investigates how large language models can enhance patient-trial matching in healthcare by improving data compatibility and privacy, demonstrating significant performance gains and generalizability in experimental evaluations.

Contribution

It introduces a privacy-aware data augmentation method using LLMs for patient-trial matching, addressing interoperability and confidentiality challenges in healthcare data.

Findings

7.32% performance improvement

12.12% better generalizability

Effective case studies illustrating approach

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. 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.