Large Language Model Empowered Privacy-Protected Framework for PHI Annotation in Clinical Notes
This addresses privacy risks in medical data release for healthcare and research, though it is incremental as it builds on existing LLM methods with a focus on privacy and efficiency.
The paper tackled the problem of de-identifying private health information (PHI) in clinical notes by introducing LPPA, a framework that fine-tunes large language models locally with synthetic data, achieving high PHI annotation accuracy and strong privacy protection.
The de-identification of private information in medical data is a crucial process to mitigate the risk of confidentiality breaches, particularly when patient personal details are not adequately removed before the release of medical records. Although rule-based and learning-based methods have been proposed, they often struggle with limited generalizability and require substantial amounts of annotated data for effective performance. Recent advancements in large language models (LLMs) have shown significant promise in addressing these issues due to their superior language comprehension capabilities. However, LLMs present challenges, including potential privacy risks when using commercial LLM APIs and high computational costs for deploying open-source LLMs locally. In this work, we introduce LPPA, an LLM-empowered Privacy-Protected PHI Annotation framework for clinical notes, targeting the English language. By fine-tuning LLMs locally with synthetic notes, LPPA ensures strong privacy protection and high PHI annotation accuracy. Extensive experiments demonstrate LPPA's effectiveness in accurately de-identifying private information, offering a scalable and efficient solution for enhancing patient privacy protection.