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Robust Privacy Amidst Innovation with Large Language Models Through a Critical Assessment of the Risks by Chuang, Yao-Shun; Sarkar, Atiquer Rahman; Hsu, Yu-Chun; Mohammed, Noman; Jiang, Xiaoqian is a scholarly article available to read on EtoBox.

What is Robust Privacy Amidst Innovation with Large Language Models Through a Critical Assessment of the Risks about?

This study examines integrating EHRs and NLP with large language models (LLMs) to improve healthcare data management and patient care. It focuses on using advanced models to create secure, HIPAA-compliant synthetic patient notes for biomedical research. The study used de-identified and re-identified MIMIC III datasets with GPT-3.5, GPT-4, and Mistral 7B to generate synthetic notes. Text generation employed templates and keyword extraction for contextually relevant notes, with one-shot generation for comparison. Privacy assessment checked PHI occurrence, while text utility was tested using an ICD-9 coding task. Text quality was evaluated with ROUGE and cosine similarity metrics to measure semantic similarity with source notes. Analysis of PHI occurrence and text utility via the ICD-9 coding task showed that the keyword-based method had low risk and good performance. One-shot generation showed the highest PHI exposure and PHI co-occurrence, especially in geographic location and date categories. The Normalized One-shot method achieved the highest classification accuracy. Privacy analysis revealed a critical balance between data utility and privacy protection, influencing future data u

Author

Chuang, Yao-Shun; Sarkar, Atiquer Rahman; Hsu, Yu-Chun; Mohammed, Noman; Jiang, Xiaoqian

Published

2024

Language

EN