DIRI: Adversarial Patient Reidentification with Large Language Models for Evaluating Clinical Text Anonymization.

Area of Science:

• Biomedical Informatics
• Natural Language Processing
• Data Privacy

Background:

• Sharing protected health information (PHI) is vital for biomedical research.
• Deidentification is crucial for removing PHI from clinical text before data distribution.
• Current deidentification methods are often evaluated on limited datasets, potentially overestimating real-world performance.

Purpose of the Study:

• To develop and evaluate a novel adversarial method using a large language model (LLM) to re-identify patients from de-identified clinical notes.
• To assess the effectiveness of state-of-the-art deidentification tools against a re-identification attack.
• To highlight limitations in current deidentification technologies and provide a tool for iterative improvement.

Main Methods:

• Developed an adversarial approach using a large language model (LLM) for re-identification.
• Introduced a De-Identification/Re-Identification (DIRI) method to evaluate deidentification tool performance.
• Tested the method on clinical data from Weill Cornell Medicine anonymized using Philter, BiLSTM-CRF, and ClinicalBERT.

Main Results:

• The LLM-based re-identification tool successfully re-identified 9% of clinical notes, even those processed by the most effective deidentification tool (ClinicalBERT).
• This demonstrates significant weaknesses in current deidentification technologies.
• The DIRI method provides a robust evaluation framework for deidentification tools.

Conclusions:

• Existing deidentification technologies exhibit significant vulnerabilities.
• The developed LLM-based re-identification method can effectively challenge and expose these weaknesses.
• Continuous improvement and novel approaches are necessary to ensure robust patient privacy in biomedical research.