DeIDClinic: A Risk-Aware Pseudonymization Framework for Clinical Text De-identification and Re-identification Risk Assessment
arXiv:2410.01648v2 Announce Type: replace Abstract: The increasing availability of sensitive textual data has created an urgent need for robust de-identification methods that enable compliant data sharing while preserving downstream utility. This paper presents DeID-Clinic, a multi-layered framework for automated pseudonymization and re-identification risk assessment of clinical free-text data. Our approach integrates domain-adapted transformer models, including BioBERT and ClinicalBERT, into the MASK de-identification framework to improve the detection and masking of protected health informat
The increasing availability of sensitive textual data combined with advancements in transformer models creates an immediate need and opportunity for robust de-identification and privacy-preserving AI solutions.
This framework addresses a critical barrier to compliant data sharing in healthcare, enabling greater utilization of clinical data for research and AI development while mitigating re-identification risks.
The ability to pseudonymize sensitive clinical text more effectively and with transparent risk assessment will accelerate the development of AI applications in healthcare by making data more accessible.
- · Healthcare AI Developers
- · Medical Researchers
- · Patients (via improved treatments)
- · Cloud Providers (specializing in healthcare data)
- · Legacy Data Anonymization Solutions
- · Data Brokers (reliant on less-secure methods)
More clinical data becomes available for AI training, accelerating model development in healthcare.
Improved diagnostic tools and personalized medicine emerge faster due to richer datasets.
Enhanced data privacy standards become a competitive advantage for healthcare institutions and technology providers.
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Read at arXiv cs.CL