De-identifying Clinical Texts using Biomed-Clinical BERTs and Comprehensive Risk Assessment

This work focuses on the de-identification and risk assessment of clinical text using Transformer-based biomedical models BioBERT and ClinicalBERT. The primary objective is to accurately identify and mask sensitive entities while maintaining data utility for research. We conduct a detailed comparative analysis between BioBERT, ClinicalBERT, and other models, including NER-BERT, NER-CRF, and LSTMs. We integrate BioBERT and ClinicalBERT into the MASK framework to facilitate effective de-identification (MASK-BioBERT / ClinicalBERT). We introduce a risk assessment tool that evaluates the potential re-identification risks of clinical data post-de-identification. The developed methods contribute to safeguarding patient privacy while supporting the usability of healthcare data for medical research. Our models and platform are publicly available at https://github.com/angelpaulml17/DeIDClinic