Work

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

Year: 2025

Type: conference-paper

Abstract: 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

Source: IEEE International Conference on Healthcare Informatics (ICHI)

DOI: 10.1109/ichi64645.2025.00098

Authors: Dhivin Shaji, Angel Paul, Lifeng Han, Warren Del-Pinto, Goran Nenadic, Suzan Verberne

Institutions: University of Manchester, Leiden University, Leiden University Medical Center

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funder: HORIZON EUROPE Health