Handling Name Errors of a BERT-Based De-Identification System: Insights from Stratified Sampling and Markov-based Pseudonymization

Dalton Simancek, VG Vinod Vydiswaran


Abstract
Missed recognition of named entities while de-identifying clinical narratives poses a critical challenge in protecting patient-sensitive health information. Mitigating name recognition errors is essential to minimize risk of patient re-identification. In this paper, we emphasize the need for stratified sampling and enhanced contextual considerations concerning Name Tokens using a fine-tuned Longformer BERT model for clinical text de-identifcation. We introduce a Hidden in Plain Sight (HIPS) Markov-based replacement technique for names to mask name recognition misses, revealing a significant reduction in name leakage rates. Our experimental results underscore the impact on addressing name recognition challenges in BERT-based de-identification systems for heightened privacy protection in electronic health records.
Anthology ID:
2024.caldpseudo-1.1
Volume:
Proceedings of the Workshop on Computational Approaches to Language Data Pseudonymization (CALD-pseudo 2024)
Month:
March
Year:
2024
Address:
St. Julian’s, Malta
Editors:
Elena Volodina, David Alfter, Simon Dobnik, Therese Lindström Tiedemann, Ricardo Muñoz Sánchez, Maria Irena Szawerna, Xuan-Son Vu
Venues:
CALD-pseudo | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–7
Language:
URL:
https://aclanthology.org/2024.caldpseudo-1.1
DOI:
Bibkey:
Cite (ACL):
Dalton Simancek and VG Vinod Vydiswaran. 2024. Handling Name Errors of a BERT-Based De-Identification System: Insights from Stratified Sampling and Markov-based Pseudonymization. In Proceedings of the Workshop on Computational Approaches to Language Data Pseudonymization (CALD-pseudo 2024), pages 1–7, St. Julian’s, Malta. Association for Computational Linguistics.
Cite (Informal):
Handling Name Errors of a BERT-Based De-Identification System: Insights from Stratified Sampling and Markov-based Pseudonymization (Simancek & Vydiswaran, CALD-pseudo-WS 2024)
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