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    <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.</abstract>
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%0 Conference Proceedings
%T Handling Name Errors of a BERT-Based De-Identification System: Insights from Stratified Sampling and Markov-based Pseudonymization
%A Simancek, Dalton
%A Vydiswaran, VG Vinod
%Y Volodina, Elena
%Y Alfter, David
%Y Dobnik, Simon
%Y Lindström Tiedemann, Therese
%Y Muñoz Sánchez, Ricardo
%Y Szawerna, Maria Irena
%Y Vu, Xuan-Son
%S Proceedings of the Workshop on Computational Approaches to Language Data Pseudonymization (CALD-pseudo 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F simancek-vydiswaran-2024-handling
%X 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.
%U https://aclanthology.org/2024.caldpseudo-1.1
%P 1-7

Markdown (Informal)

[Handling Name Errors of a BERT-Based De-Identification System: Insights from Stratified Sampling and Markov-based Pseudonymization](https://aclanthology.org/2024.caldpseudo-1.1) (Simancek & Vydiswaran, CALD-pseudo-WS 2024)

• Handling Name Errors of a BERT-Based De-Identification System: Insights from Stratified Sampling and Markov-based Pseudonymization (Simancek & Vydiswaran, CALD-pseudo-WS 2024)

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.