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        <title>Generalizing Clinical De-identification Models by Privacy-safe Data Augmentation using GPT-4</title>
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    <abstract>De-identification (de-ID) refers to removing the association between a set of identifying data and the data subject. In clinical data management, the de-ID of Protected Health Information (PHI) is critical for patient confidentiality. However, state-of-the-art de-ID models show poor generalization on a new dataset. This is due to the difficulty of retaining training corpora. Additionally, labeling standards and the formats of patient records vary across different institutions. Our study addresses these issues by exploiting GPT-4 for data augmentation through one-shot and zero-shot prompts. Our approach effectively circumvents the problem of PHI leakage, ensuring privacy by redacting PHI before processing. To evaluate the effectiveness of our proposal, we conduct cross-dataset testing. The experimental result demonstrates significant improvements across three types of F1 scores.</abstract>
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%0 Conference Proceedings
%T Generalizing Clinical De-identification Models by Privacy-safe Data Augmentation using GPT-4
%A Kim, Woojin
%A Hahm, Sungeun
%A Lee, Jaejin
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F kim-etal-2024-generalizing
%X De-identification (de-ID) refers to removing the association between a set of identifying data and the data subject. In clinical data management, the de-ID of Protected Health Information (PHI) is critical for patient confidentiality. However, state-of-the-art de-ID models show poor generalization on a new dataset. This is due to the difficulty of retaining training corpora. Additionally, labeling standards and the formats of patient records vary across different institutions. Our study addresses these issues by exploiting GPT-4 for data augmentation through one-shot and zero-shot prompts. Our approach effectively circumvents the problem of PHI leakage, ensuring privacy by redacting PHI before processing. To evaluate the effectiveness of our proposal, we conduct cross-dataset testing. The experimental result demonstrates significant improvements across three types of F1 scores.
%U https://aclanthology.org/2024.emnlp-main.1181
%P 21204-21218

Markdown (Informal)

[Generalizing Clinical De-identification Models by Privacy-safe Data Augmentation using GPT-4](https://aclanthology.org/2024.emnlp-main.1181) (Kim et al., EMNLP 2024)

• Generalizing Clinical De-identification Models by Privacy-safe Data Augmentation using GPT-4 (Kim et al., EMNLP 2024)

ACL

• Woojin Kim, Sungeun Hahm, and Jaejin Lee. 2024. Generalizing Clinical De-identification Models by Privacy-safe Data Augmentation using GPT-4. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 21204–21218, Miami, Florida, USA. Association for Computational Linguistics.