Leveraging large language models for the deidentification and temporal normalization of sensitive health information in electronic health records

Leveraging large language models for the deidentification and temporal normalization of sensitive health information in electronic health records

Abstract Secondary use of electronic health record notes enhances clinical outcomes and personalized medicine, but risks sensitive health information (SHI) exposure. Inconsistent time formats hinder interpretation, necessitating deidentification and temporal normalization. The SREDH/AI CUP 2023 comp...

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Main Authors: Hong-Jie Dai, Tatheer Hussain Mir, Ching-Tai Chen, Chien-Chang Chen, Hao-Ping Yang, Chung-Hong Lee, Yi-Yun Chou, Yu-Chin Teng, Shalini Gupta, Omkar Panchal, Divyabharathy Ramesh Nadar, Wei-Hsiang Liao, Yu-Chuan Lin, Zi-Rui Zhao, Richard Tzong-Han Tsai, Yung-Chun Chang, Jitendra Jonnagaddala
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:npj Digital Medicine
Online Access:https://doi.org/10.1038/s41746-025-01921-7
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https://doi.org/10.1038/s41746-025-01921-7

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