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

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…

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