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Harnessing Moderate-Sized Language Models for Reliable Patient Data Deidentification in Emergency Department Records: Algorithm Development, Validation, and Implementation Study.

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Title: Harnessing Moderate-Sized Language Models for Reliable Patient Data Deidentification in Emergency Department Records: Algorithm Development, Validation, and Implementation Study.
Authors: Dorémus, Océane1 (AUTHOR) oceane.doremus@u-bordeaux.fr, Russon, Dylan1 (AUTHOR), Contrand, Benjamin1 (AUTHOR), Guerra-Adames, Ariel1,2 (AUTHOR), Avalos-Fernandez, Marta2 (AUTHOR), Gil-Jardiné, Cédric1,3 (AUTHOR), Lagarde, Emmanuel1 (AUTHOR)
Source: JMIR AI. 2025, Vol. 4, p1-16. 16p.
Database: Academic Search Ultimate
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  Data: Harnessing Moderate-Sized Language Models for Reliable Patient Data Deidentification in Emergency Department Records: Algorithm Development, Validation, and Implementation Study.
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  Data: <searchLink fieldCode="JN" term="%22JMIR+AI%22">JMIR AI</searchLink>. 2025, Vol. 4, p1-16. 16p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=191867831
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        Value: 10.2196/57828
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      – Code: eng
        Text: English
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        PageCount: 16
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      – TitleFull: Harnessing Moderate-Sized Language Models for Reliable Patient Data Deidentification in Emergency Department Records: Algorithm Development, Validation, and Implementation Study.
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            NameFull: Dorémus, Océane
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            NameFull: Avalos-Fernandez, Marta
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            NameFull: Gil-Jardiné, Cédric
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            – D: 01
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              Text: 2025
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              Y: 2025
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