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

Dorémus O, Russon D, Contrand B, Guerra-Adames A, Avalos-Fernandez M, Gil-Jardiné C, Lagarde E

Open source

DOI
10.2196/57828
Published
2025 Apr 1
Container
JMIR AI
Publisher
Not recorded
Open access
yes

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BibTeX

@article{allodium:10.2196/57828,
  title = {Harnessing Moderate-Sized Language Models for Reliable Patient Data Deidentification in Emergency Department Records: Algorithm Development, Validation, and Implementation Study.},
  author = {Dorémus O and Russon D and Contrand B and Guerra-Adames A and Avalos-Fernandez M and Gil-Jardiné C and Lagarde E},
  year = {2025},
  journal = {JMIR AI},
  doi = {10.2196/57828},
  url = {https://doi.org/10.2196/57828}
}

RIS

TY  - JOUR
TI  - Harnessing Moderate-Sized Language Models for Reliable Patient Data Deidentification in Emergency Department Records: Algorithm Development, Validation, and Implementation Study.
AU  - Dorémus O
AU  - Russon D
AU  - Contrand B
AU  - Guerra-Adames A
AU  - Avalos-Fernandez M
AU  - Gil-Jardiné C
AU  - Lagarde E
PY  - 2025
JO  - JMIR AI
DO  - 10.2196/57828
UR  - https://doi.org/10.2196/57828
ER  -

APA

O, D., D, R., B, C., A, G., M, A., C, G., & E, L. (2025). Harnessing Moderate-Sized Language Models for Reliable Patient Data Deidentification in Emergency Department Records: Algorithm Development, Validation, and Implementation Study.. JMIR AI. https://doi.org/10.2196/57828

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