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

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

The relevance of precision is altered in this context, as it necessitates a different consideration of what constitutes a pseudonymization attempt, denoted by the presence of a pseudonymization tag. Instead, the potential alteration of content possibly induced by the deidentification process was measured using the BLEU score [33]. where BP is the brevity penalty, wn the weight for each n-gram, and pn the precision of n-grams.

Océane Dorémus, Dylan Russon, Benjamin Contrand, Ariel Guerra-Adames, Marta Avalos-Fernandez, Cédric Gil-Jardiné, Emmanuel Lagarde

JMIR AI 2025;4:e57828

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