ArticleFrontiers in public health2025
Guardians of the data: NER and LLMs for effective medical record anonymization in Brazilian Portuguese.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: The anonymization of medical records is essential to protect patient privacy while enabling the use of clinical data for research and Natural Language Processing (NLP) applications. However, for Brazilian Portuguese, the lack of publicly available and high-quality anonymized datasets limits progress in this area. Methods: In this study, we present Results: All evaluated models achieved good performance in the anonymization task, with the best models reaching F1 scores above 0.90. Both extractive and generative approaches were effective in identifying and masking sensitive entities while preserving the clinical meaning of the texts. Experiments also revealed that including synthetic data improved model generalization, and that task-specific fine-tuning yielded greater performance gains than pre-training the model on biomedical domain. Discussion and conclusion: To the best of our knowledge,
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