A De-Identification Model for Korean Clinical Notes: Using Deep Learning Models.

Journal: Studies in health technology and informatics
PMID: 38269694

Abstract

To extract information from free-text in clinical records due to the patient's protected health information PHI in the records pre-processing of de-identification is required. Therefore we aimed to identify PHI list and fine-tune the deep learning BERT model for developing de-identification model. The result of fine-tuning the model is strict F1 score of 0.924. Due to the convinced score the model can be used for the development of a de-identification model.

Authors

  • Junhyuk Chang
    Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Korea.
  • Jimyung Park
    Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Korea.
  • Chungsoo Kim
    Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Gyeonggi-do, Republic of Korea.
  • Rae Woong Park
    Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Gyeonggi-do, Republic of Korea.

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