A De-Identification Model for Korean Clinical Notes: Using Deep Learning Models.
| Autor: | Chang J; Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Korea., Park J; Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Korea., Kim C; Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Korea., Park RW; Department of Biomedical Informatics, Ajou University School of Medicine, Korea. |
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| Jazyk: | angličtina |
| Zdroj: | Studies in health technology and informatics [Stud Health Technol Inform] 2024 Jan 25; Vol. 310, pp. 1456-1457. |
| DOI: | 10.3233/SHTI231242 |
| Abstrakt: | 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. |
| Databáze: | MEDLINE |
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