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

Junhyuk Chang¹, Jimyung Park¹, Chungsoo Kim¹

¹Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Korea.

Studies in Health Technology and Informatics

|January 25, 2024

Summary

This summary is machine-generated.

This study developed a de-identification model using a fine-tuned BERT deep learning approach to protect patient health information (PHI) in clinical records. The model achieved a high F1 score, demonstrating its effectiveness for secure data extraction.

Keywords:

Electronic health record natural language processing

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Area of Science:

Clinical Informatics
Natural Language Processing
Machine Learning

Background:

Extracting information from clinical records requires de-identification to protect patient health information (PHI).
Existing methods may not be sufficient for accurate PHI removal in free-text clinical notes.

Purpose of the Study:

To develop and evaluate a deep learning model for de-identifying clinical records.
To create a comprehensive list of protected health information (PHI) entities for model training.

Main Methods:

Fine-tuning the BERT deep learning model.
Utilizing a curated list of protected health information (PHI) entities.
Implementing a robust de-identification pre-processing pipeline.

Main Results:

The fine-tuned BERT model achieved a strict F1 score of 0.924.
The model demonstrated high accuracy in identifying and removing PHI from clinical text.

Conclusions:

The developed BERT-based de-identification model is effective and suitable for clinical data.
This approach facilitates secure information extraction from sensitive patient records.