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A pre-trained BERT for Korean medical natural language processing.

Yoojoong Kim¹, Jong-Ho Kim^2,3, Jeong Moon Lee²

¹School of Computer Science and Information Engineering, The Catholic University of Korea, Bucheon, Republic of Korea.

Scientific Reports

|August 16, 2022

Summary

This study introduces a novel Korean medical language model using deep learning natural language processing (NLP). The model significantly enhances accuracy in analyzing complex Korean medical texts, improving semantic similarity and named entity recognition.

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Area of Science:

Natural Language Processing
Computational Linguistics
Medical Informatics

Background:

Deep learning and NLP are crucial for medical text analysis.
Korean medical texts present unique challenges due to language structure and complex terminology.
No prior research exists on Korean medical-specific language models.

Purpose of the Study:

To develop and evaluate a Korean medical language model using deep learning NLP.
To address the difficulties in analyzing Korean medical texts.
To improve the performance of medical text analysis tasks in Korean.

Main Methods:

Collected a Korean medical corpus for training.
Utilized BERT's pre-training framework for medical context.
Built upon a state-of-the-art Korean language model.
Performed intrinsic and extrinsic evaluations.

Main Results:

Achieved increased accuracies of 0.147 and 0.148 for masked language model and next sentence prediction.
Demonstrated a 0.258 improvement in next sentence prediction accuracy.
Showed a 0.046 increase in Pearson correlation for semantic textual similarity.
Achieved a 0.053 increase in F1-score for named entity recognition.

Conclusions:

The developed Korean medical language model shows significant performance improvements.
The model effectively handles the complexities of Korean medical language.
This research provides a foundation for advanced Korean medical NLP applications.