ArticleScientific reports2022
A pre-trained BERT for Korean medical natural language processing.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers.
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22 citing papers in PubMed, 60 citations in OpenAlex.
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- Evaluating Encoder and Decoder Models for Extended Clinical Concept Recognition in Japanese Clinical Texts: Comparative Study With Weighted Soft Matching.Journal of medical Internet research · 2026Article
- Korean Medical Consultation With Open-Weight Large Language Models: Pilot Comparative Evaluation of Retrieval-Augmented Generation With Metadata Filtering.JMIR formative research · 2026Article
- Improving Retrieval Augmented Generation for Health Care by Fine-Tuning Clinical Embedding Models: Development and Evaluation Study.Journal of medical Internet research · 2026Article
- Domain and Language adaptive pre-training of BERT models for Korean-English bilingual clinical text analysis.BMC medical informatics and decision making · 2025Article
- Protecting medical privacy with pretrained language models: named entity recognition-based de-identification in Korean unstructured electronic medical records.npj health systems · 2025Article
- Beyond digital twins: the role of foundation models in enhancing the interpretability of multiomics modalities in precision medicine.FEBS open bio · 2025Review
- Machine learning for automated cause-of-death classification from 2021 to 2022 in Korea: development and validation of an ICD-10 prediction model.Ewha medical journal · 2025Article
- Natural language processing of electronic medical records identifies cardioprotective agents for anthracycline induced cardiotoxicity.Scientific reports · 2025Observational
- A pediatric emergency prediction model using natural language process in the pediatric emergency department.Scientific reports · 2025Article
- Entity-enhanced BERT for medical specialty prediction based on clinical questionnaire data.PloS one · 2025Article
- Performance of GPT-3.5 and GPT-4 on the Korean Pharmacist Licensing Examination: Comparison Study.JMIR medical education · 2024Article
- Post-marketing surveillance of anticancer drugs using natural language processing of electronic medical records.NPJ digital medicine · 2024Article
- Fine-Tuned Bidirectional Encoder Representations From Transformers Versus ChatGPT for Text-Based Outpatient Department Recommendation: Comparative Study.JMIR formative research · 2024Article
- Transformer models in biomedicine.BMC medical informatics and decision making · 2024Review
- Assessing GPT-4's Performance in Delivering Medical Advice: Comparative Analysis With Human Experts.JMIR medical education · 2024Article
- Comparison of an Ensemble of Machine Learning Models and the BERT Language Model for Analysis of Text Descriptions of Brain CT Reports to Determine the Presence of Intracranial Hemorrhage.Sovremennye tekhnologii v meditsine · 2024Article
- Correcting spelling mistakes in Persian texts with rules and deep learning methods.Scientific reports · 2023Article
- Year 2022 in Medical Natural Language Processing: Availability of Language Models as a Step in the Democratization of NLP in the Biomedical Area.Yearbook of medical informatics · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With advances in deep learning and natural language processing (NLP), the analysis of medical texts is becoming increasingly important. Nonetheless, despite the importance of processing medical texts, no research on Korean medical-specific language models has been conducted. The Korean medical text is highly difficult to analyze because of the agglutinative characteristics of the language, as well as the complex terminologies in the medical domain. To solve this problem, we collected a Korean medical corpus and used it to train the language models. In this paper, we present a Korean medical language model based on deep learning NLP. The model was trained using the pre-training framework of BERT for the medical context based on a state-of-the-art Korean language model. The pre-trained model showed increased accuracies of 0.147 and 0.148 for the masked language model with next sentence prediction. In the intrinsic evaluation, the next sentence prediction accuracy improved by 0.258, which is a remarkable enhancement. In addition, the extrinsic evaluation of Korean medical semantic textual similarity data showed a 0.046 increase in the Pearson correlation, and the evaluation for the Korean medical named entity recognition showed a 0.053 increase in the F1-score.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.