## 5 Conclusions  
This study utilized an NLP-based RoBERTa model to classify patients with LOC from ED text records, achieving high accuracy. By applying a weighted loss function, we effectively mitigated the impact of data imbalance, a common issue in medical data. LIME analysis demonstrated the model's ability to capture nuanced language. Our findings suggest that NLP is a robust approach for classifying LOC and other imbalanced medical data. Future work will focus on enhancing contextual understanding through explainable deep learning strategies, further improving model accuracy and transparency in clinical settings.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app142311399/s1](https://www.mdpi.com/article/10.3390/app142311399/s1)]([https://www.mdpi.com/article/10.3390/app142311399/s1](https://www.mdpi.com/article/10.3390/app142311399/s1)), Table S1: Distribution of patients across the entire dataset based on LOC status; Table S2: Training parameters of the model; Table S3. Classification performance according to input data evaluated using 5-fold cross-validation; Figure S1: Evaluation of model performance with cross-validation; Figure S2: Interpretation of LOC classification and the importance of each word feature (original Korean version).  
**Author Contributions**: Conceptualization, J.O.P., C.K., H.A.P. and S.-H.S.; Data curation, J.O.P., H.A.P., J.J. and S.-H.S.; Formal analysis, I.J., S.-H.S. and S.Y.S.; Funding acquisition, J.J.L. and C.K.; Investigation, H.A.P. and J.O.P.; Methodology, H.A.P., J.J., S.-H.S., C.K. and J.O.P.; Software, I.J.; Validation, I.J.; Visualization, S.-H.S.; Writing--original draft, H.A.P., S.-H.S., J.O.P. and C.K.; Writing--review and editing: H.A.P., S.-H.S., I.J., J.O.P., C.K. and J.J.L. All authors have read and agreed to the published version of the manuscript.  
**Funding**: This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HR21C0198, HI22C1498); by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1A5A8019303); and supported by \"Regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2023 RIS-005), and Hallym University Research Fund (HURF).  
**Institutional Review Board Statement**: This study was conducted in accordance with the Declaration of Helsinki and approved by the Hallym University Dongtan Sacred Heart Hospital Institutional Review Board/Ethics Committee (IRB No. 2023-08-013, 26 September 2023).  
**Informed Consent Statement**: Informed consent was waived by the institutional review boards because this study used only de-identified image data which were retrospectively collected.  
**Data Availability Statement**: The original contributions presented in the study are included in the article/Supplementary Materials, further inquiries can be directed to the corresponding authors.  
**Conflicts of Interest**: The authors declare no conflicts of interest.