## 4 Discussion
### Principal Results
This suggests that pre-diagnosis data alone can effectively support the rapid identification of LOC information, especially in time-sensitive scenarios where immediate clinical decisions are critical. In addition, utilizing pre-diagnosis data exclusively can offer a practical advantage by reducing data processing requirements and computational complexity. NLP applications provide significant advantages in healthcare by automating the processing of vast amounts of clinical text. While rule-based algorithms were previously the most common, recent trends reveal a marked increase in studies using NLP to retrieve and extract medical concepts and terminology [25]. This shift reflects the growing reliance on NLP in healthcare as EMRs and other health information systems expand, resulting in increased volumes of textual data that necessitate advanced methods for efficient data retrieval. NLP addresses this need by reducing or eliminating the need for manual narrative reviews, enabling the quick and accurate assessment of large datasets. Additionally, NLP is instrumental in extracting critical factors from EMRs. By applying these techniques to LOC identification, this model fills critical gaps in EMR documentation that ED physicians may overlook. A systematic review has also demonstrated NLP's effectiveness in identifying social determinants of health from EMRs, factors such as smoking status, substance use, and homelessness, which are essential for comprehensive patient assessments and health risk evaluations [26]. NLP models offer substantial benefits for healthcare providers, patients, and caregivers. In healthcare AI applications, NLP is primarily used for information extraction,either as part of a larger input set or to generate training target values for downstream AI models [27]. By integrating LOC information from patients' or caregivers' narrative accounts and other key characteristics related to traumatic brain injury, this model enhances clinical decision support systems. Such systems can provide recommendations for ED visits after a TBI, enabling timely intervention for patients at risk of post-concussion complications. Additionally, the model can track patient recovery by analyzing follow-up records, aiding in the monitoring of symptom resolution or progression over time, which supports continuous care and informed clinical management. In this study, we used a bilingual (English and Korean) dataset in which all text was translated into English prior to the classification task. This approach was chosen to leverage pre-trained language models like RoBERTa, which are optimized for English text processing. Our model effectively captures contextual information, achieving a high AUROC score of 0.91. However, it is important to consider the potential impact of using monolingual datasets directly, particularly in scenarios where translation may result in a loss of nuanced clinical details specific to the source language. Therefore, the integration of multilingual and monolingual approaches presents a promising avenue for future research. Comparing performance metrics across translation-based and native language models will help determine the optimal approach for specific clinical contexts.