###### Abstract  
The increasing adoption of electronic medical records (EMRs) presents a unique opportunity to enhance trauma care through data-driven insights. However, extracting meaningful and actionable information from unstructured clinical text remains a significant challenge. Addressing this gap, this study focuses on the application of natural language processing (NLP) techniques to extract injury-related variables and classify trauma patients based on the presence of loss of consciousness (LOC). A dataset of 23,308 trauma patient EMRs, including pre-diagnosis and post-diagnosis free-text notes, was analyzed using a bilingual (English and Korean) pre-trained RoBERTa model. The patients were categorized into four groups based on the presence of LOC and head trauma. To address class imbalance in LOC labeling, deep learning models were trained with weighted loss functions, achieving a high area under the curve (AUC) of 0.91. Local Interpretable Model-agnostic Explanations analysis further demonstrated the model's ability to identify critical terms related to head injuries and consciousness. NLP can effectively identify LOC in trauma patients' EMRs, with weighted loss functions addressing data imbalances. These findings can inform the development of AI tools to improve trauma care and decision-making.  
natural language processing; text mining; deep learning; emergency departments; clinical decision support +
Footnote †: journal: Applied Sciences  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000-analysis of specific clinical scenarios. For example, NLP has been used to identify vulnerable populations, such as homeless youth using psychiatric emergency services, by extracting social determinants from narrative data combined with demographic information [8]. Additionally, NLP can enhance the accuracy of predictive models by analyzing discharge summaries to detect risk factors for psychiatric readmission [8].  
Detecting loss of consciousness (LOC) in EMRs is particularly important for assessing traumatic brain injury (TBI) severity, as LOC is a critical criterion in TBI diagnosis and classification. LOC information guides clinical decisions, such as the need for computed tomography in blunt head trauma cases [9; 10], and is a key data element for TBI data collection [11]. Automating the extraction of LOC from EMRs streamlines the data collection process, reducing reliance on manual chart reviews. NLP-based EMR analytics can identify injury patterns and severity, offering insights beyond what structured data alone can provide. However, the lack of real-world datasets for multilingual text processing like EMRs [12], the computational power to handle complex content, and the lack of research on whether multilingual clinical NLP has helped with healthcare decisions has made it difficult to conduct multilingual NLP studies using EMRs [13].  
This study aims to develop NLP algorithms for the automatic identification of injury-related variables within free-text medical records from emergency department (ED) settings. Specifically, the goal is to classify patients into four categories based on the presence or absence of head trauma and loss of consciousness (LOC). This classification not only identifies cases with LOC but also distinguishes them from other head trauma-related conditions, including cases with no LOC or missing LOC information. By providing a granular categorization of injury-related variables, this study supports clinical decision-making and contributes to more targeted trauma care. In addition, other studies explore the feasibility of developing a multilingual NLP model (English and Korean) capable of processing and interpreting EMR data across diverse clinical contexts.