Traumatic brain injury (TBI) remains a critical public health challenge. Although studies have found several prognostic factors for TBI, a useful early predictive tool for mortality has yet to be developed in the triage of the emergency room. This study aimed to use machine learning algorithms of artificial intelligence (AI) to develop predictive models for TBI patients in the emergency room triage. We retrospectively enrolled 18,249 adult TBI patients in the electronic medical records of three hospitals of Chi Mei Medical Group from January 2010 to December 2019, and undertook the 12 potentially predictive feature variables for predicting mortality during hospitalization. Six machine learning algorithms including logistical regression (LR) random forest (RF), support vector machines (SVM), LightGBM, XGBoost, and multilayer perceptron (MLP) were used to build the predictive model. The results showed that all six predictive models had high AUC from 0.851 to 0.925. Among these models, the LR-based model was the best model for mortality risk prediction with the highest AUC of 0.925; thus, we integrated the best model into the existed hospital information system for assisting clinical decision-making. These results revealed that the LR-based model was the best model to predict the mortality risk in patients with TBI in the emergency room. Since the developed prediction system can easily obtain the 12 feature variables during the initial triage, it can provide quick and early mortality prediction to clinicians for guiding deciding further treatment as well as helping explain the patient’s condition to family members.

Background: Numerous mortality prediction tools are currently available to assist patients with moderate to severe traumatic brain injury (TBI). However, an algorithm that utilizes various machine learning methods and employs diverse combinations of features to identify the most suitable predicting outcomes of brain injury patients in the intensive care unit (ICU) has not yet been well-established. Method: Between January 2016 and December 2021, we retrospectively collected data from the electronic medical records of Chi Mei Medical Center, comprising 2260 TBI patients admitted to the ICU. A total of 42 features were incorporated into the analysis using four different machine learning models, which were then segmented into various feature combinations. The predictive performance was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and validated using the Delong test. Result: The AUC for each model under different feature combinations ranged from 0.877 (logistic regression with 14 features) to 0.921 (random forest with 22 features). The Delong test indicated that the predictive performance of the machine learning models is better than that of traditional tools such as APACHE II and SOFA scores. Conclusion: Our machine learning training demonstrated that the predictive accuracy of the LightGBM is better than that of APACHE II and SOFA scores. These features are readily available on the first day of patient admission to the ICU. By integrating this model into the clinical platform, we can offer clinicians an immediate prognosis for the patient, thereby establishing a bridge for educating and communicating with family members.

Background: Traumatic brain injury (TBI) research often focuses on mortality rates or functional recovery, yet the critical need for long-term care among patients dependent on institutional or Respiratory Care Ward (RCW) support remains underexplored. This study aims to address this gap by employing machine learning techniques to develop and validate predictive models that analyze the prognosis of this patient population. Method: Retrospective data from electronic medical records at Chi Mei Medical Center, encompassing 2020 TBI patients admitted to the ICU between January 2016 and December 2021, were collected. A total of 44 features were included, utilizing four machine learning models and various feature combinations based on clinical significance and Spearman correlation coefficients. Predictive performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and validated with the DeLong test and SHAP (SHapley Additive exPlanations) analysis. Result: Notably, 236 patients (11.68%) were transferred to long-term care centers. XGBoost with 27 features achieved the highest AUC (0.823), followed by Random Forest with 11 features (0.817), and LightGBM with 44 features (0.813). The DeLong test revealed no significant differences among the best predictive models under various feature combinations. SHAP analysis illustrated a similar distribution of feature importance for the top 11 features in XGBoost, with 27 features, and Random Forest with 11 features. Conclusions: Random Forest, with an 11-feature combination, provided clinically meaningful predictive capability, offering early insights into long-term care trends for TBI patients. This model supports proactive planning for institutional or RCW resources, addressing a critical yet often overlooked aspect of TBI care.

Artificial intelligence (AI) has become increasingly important in predicting outcomes of traumatic brain injury (TBI). Traditional machine learning (ML) models, such as support vector machines (SVMs), have shown high accuracy, whereas the potential of large language models (LLMs) in structured clinical prediction remains underexplored. This study compared the predictive performance of ML and LLM approaches (ChatGPT-5 with Thinking response mode) using the same TBI dataset and evaluated the impact of prompting strategies and threshold calibration on model reliability and clinical applicability. A dataset of 5,475 TBI cases with 12 clinical features was used to build an SVM model and four LLM strategies: zero-shot GPT, few-shot GPT, few-shot + chain-of-thought (CoT) GPT, and CoT-only GPT. Performance was evaluated by accuracy, sensitivity, specificity, and ROC-AUC under fixed and balanced thresholds. The SVM model achieved the best overall performance (AUC = 0.920). After threshold adjustment, all LLMs reached comparable AUCs (0.902-0.919). Few-shot GPT most closely matched SVM, CoT + few-shot achieved highest sensitivity, and CoT-only favored specificity. Proper threshold calibration enables LLMs to approximate ML accuracy while offering rapid deployment and interpretability. Prompt engineering combined with adaptive cut-off tuning may enhance clinical usability of LLM-based prediction systems.

Traumatic brain injury (TBI) is increasingly recognized as a systemic disorder associated with long-term neuropsychiatric sequelae. However, substantial inter-individual variability suggests that pre-injury metabolic status may influence neurobehavioral vulnerability following TBI. We investigated whether pre-injury hypercholesterolemia is associated with altered microbiota-associated neurobehavioral interactions after TBI. Male Sprague-Dawley rats were fed a high-fat diet prior to moderate fluid percussion injury, followed by behavioral, biochemical, histological, and microbiota analyses on day 15 post-injury. Pre-injury hypercholesterolemia was associated with increased depression-like behavior, reduced peripheral serotonin levels, greater TTC-negative injury burden, and increased TUNEL positivity. These changes co-occurred with reduced microbial diversity, depletion of Prevotella spp., and enrichment of Akkermansia muciniphila. Predicted microbial functional annotations suggested alterations in amino acid and tryptophan-related pathways associated with behavioral and biochemical measures. Correlation analyses demonstrated inverse associations between peripheral serotonin levels and depression-like behavior, while Akkermansia muciniphila abundance was negatively correlated with peripheral serotonin levels. Network analyses further revealed coordinated associations among microbial taxa, peripheral serotonergic-related markers, and behavioral outcomes. Together, these findings support coordinated microbiota-associated neurobehavioral interactions following traumatic brain injury.