Machine learning models predict coagulopathy in traumatic brain injury patients in ER.

Traumatic brain injury (TBI) is a critical emergency condition, with 15-35% of patients developing coagulopathy, increasing risks of secondary brain injury and mortality. We developed a machine learning model to predict coagulopathy in TBI patients in the emergency room. Using data from 322 TBI patients (mean age 55.7 ± 21.1 years, coagulopathy incidence 15.8%) at Chongqing Ninth People's Hospital (2018-2024), we collected clinical and laboratory data (GCS scores, blood counts, liver function). Data were preprocessed in R, using SMOTE for class imbalance and selecting top 70% features by information gain. Among 11 algorithms, Random Forest (RF) achieved the best performance (AUC = 0.92, recall = 0.94, false negative rate = 6%), outperforming coagulation tests. Neutrophil percentage, A/G ratio, and ALT were key predictors, reflecting inflammation and liver dysfunction. SHAP analysis enhanced model interpretability. This model supports rapid risk stratification for early intervention, though multi-center validation is needed.