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

Abstract

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.