ORIGINAL RESEARCH article
Front. Neurol.
Sec. Neurotrauma
Volume 16 - 2025 | doi: 10.3389/fneur.2025.1649869
Machine learning models predict coagulopathy in Traumatic Brain Injury patients in ER
Provisionally accepted
- 1The Ninth People's Hospital of Chongqing, Chongqing, China
- 2Affiliated Hospital of Putian University, Putian, China
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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.
Keywords: Traumatic Brain Injury, coagulopathy, machine learning, Emergency Medicine, Featureimportance, SHAP analysis, risk stratification
Received: 19 Jun 2025; Accepted: 02 Sep 2025.
Copyright: © 2025 Wang, Cao, Huang, Feng and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jianhuang Huang, Affiliated Hospital of Putian University, Putian, China
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