ArticleFrontiers in neurology2025
Machine learning models predict coagulopathy in traumatic brain injury patients in ER.
Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Machine learning-based triage model for elderly traumatic brain injury patients in Chinese emergency department.Frontiers in neurologyArticle
- Screening of biomarkers and machine learning prediction in traumatic brain injury.Frontiers in neurologyArticle
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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