ArticleScientific reports2024
Machine learning models for predicting early hemorrhage progression in traumatic brain injury.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 3 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Can artificial intelligence accurately predict the risk of hematoma expansion in intracerebral hemorrhage? A systematic review and Meta-analysis of 7,665 patients.Neurosurgical review · 2025Pooled it
- Can machine learning be a reliable tool for predicting hematoma progression following traumatic brain injury? A systematic review and meta-analysis.Neuroradiology · 2025Pooled it
- Risk factors for traumatic intracranial hemorrhage in mild traumatic brain injury patients at the emergency department: a systematic review and meta-analysis.Scandinavian journal of trauma, resuscitation and emergency medicine · 2024Pooled it
- Explainable machine learning of PROGRESS-Plus social factors predicts cognitive trajectories after traumatic brain injury.Scientific reports · 2026Article
- Machine learning-based identification of abnormal functional connectivity in obesity across different metabolic states.Communications medicine · 2026Article
- Early hematoma expansion provides incremental prognostic information beyond admission characteristics for 6-month functional outcome after adult traumatic brain injury.Frontiers in neurology · 2026Article
- An interpretable multimodal model for early prediction of delayed hematoma progression in frontal lobe contusion: a machine learning approach.Frontiers in neuroscience · 2026Article
- Development and validation of a prognostic prediction model for patients with traumatic multiple fractures and hemorrhagic shock using an Automated Machine Learning framework: a retrospective cohort study.Frontiers in medicine · 2026Article
- Artificial intelligence in traumatic brain injury: Brain imaging analysis and outcome prediction: A mini review.World journal of critical care medicine · 2025Review
- Machine Learning Approaches to Prognostication in Traumatic Brain Injury.Current neurology and neuroscience reports · 2025Review
- Machine Learning Models for Predicting Abnormal Brain CT Scan Findings in Mild Traumatic Brain Injury Patients.Archives of academic emergency medicine · 2025Article
- Early mortality prediction after severe trauma using ensemble machine learning: a single-center retrospective study.Frontiers in public health · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study explores the progression of intracerebral hemorrhage (ICH) in patients with mild to moderate traumatic brain injury (TBI). It aims to predict the risk of ICH progression using initial CT scans and identify clinical factors associated with this progression. A retrospective analysis of TBI patients between January 2010 and December 2021 was performed, focusing on initial CT evaluations and demographic, comorbid, and medical history data. ICH was categorized into intraparenchymal hemorrhage (IPH), petechial hemorrhage (PH), and subarachnoid hemorrhage (SAH). Within our study cohort, we identified a 22.2% progression rate of ICH among 650 TBI patients. The Random Forest algorithm identified variables such as petechial hemorrhage (PH) and countercoup injury as significant predictors of ICH progression. The XGBoost algorithm, incorporating key variables identified through SHAP values, demonstrated robust performance, achieving an AUC of 0.9. Additionally, an individual risk assessment diagram, utilizing significant SHAP values, visually represented the impact of each variable on the risk of ICH progression, providing personalized risk profiles. This approach, highlighted by an AUC of 0.913, underscores the model's precision in predicting ICH progression, marking a significant step towards enhancing TBI patient management through early identification of ICH progression risks.
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