SynthesisFrontiers in neuroscience2023
Machine learning-based model for predicting inpatient mortality in adults with traumatic brain injury: a systematic review and meta-analysis.
Synthesis in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- 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
- Development and validation of a risk stratification tool for 28-day mortality based on D-dimer to platelet ratio in moderate-to -severe traumatic brain injury: a retrospective cohort study.Frontiers in neuroscience · 2026Article
- A novel CT-based edema grading system combined with machine learning for precise prognostic prediction in traumatic brain injury.European journal of medical research · 2025Article
- Revolutionizing cross professional collaboration outcomes in TBI: emerging trends in diagnostics, personalized medicine, technological innovations and neurorehabilitation.Brain informatics · 2025Review
- Predictive Modeling of Long-Term Care Needs in Traumatic Brain Injury Patients Using Machine Learning.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
Background and objective: Predicting mortality from traumatic brain injury facilitates early data-driven treatment decisions. Machine learning has predicted mortality from traumatic brain injury in a growing number of studies, and the aim of this study was to conduct a meta-analysis of machine learning models in predicting mortality from traumatic brain injury. Methods: This systematic review and meta-analysis included searches of PubMed, Web of Science and Embase from inception to June 2023, supplemented by manual searches of study references and review articles. Data were analyzed using Stata 16.0 software. This study is registered with PROSPERO (CRD2023440875). Results: A total of 14 studies were included. The studies showed significant differences in the overall sample, model type and model validation. Predictive models performed well with a pooled AUC of 0.90 (95% CI: 0.87 to 0.92). Conclusion: Overall, this study highlights the excellent predictive capabilities of machine learning models in determining mortality following traumatic brain injury. However, it is important to note that the optimal machine learning modeling approach has not yet been identified. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=440875, identifier CRD2023440875.
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