ReviewSurgical neurology international2023
Machine learning algorithms for predicting outcomes of traumatic brain injury: A systematic review and meta-analysis.
Review in Surgical neurology international, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The role of artificial intelligence in the diagnosis and prognosis of traumatic brain injury based on brain CT scans: a systematic review.Neurosurgical review · 2026Pooled it
- Applications of machine learning in pediatric traumatic brain injury (pTBI): a systematic review of the literature.Neurosurgical review · 2024Pooled it
- A Meta-analysis of Predicting Disorders of Consciousness After Traumatic Brain Injury by Machine Learning Models.Alpha psychiatry · 2024Pooled it
- Progesterone for Traumatic Brain Injury, Experimental Clinical Treatment III Trial Revisited: Objective Classification of Traumatic Brain Injury With Brain Imaging Segmentation and Biomarker Levels.Critical care explorations · 2025Trial
- Predictive Models of Long-Term Outcome in Patients with Moderate to Severe Traumatic Brain Injury are Biased Toward Mortality Prediction.Neurocritical care · 2025Trial
- Predicting Progression of Intracranial Hemorrhage in the Prehospital TXA for TBI Trial.Journal of neurotrauma · 2024Trial
- Evaluation of ChatGPT-5 for CT Imaging in Canadian CT Head Rule-Positive Mild Traumatic Brain Injury: A Pilot Study.Biomedicines · 2026Article
- Predicting Prolonged Hospital Length of Stay in Trauma Patients Using Machine Learning Techniques: A Cross-Sectional Study.Health science reports · 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
- A scoping review on the essential role of global neurotrauma in achieving the sustainable development goals.Discover public health · 2026Review
- Prediction of Clinically Significant Improvements During the Interdisciplinary Intensive Outpatient Program for Traumatic Brain Injury Using Machine Learning.Annals of biomedical engineering · 2025Article
- Traumatic Brain Injury: Advances in Diagnostic Techniques and Treatment Modalities.Journal of clinical medicine · 2025Review
- Artificial intelligence in traumatic brain injury: Brain imaging analysis and outcome prediction: A mini review.World journal of critical care medicine · 2025Review
- Mortality During In-Hospital Stay and the First 24 h After Decompressive Craniectomy in Severe Traumatic Brain Injury: A Multi-Center, Retrospective Propensity Score-Matched Study.Journal of clinical medicine · 2025Article
- Traumatic Brain Injury and Artificial Intelligence: Shaping the Future of Neurorehabilitation-A Review.Life (Basel, Switzerland) · 2025Review
- Developing practical machine learning survival models to identify high-risk patients for in-hospital mortality following traumatic brain injury.Scientific reports · 2025Article
- Advancements in the application of multimodal monitoring and machine learning for the development of personalized therapeutic strategies in traumatic brain injury.Frontiers in human neuroscience · 2025Review
- Machine Learning Models for Predicting Abnormal Brain CT Scan Findings in Mild Traumatic Brain Injury Patients.Archives of academic emergency medicine · 2025Article
- Predictive Modeling of Long-Term Care Needs in Traumatic Brain Injury Patients Using Machine Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Resuscitation and Initial Management After Moderate-to-Severe Traumatic Brain Injury: Questions for the On-Call Shift.Journal of clinical medicine · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Traumatic brain injury (TBI) is a leading cause of death and disability worldwide. The use of machine learning (ML) has emerged as a key advancement in TBI management. This study aimed to identify ML models with demonstrated effectiveness in predicting TBI outcomes. Methods: We conducted a systematic review in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis statement. In total, 15 articles were identified using the search strategy. Patient demographics, clinical status, ML outcome variables, and predictive characteristics were extracted. A small meta-analysis of mortality prediction was performed, and a meta-analysis of diagnostic accuracy was conducted for ML algorithms used across multiple studies. Results: ML algorithms including support vector machine (SVM), artificial neural networks (ANN), random forest, and Naïve Bayes were compared to logistic regression (LR). Thirteen studies found significant improvement in prognostic capability using ML versus LR. The accuracy of the above algorithms was consistently over 80% when predicting mortality and unfavorable outcome measured by Glasgow Outcome Scale. Receiver operating characteristic curves analyzing the sensitivity of ANN, SVM, decision tree, and LR demonstrated consistent findings across studies. Lower admission Glasgow Coma Scale (GCS), older age, elevated serum acid, and abnormal glucose were associated with increased adverse outcomes and had the most significant impact on ML algorithms. Conclusion: ML algorithms were stronger than traditional regression models in predicting adverse outcomes. Admission GCS, age, and serum metabolites all have strong predictive power when used with ML and should be considered important components of TBI risk stratification.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.