ArticleScientific reports2023
Prognosis prediction in traumatic brain injury patients using machine learning algorithms.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.
- Effects of non-invasive brain stimulation on arousal and alertness among traumatic brain injury patients with disorders of consciousness or persistent vegetative State: a systematic review.Acta neurologica Belgica · 2025Pooled it
- Nanozyme-based therapeutic strategies for traumatic brain injury.International journal of pharmaceutics: X · 2026Review
- Mechanisms of inflammatory response in secondary brain injury: a review of computational approaches, challenges, and future directions.Biomechanics and modeling in mechanobiology · 2026Review
- Predicting Traumatic Brain Injury Post-Trauma Using Temporal Attention on Sleep-Wake Data.IEEE transactions on bio-medical engineering · 2026Article
- Soft-voting classifier for outcome prediction in traumatic brain injury patients with clear consciousness: model development and error pattern analysis.Neurosurgical review · 2025Article
- A Hybrid Bidirectional Deep Learning Model Using HRV for Prediction of ICU Mortality Risk in TBI Patients.Journal of healthcare informatics research · 2025Article
- E-TBI: explainable outcome prediction after traumatic brain injury using machine learning.Medical & biological engineering & computing · 2025Article
- Interpretable Multiomics Models for Predicting Surgical Interventions and Blood Transfusion Requirements in Traumatic Brain Injury.NPJ digital medicine · 2025Article
- Clinical Assessment on Days 1-14 for the Characterization of Traumatic Brain Injury: Recommendations from the 2024 NINDS Traumatic Brain Injury Classification and Nomenclature Initiative Clinical/Symptoms Working Group.Journal of neurotrauma · 2025Review
- Traumatic Brain Injury: Novel Experimental Approaches and Treatment Possibilities.Life (Basel, Switzerland) · 2025Review
- Traumatic Brain Injury and Artificial Intelligence: Shaping the Future of Neurorehabilitation-A Review.Life (Basel, Switzerland) · 2025Review
- Development and Validation of a Novel Classification System and Prognostic Model for Open Traumatic Brain Injury: A Multicenter Retrospective Study.Neurology and therapy · 2025Article
- Extracellular cold-inducible RNA-binding protein in CNS injury: molecular insights and therapeutic approaches.Journal of neuroinflammation · 2025Review
- Prediction tool for discharge disposition and 30-day readmission using electronic health records among patients hospitalized for traumatic brain injury.Frontiers in neurology · 2025Article
- Development and validation of a nomogram-based risk prediction model for unfavorable outcomes in pediatric traumatic brain injury: a retrospective study.Frontiers in pediatrics · 2025Article
- Prediction of prognosis in patients with cerebral contusions based on machine learning.Scientific reports · 2024Article
- Exploration of simultaneous transients between cerebral hemodynamics and the autonomic nervous system using windowed time-lagged cross-correlation matrices: a CENTER-TBI study.Acta neurochirurgica · 2024Article
- Article
- Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array.Nano-micro letters · 2024Review
- Machine learning-based prediction of clinical outcomes after traumatic brain injury: Hidden information of early physiological time series.CNS neuroscience & therapeutics · 2024Article
Corrections and comments
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Authors and funding
14 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting treatment outcomes in traumatic brain injury (TBI) patients is challenging worldwide. The present study aimed to achieve the most accurate machine learning (ML) algorithms to predict the outcomes of TBI treatment by evaluating demographic features, laboratory data, imaging indices, and clinical features. We used data from 3347 patients admitted to a tertiary trauma centre in Iran from 2016 to 2021. After the exclusion of incomplete data, 1653 patients remained. We used ML algorithms such as random forest (RF) and decision tree (DT) with ten-fold cross-validation to develop the best prediction model. Our findings reveal that among different variables included in this study, the motor component of the Glasgow coma scale, the condition of pupils, and the condition of cisterns were the most reliable features for predicting in-hospital mortality, while the patients' age takes the place of cisterns condition when considering the long-term survival of TBI patients. Also, we found that the RF algorithm is the best model to predict the short-term mortality of TBI patients. However, the generalized linear model (GLM) algorithm showed the best performance (with an accuracy rate of 82.03 ± 2.34) in predicting the long-term survival of patients. Our results showed that using appropriate markers and with further development, ML has the potential to predict TBI patients' survival in the short- and long-term.
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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.