ArticleNeurotrauma reports2020
XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury.
Article in Neurotrauma reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
60 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning-Based Prognostic Models for Functional Outcomes in Spinal Cord Injury: Systematic Review.JMIR medical informatics · 2026Pooled it
- The application of artificial intelligence in the acute and sub-acute phases of spinal cord injury- a systematic review.Spinal cord · 2026Pooled it
- Beyond ICD-10 Codes: A Multi-class Machine-Learning Approach to Identify True IBD Cases in Electronic Medical Records.Digestive diseases and sciences · 2026Article
- Interpretable machine learning model based on non-contrast computed tomography within 24 hours of admission for early prediction of the risk of severe deterioration of acute pancreatitis.Quantitative imaging in medicine and surgery · 2026Article
- Uncertainty-calibrated adaptation of clinical transformer foundation models enhances in-hospital mortality and hospital readmission prediction.npj health systems · 2026Article
- Integrative single-cell and multi-omics analysis of ZBTB21-mediated serine metabolism in colorectal cancer: from metabolic reprogramming to immune microenvironment modulation.Cancer immunology, immunotherapy : CII · 2026Article
- Integrating multisequence radiomics and clinical features to predict seizure recurrence after gross total resection of pediatric low-grade epilepsy-associated brain tumors.Neuroradiology · 2026Article
- Predictive Cellular Signatures from Live Human Motor Neurons Distinguish TDP-43 ALS and Enable ALS Subtype Stratification.bioRxiv : the preprint server for biology · 2026Article
- Toward Personalized Rotator Cuff Physical Therapy Dosage Using a Machine Learning-Based Pilot Study with EMG.Bioengineering (Basel, Switzerland) · 2026Article
- GEMap: A comprehensive gene essentiality map for drug discovery.Acta pharmaceutica Sinica. B · 2026Article
- Predicting cardiovascular diseases using imbalanced data: An XGBoost-based analysis of the 2022 BRFSS dataset.American heart journal plus : cardiology research and practice · 2026Article
- Intelligent prediction of cervical instability based on MRI radiomics combined with clinical features.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Opportunistic osteoporosis screening using CT-based Hounsfield units: a machine learning approach for preoperative spine surgery planning.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Development and Validation of Machine Learning Models for Predicting 7-Day Mortality in Critically Ill Patients with Traumatic Spinal Cord Injury: A Multicenter Retrospective Study.Neurocritical care · 2026Article
- CFDFrontiers in immunology · 2026Article
- Leveraging a machine learning model to predict hospital readmission risk: integrating clinical and social determinants of health data.Frontiers in public health · 2026Article
- Key dietary amino acids modulating overweight/obesity risk in Chinese children and adolescents: a machine learning analysis of a national survey.Frontiers in nutrition · 2026Article
- An interpretable machine learning model integrating early immune biomarkers for predicting outcomes after spinal cord injury.Frontiers in neurology · 2026Article
- Predicting Spinal Cord Injury Prognosis Using Machine Learning: Systematic Review and Meta-Analysis.JMIR AI · 2025Review
- Predicting five-year comorbid bipolar disorder after attention-deficit/hyperactivity disorder diagnosis: a population-based machine learning approach.Child and adolescent psychiatry and mental health · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The accurate prediction of neurological outcomes in patients with cervical spinal cord injury (SCI) is difficult because of heterogeneity in patient characteristics, treatment strategies, and radiographic findings. Although machine learning algorithms may increase the accuracy of outcome predictions in various fields, limited information is available on their efficacy in the management of SCI. We analyzed data from 165 patients with cervical SCI, and extracted important factors for predicting prognoses. Extreme gradient boosting (XGBoost) as a machine learning model was applied to assess the reliability of a machine learning algorithm to predict neurological outcomes compared with that of conventional methodology, such as a logistic regression or decision tree. We used regularly obtainable data as predictors, such as demographics, magnetic resonance variables, and treatment strategies. Predictive tools, including XGBoost, a logistic regression, and a decision tree, were applied to predict neurological improvements in the functional motor status (ASIA [American Spinal Injury Association] Impairment Scale [AIS] D and E) 6 months after injury. We evaluated predictive performance, including accuracy and the area under the receiver operating characteristic curve (AUC). Regarding predictions of neurological improvements in patients with cervical SCI, XGBoost had the highest accuracy (81.1%), followed by the logistic regression (80.6%) and the decision tree (78.8%). Regarding AUC, the logistic regression showed 0.877, followed by XGBoost (0.867) and the decision tree (0.753). XGBoost reliably predicted neurological alterations in patients with cervical SCI. The utilization of predictive machine learning algorithms may enhance personalized management choices through pre-treatment categorization of patients.
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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.