ArticleBMC medical informatics and decision making2023
A comparison of random survival forest and Cox regression for prediction of mortality in patients with hemorrhagic stroke.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine Learning-Based Prediction of Poor Outcomes in Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis.Brain and behavior · 2026Pooled it
- Risk prediction models for short-term mortality in ICU stroke patients: a systematic review and meta-analysis.Frontiers in neurology · 2025Pooled it
- Impact of fever on the outcome non-anoxic acute brain injury patients: a systematic review and meta-analysis.Critical care (London, England) · 2024Pooled it
- Machine learning-based prediction model for long-term mortality after ischemic stroke.Scientific reports · 2026Article
- Interpretable Machine Learning Framework for Predicting Major Adverse Cardiovascular Events in Rheumatoid Arthritis Using Electronic Health Records: Multicenter Cohort Study.JMIR formative research · 2026Article
- Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence.NPJ digital medicine · 2026Article
- Outcome Risk Modeling for Disability-Free Longevity: Comparison of Random Forest and Random Survival Forest Methods.medRxiv : the preprint server for health sciences · 2026Article
- Machine Learning Models for Disease-Free Survival Analysis after Liver Resection for Hepatocellular Carcinoma: A Multicentric French Collaborative Study.Liver cancer · 2026Article
- Prognostic value of the SPICE score for 1-year functional outcomes in patients with acute stroke requiring mechanical ventilation.BMJ neurology open · 2026Article
- Development and validation of a stroke risk prediction model using regional healthcare big data and machine learning.International journal of nursing sciences · 2025Article
- Development of a Comprehensive Program for the Early Diagnosis and Treatment of Severe Infections in a Tertiary Hospital in Spain.Open forum infectious diseases · 2025Article
- Machine learning survival models for Non-alcoholic fatty liver disease based on a health checkup cohort.BMC gastroenterology · 2025Article
- Article
- Machine Learning Predictive Models for Survival in Patients with Brain Stroke.Health promotion perspectives · 2025Article
- Evaluation of Machine Learning and Traditional Statistical Models to Assess the Value of Stroke Genetic Liability for Prediction of Risk of Stroke Within the UK Biobank.Healthcare (Basel, Switzerland) · 2025Article
- 5-hydroxymethylcytosines from circulating cell-free DNA as noninvasive prognostic markers for gastric cancer.PloS one · 2025Article
- Predicting Stroke Risk Using Machine Learning: A Data-Driven Approach to Early Detection and Prevention.Stroke research and treatment · 2025Article
- A prognostic framework for predicting lung signet ring cell carcinoma via a machine learning based cox proportional hazard model.Journal of cancer research and clinical oncology · 2024Article
- Article
- Deep-learning survival analysis for patients with calcific aortic valve disease undergoing valve replacement.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate RSF and Cox models for mortality prediction of hemorrhagic stroke (HS) patients in intensive care unit (ICU).
methodsIn the training set, the optimal models were selected using five-fold cross-validation and grid search method. In the test set, the bootstrap method was used to validate. The area under the curve(AUC) was used for discrimination, Brier Score (BS) was used for calibration, positive predictive value(PPV), negative predictive value(NPV), and F1 score were combined to compare.
resultsA total of 2,990 HS patients were included. For predicting the 7-day mortality, the mean AUCs for RSF and Cox regression were 0.875 and 0.761, while the mean BS were 0.083 and 0.108. For predicting the 28-day mortality, the mean AUCs for RSF and Cox regression were 0.794 and 0.649, while the mean BS were 0.129 and 0.174. The mean AUCs of RSF and Cox versus conventional scores for predicting patients' 7-day mortality were 0.875 (RSF), 0.761 (COX), 0.736 (SAPS II), 0.723 (OASIS), 0.632 (SIRS), and 0.596 (SOFA), respectively.
conclusionsRSF provided a better clinical reference than Cox. Creatine, temperature, anion gap and sodium were important variables in both models.
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