ArticlePloS one2021
Predicting mortality of patients with acute kidney injury in the ICU using XGBoost model.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 3 of them syntheses that pooled it.
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Who cites it
66 citing papers in PubMed, 3 syntheses or guidelines pooled it, 131 citations in OpenAlex.
- Exploring the role of Artificial Intelligence in Acute Kidney Injury management: a comprehensive review and future research agenda.BMC medical informatics and decision making · 2024Pooled it
- Machine Learning Models for Prediction of Diabetic Microvascular Complications.Journal of diabetes science and technology · 2024Pooled it
- Advanced Machine Learning Models for Predicting Post-Thrombolysis Hemorrhagic Transformation in Acute Ischemic Stroke Patients: A Systematic Review and Meta-Analysis.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisPooled it
- Development and validation of an early prediction model for bee-sting-induced acute kidney injury using machine learning.Renal failure · 2026Article
- Machine learning-driven risk prediction for post-hospitalization diabetes case management: Integrating clinical and social determinants of health.International journal of medical informatics · 2026Article
- The utility of machine learning for predicting donor non-use in lung transplantation.JHLT open · 2026Article
- Machine learning models predict survival in unresectable stage III non-small cell lung cancer: Surveillance, Epidemiology, and End Results and Chinese cohort study.Journal of thoracic disease · 2026Article
- First-Day Glycemic Exposure and 28-Day Mortality in the ICU: A Multicohort Study.Research square · 2026Article
- Establishment and validation of an electrocardiogram vector-based machine learning model for the conversion of prone position electrocardiograms into standard electrocardiograms.European heart journal. Digital health · 2026Article
- First-Day Glycemic Exposure and 28-Day Mortality in the ICU: A Multicenter Cohort Study.Research square · 2026Article
- Artificial Intelligence in Nephrology-State of the Art on Theoretical Background, Molecular Applications, and Clinical Interpretation.International journal of molecular sciences · 2026Review
- Machine learning model for predicting the risk of AKI in early hemodynamically stable sepsis patients: a study based on the MIMIC IV database.Frontiers in medicine · 2026Article
- Novel graph-based centralized and decentralized approaches for early AKI prediction.Scientific reports · 2025Article
- Interpretable machine learning for the early prediction of acute kidney injury in preterm infants in the neonatal intensive care unit.Renal failure · 2025Article
- A Generative Patient Digital Twin for Sequential Treatment of Oropharyngeal Squamous Carcinomas.medRxiv : the preprint server for health sciences · 2025Article
- MicroRNAs signatures in small extracellular vesicles for psychological resilience in young adults using machine learning.Epigenomics · 2025Article
- Machine learning based CAGIB score predicts in-hospital mortality of cirrhotic patients with acute gastrointestinal bleeding.NPJ digital medicine · 2025Article
- Predictive model and risk analysis for outcomes in diabetic foot ulcer using eXtreme Gradient Boosting algorithm and SHapley Additive exPlanation.World journal of diabetes · 2025Article
- Article
- Acute Kidney Injury Prognosis Prediction Using Machine Learning Methods: A Systematic Review.Kidney medicine · 2025Review
6 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe goal of this study is to construct a mortality prediction model using the XGBoot (eXtreme Gradient Boosting) decision tree model for AKI (acute kidney injury) patients in the ICU (intensive care unit), and to compare its performance with that of three other machine learning models.
methodsWe used the eICU Collaborative Research Database (eICU-CRD) for model development and performance comparison. The prediction performance of the XGBoot model was compared with the other three machine learning models. These models included LR (logistic regression), SVM (support vector machines), and RF (random forest). In the model comparison, the AUROC (area under receiver operating curve), accuracy, precision, recall, and F1 score were used to evaluate the predictive performance of each model.
resultsA total of 7548 AKI patients were analyzed in this study. The overall in-hospital mortality of AKI patients was 16.35%. The best performing algorithm in this study was XGBoost with the highest AUROC (0.796, p < 0.01), F1(0.922, p < 0.01) and accuracy (0.860). The precision (0.860) and recall (0.994) of the XGBoost model rank second among the four models.
conclusionXGBoot model had obvious advantages of performance compared to the other machine learning models. This will be helpful for risk identification and early intervention for AKI patients at risk of death.
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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.