ArticleiScience2022
Developing an ensemble machine learning model for early prediction of sepsis-associated acute kidney injury.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The application of machine learning in predicting post-cardiac surgery acute kidney injury in pediatric patients: a systematic review.Frontiers in pediatrics · 2025Pooled it
- 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
- Risk prediction of sepsis-associated acute kidney injury: development, validation of a machine learning model with multicenter data.BMC medical informatics and decision making · 2026Article
- Early recognition of sepsis-associated acute kidney injury based on BioBERT-BPNN multimodal clinical data analysis.PloS one · 2026Article
- Prediction of hospital mortality in sepsis-associated acute kidney injury using a machine-learning approach: a multicenter study using SHAP interpretability analysis.Clinical kidney journal · 2026Article
- Progress in sepsis prediction models: from traditional scoring systems to multimodal intelligence and clinical translation.Frontiers in medicine · 2026Review
- Development and validation of machine learning models to predict in-hospital mortality in ICU patients with sepsis and chronic kidney disease.BMC infectious diseases · 2025Article
- Beyond Biomarkers: Blending Copeptin and Clinical Cues to Distinguish Central Diabetes Insipidus from Primary Polydipsia in Children.Biomedicines · 2025Article
- Systematic Review and Meta-Analysis of Machine Learning Models for Acute Kidney Injury Risk Classification.Journal of the American Society of Nephrology : JASN · 2025Article
- Review
- Predictive models of sepsis-associated acute kidney injury based on machine learning: a scoping review.Renal failure · 2024Article
- Development and external validation of a nomogram for the early prediction of acute kidney injury in septic patients: a multicenter retrospective clinical study.Renal failure · 2024Article
- Investigating computational models for diagnosis and prognosis of sepsis based on clinical parameters: Opportunities, challenges, and future research directions.Journal of intensive medicine · 2024Review
- Improving cardiovascular risk prediction with machine learning: a focus on perivascular adipose tissue characteristics.Biomedical engineering online · 2024Article
- Artificial intelligence and machine learning's role in sepsis-associated acute kidney injury.Kidney research and clinical practice · 2024Article
- Article
- Machine learning approaches for practical predicting outpatient near-future AECOPD based on nationwide electronic medical records.iScience · 2024Article
- Article
- Acute Kidney Injury Prediction Model Using Cystatin-C, Beta-2 Microglobulin, and Neutrophil Gelatinase-Associated Lipocalin Biomarker in Sepsis Patients.International journal of nephrology and renovascular disease · 2024Article
- Federated machine learning for predicting acute kidney injury in critically ill patients: a multicenter study in Taiwan.Health information science and systems · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis-associated acute kidney injury (S-AKI) is very common and early prediction is beneficial. This study aiming to develop an accurate ensemble model to predict the risk of S-AKI based on easily available clinical information. Patients with sepsis from the United States (US) database Medical Information Mart for Intensive Care-IV were used as a modeling cohort to predict the occurrence of AKI by combining Support Vector Machine, Random Forest, Neural Network, and Extreme Gradient Boost as four first-level learners via stacking algorithm. The external validation databases were the eICU Collaborative Research Database from US and Critical Care Database comprising infection patients at Zigong Fourth People's Hospital from China, whose AUROC values for the ensemble model 48-12 h before the onset of AKI were 0.774-0.788 and 0.756-0.813, respectively. In this study, an ensemble model for early prediction of S-AKI onset was developed and it demonstrated good performance in multicenter external datasets.
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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 Socratic graph.