ArticleScientific reports2022
Outcome prediction for acute kidney injury among hospitalized children via eXtreme Gradient Boosting algorithm.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 32 citations in OpenAlex.
- Current status and future directions for the use of artificial intelligence in pediatric critical care nephrology.Pediatric nephrology (Berlin, Germany) · 2026Review
- Using health systems evidence to study and predict long-term outcomes of childhood acute kidney injury.Pediatric nephrology (Berlin, Germany) · 2026Article
- Interpretable prediction of anxiety symptoms during pregnancy via machine learning and SHAP.Archives of women's mental health · 2026Article
- Machine learning early risk assessment model for acute kidney injury in critically ill children: a retrospective cohort study.Frontiers in pediatrics · 2026Article
- A novel age-norm-capped baseline serum creatinine approach for acute kidney injury diagnosis in critically Ill children: a retrospective cohort study.Frontiers in pediatrics · 2026Article
- Diagnostic Stewardship of Blood Cultures in the Pediatric ICU Using Machine Learning.Hospital pediatrics · 2025Article
- Prediction Model for Risk of Death in Elderly Critically Ill Patients with Kidney Failure.Medicina (Kaunas, Lithuania) · 2025Article
- Research hotspots and frontiers of machine learning in renal medicine: a bibliometric and visual analysis from 2013 to 2024.International urology and nephrology · 2025Review
- The development and validation of a prediction model for post-AKI outcomes of pediatric inpatients.Clinical kidney journal · 2025Article
- Major Adverse Kidney Events in Hospitalized Older Patients With Acute Kidney Injury: Machine Learning-Based Model Development and Validation Study.Journal of medical Internet research · 2025Article
- Artificial intelligence and pediatric acute kidney injury: a mini-review and white paper.Frontiers in nephrology · 2025Review
- Use of CatBoost algorithm to identify the need for surgery in infants with necrotizing enterocolitis.Frontiers in pediatrics · 2025Article
- Artificial intelligence in early detection and prediction of pediatric/neonatal acute kidney injury: current status and future directions.Pediatric nephrology (Berlin, Germany) · 2024Review
- Development of a diagnostic model for biliary atresia based on MMP7 and serological tests using machine learning.Pediatric surgery international · 2024Article
- Heterogeneity in the definition of major adverse kidney events: a scoping review.Intensive care medicine · 2024Article
- Forecasting acute kidney injury and resource utilization in ICU patients using longitudinal, multimodal models.Journal of biomedical informatics · 2024Article
- Predicting pediatric cardiac surgery-associated acute kidney injury using machine learning.Pediatric nephrology (Berlin, Germany) · 2024Article
- Forecasting Acute Kidney Injury and Resource Utilization in ICU patients using longitudinal, multimodal models.medRxiv : the preprint server for health sciences · 2024Article
- Using machine learning models to predict the surgical risk of children with pancreaticobiliary maljunction and biliary dilatation.Surgery today · 2023Article
- A generalizable and interpretable model for mortality risk stratification of sepsis patients in intensive care unit.BMC medical informatics and decision making · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute kidney injury (AKI) is common among hospitalized children and is associated with a poor prognosis. The study sought to develop machine learning-based models for predicting adverse outcomes among hospitalized AKI children. We performed a retrospective study of hospitalized AKI patients aged 1 month to 18 years in the Second Xiangya Hospital of Central South University in China from 2015 to 2020. The primary outcomes included major adverse kidney events within 30 days (MAKE30) (death, new renal replacement therapy, and persistent renal dysfunction) and 90-day adverse outcomes (chronic dialysis and death). The state-of-the-art machine learning algorithm, eXtreme Gradient Boosting (XGBoost), and the traditional logistic regression were used to establish prediction models for MAKE30 and 90-day adverse outcomes. The models' performance was evaluated by split-set test. A total of 1394 pediatric AKI patients were included in the study. The incidence of MAKE30 and 90-day adverse outcomes was 24.1% and 8.1%, respectively. In the test set, the area under the receiver operating characteristic curve (AUC) of the XGBoost model was 0.810 (95% CI 0.763-0.857) for MAKE30 and 0.851 (95% CI 0.785-0.916) for 90-day adverse outcomes, The AUC of the logistic regression model was 0.786 (95% CI 0.731-0.841) for MAKE30 and 0.759 (95% CI 0.654-0.864) for 90-day adverse outcomes. A web-based risk calculator can facilitate the application of the XGBoost models in daily clinical practice. In conclusion, XGBoost showed good performance in predicting MAKE30 and 90-day adverse outcomes, which provided clinicians with useful tools for prognostic assessment in hospitalized AKI children.
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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.