ArticleBMC nephrology2023
Predicting hyperkalemia in patients with advanced chronic kidney disease using the XGBoost model.
Article in BMC nephrology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- Current status and future directions for the use of artificial intelligence in pediatric critical care nephrology.Pediatric nephrology (Berlin, Germany) · 2026Review
- Applications of Machine Learning for Early Diagnosis and Prognosis of Chronic Kidney Disease: Current Evidence.Diagnostics (Basel, Switzerland) · 2026Review
- Prediction of Chronic Kidney Disease Based on Simulated Serum Analysis by Vibrational Spectroscopy.Biosensors · 2026Article
- An explainable AI-driven hybrid feature selection approach for coronary artery disease diagnosis.Scientific reports · 2026Article
- Artificial Intelligence in Nephrology-State of the Art on Theoretical Background, Molecular Applications, and Clinical Interpretation.International journal of molecular sciences · 2026Review
- Article
- Application of machine learning models on predicting the length of hospital stay in fragility fracture patients.BMC medical informatics and decision making · 2024Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHyperkalemia is a common complication of chronic kidney disease (CKD). Hyperkalemia is associated with mortality, CKD progression, hospitalization, and high healthcare costs in patients with CKD. We developed a machine learning model to predict hyperkalemia in patients with advanced CKD at an outpatient clinic.
methodsThis retrospective study included 1,965 advanced CKD patients between January 1, 2010, and December 31, 2020 in Taiwan. We randomly divided all patients into the training (75%) and testing (25%) datasets. The primary outcome was to predict hyperkalemia (K
resultsIn a human-machine competition of hyperkalemia prediction, the AUC, PPV, and accuracy of the XGBoost model were 0.867 (95% confidence interval: 0.840-0.894), 0.700, and 0.933, which was significantly better than that of our clinicians. There were four variables that were chosen as high-ranking variables in XGBoost and logistic regression models, including hemoglobin, the serum potassium level in the previous visit, angiotensin receptor blocker use, and calcium polystyrene sulfonate use.
conclusionsThe XGBoost model provided better predictive performance for hyperkalemia than physicians at the outpatient clinic.
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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 OpenQuestion graph.