ArticleScientific reports2024
Predicting CKD progression using time-series clustering and light gradient boosting machines.
Article in Scientific reports, 2024. 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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Who cites it
7 citing papers in PubMed.
- Predicting End-Stage Renal Disease and Mortality in Chronic Kidney Disease Using Machine Learning: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- Ensemble learning approaches for early prediction of chronic kidney disease based on polysomnographic phenotype analysis.NPJ digital medicine · 2025Article
- Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases.Bioengineering (Basel, Switzerland) · 2025Review
- Chronic kidney disease enhances alternative pathway activity: a new paradigm.The Journal of clinical investigation · 2025Review
- Machine learning approaches for predicting and diagnosing chronic kidney disease: current trends, challenges, solutions, and future directions.International urology and nephrology · 2025Review
- Article
- Elevated resting heart rate is associated with mortality in patients with chronic kidney disease.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting the transition of kidney function in chronic kidney disease is difficult as specific symptoms are lacking and often overlooked, and progress occurs due to complicating factors. In this study, we applied time-series cluster analysis and a light gradient boosting machine to predict the trajectories of kidney function in non-dialysis dependent chronic kidney disease patients with baseline estimated glomerular filtration rate (GFR) ≥ 45 mL/min/1.73 m
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Registered trials
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