ReviewBiomedicines2024
Application of Machine Learning in Chronic Kidney Disease: Current Status and Future Prospects.
Review in Biomedicines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed.
- Bibliometrics and visualization analysis: Big data research on diabetic kidney disease from 2000 to 2025.Medicine · 2026Article
- Multi-Modal, Machine Learning-Driven Framework Integrating Multi-Omics for Personalized Chronic Kidney Disease Management.Journal of clinical medicine · 2026Review
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- Predicting End-Stage Renal Disease and Mortality in Chronic Kidney Disease Using Machine Learning: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- An informed monotone neural network for multi-stage chronic kidney disease prediction with integrated GFR estimation.Scientific reports · 2026Article
- Prediction of Five-Year Mortality Risk of Chronic Kidney Disease Using Artificial Intelligence-Based Models: A Retrospective Study.Health science reports · 2026Article
- Literature-informed ensemble machine learning for three-year diabetic kidney disease risk prediction in type 2 diabetes: Development, validation, and deployment of the PSMMC NephraRisk model.Diabetes, obesity & metabolism · 2026Article
- Early Detection of Chronic Kidney Disease in Men Using Lifestyle and Demographic Indicators: A Machine Learning Approach for Primary Healthcare Settings.Healthcare (Basel, Switzerland) · 2026Article
- Circulating Extracellular Vesicle MicroRNAs as Predictive Biomarkers for Kidney and Cardiovascular Events.Journal of the American Heart Association · 2026Article
- Identifying Diabetic Kidney Disease in Type 2 Diabetes Patients Using Explainable Machine Learning: A Case-Control Study.Journal of diabetes research · 2026Article
- Integrative multi-omics profiling for early diagnosis, stratification and personalized management of chronic kidney disease: a new paradigm.Clinical and experimental medicine · 2025Review
- The challenges of using electronic medical records (EMR) to facilitate guideline-directed medical therapy (GDMT) for patients with heart failure (HF) and chronic kidney disease (CKD).Heart failure reviews · 2025Review
- Exploring the association between volatile organic compound exposure and chronic kidney disease: evidence from explainable machine learning methods.Renal failure · 2025Article
- A machine-learning method for predicting the 1-year risk of death in maintenance hemodialysis patients based on continuous compliance with dialysis quality indicators.BMC nephrology · 2025Article
- Diagnosis and Emerging Biomarkers of Cystic Fibrosis-Related Kidney Disease (CFKD).Journal of clinical medicine · 2025Review
- Applicability Assessment of Technologies for Predictive and Prescriptive Analytics of Nephrology Big Data.Proteomics · 2025Review
- Machine learning approaches for predicting and diagnosing chronic kidney disease: current trends, challenges, solutions, and future directions.International urology and nephrology · 2025Review
- Research hotspots and frontiers of machine learning in renal medicine: a bibliometric and visual analysis from 2013 to 2024.International urology and nephrology · 2025Review
- A recursive embedding and clustering technique for unraveling asymptomatic kidney disease using laboratory data and machine learning.Scientific reports · 2025Article
- Uncovering glycolysis-driven molecular subtypes in diabetic nephropathy: a WGCNA and machine learning approach for diagnostic precision.Biology direct · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The emergence of artificial intelligence and machine learning (ML) has revolutionized the landscape of clinical medicine, offering opportunities to improve medical practice and research. This narrative review explores the current status and prospects of applying ML to chronic kidney disease (CKD). ML, at the intersection of statistics and computer science, enables computers to derive insights from extensive datasets, thereby presenting an interesting landscape for constructing statistical models and improving data interpretation. The integration of ML into clinical algorithms aims to increase efficiency and promote its adoption as a standard approach to data interpretation in nephrology. As the field of ML continues to evolve, collaboration between clinicians and data scientists is essential for defining data-sharing and usage policies, ultimately contributing to the advancement of precision diagnostics and personalized medicine in the context of CKD.
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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.