ArticleScientific reports2019
Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 104 papers, 7 of them syntheses that pooled it.
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Who cites it
104 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Applications of artificial intelligence in chronic kidney disease: a systematic review.Revista de saude publica · 2026Pooled it
- Convergence of disciplines: a systematic review of multidisciplinary development approaches in artificial intelligence.Frontiers in digital health · 2025Pooled it
- Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis.Renal failure · 2024Pooled it
- Machine learning prediction models for diabetic kidney disease: systematic review and meta-analysis.Endocrine · 2024Pooled it
- Applications of Artificial Intelligence and Machine Learning in Emergency Medicine Triage - A Systematic Review.Medical archives (Sarajevo, Bosnia and Herzegovina) · 2024Pooled it
- Evaluation of Machine Learning Methods Developed for Prediction of Diabetes Complications: A Systematic Review.Journal of diabetes science and technology · 2023Pooled it
- Using Natural Language Processing to Measure and Improve Quality of Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2021Pooled it
- Bibliometrics and visualization analysis: Big data research on diabetic kidney disease from 2000 to 2025.Medicine · 2026Article
- Artificial intelligence in cardio-kidney-metabolic care: Transforming integrated disease management through data-informed innovation.International journal of obesity (2005) · 2026Review
- Biomarkers in Diabetic Kidney Disease: Early Detection, Prognostic Assessment, and Integration with Multi-Omics Signatures.Life (Basel, Switzerland) · 2026Review
- Predicting Chronic Kidney Disease from Biomarkers: An Explainable Machine Learning Approach.Diagnostics (Basel, Switzerland) · 2026Article
- Workflow Bottlenecks and Staff Readiness in an NHS Emergency Urology Clinic: A Prospective Service Evaluation to Inform Future AI-Supported Triage.Healthcare (Basel, Switzerland) · 2026Article
- Next-generation therapeutics for diabetic kidney disease.Nature reviews. Nephrology · 2026Review
- Explainable machine learning models for early prediction of acute kidney injury after cardiac surgery.International urology and nephrology · 2026Article
- Cost-Effectiveness Analysis of Artificial Intelligence-Driven Risk Stratification in Patients With Diabetic Kidney Disease in the US Veterans Population.Kidney medicine · 2026Article
- A neuroimaging functional connectivity signature of emotional conflict monitoring predicting cognitive decline in type 2 diabetes.Scientific reports · 2026Article
- Algor-Ethics in Diabetes Care: Mapping the Route.Diabetes/metabolism research and reviews · 2026Review
- An intelligent ensemble machine learning model for early detection of chronic kidney disease in aging populations.Scientific reports · 2026Article
- Artificial Intelligence in Diabetic Kidney Disease Research: Bibliometric Analysis From 2006 to 2024.JMIR diabetes · 2026Article
- Chronic renal disease detection developments with future potential to improve veterinary clinical practice.Frontiers in veterinary science · 2026Review
44 more citing papers are in PubMed but not listed here.
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is expected to support clinical judgement in medicine. We constructed a new predictive model for diabetic kidney diseases (DKD) using AI, processing natural language and longitudinal data with big data machine learning, based on the electronic medical records (EMR) of 64,059 diabetes patients. AI extracted raw features from the previous 6 months as the reference period and selected 24 factors to find time series patterns relating to 6-month DKD aggravation, using a convolutional autoencoder. AI constructed the predictive model with 3,073 features, including time series data using logistic regression analysis. AI could predict DKD aggravation with 71% accuracy. Furthermore, the group with DKD aggravation had a significantly higher incidence of hemodialysis than the non-aggravation group, over 10 years (N = 2,900). The new predictive model by AI could detect progression of DKD and may contribute to more effective and accurate intervention to reduce hemodialysis.
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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.