ArticleBioData mining2023
Prediction of the risk of developing end-stage renal diseases in newly diagnosed type 2 diabetes mellitus using artificial intelligence algorithms.
Article in BioData mining, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 33 citations in OpenAlex.
- Machine learning prediction models for diabetic kidney disease: systematic review and meta-analysis.Endocrine · 2024Pooled it
- Machine Learning Models for Prediction of Diabetic Microvascular Complications.Journal of diabetes science and technology · 2024Pooled it
- Applications of Machine Learning for Early Diagnosis and Prognosis of Chronic Kidney Disease: Current Evidence.Diagnostics (Basel, Switzerland) · 2026Review
- Predicting End-Stage Renal Disease and Mortality in Chronic Kidney Disease Using Machine Learning: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- Investigation and future trend prediction of disease burden of chronic kidney disease due to diabetes mellitus type 2 globally and in China from 1990 to 2021.Journal of diabetes investigation · 2026Article
- Development of an explainable machine learning model for predicting the occurrence of advanced diabetic kidney disease.Frontiers in endocrinology · 2026Article
- Explainable Artificial Intelligence for Early Diabetic Kidney Disease Risk Profiling in Type 2 Diabetes: Translational Readiness and Clinical Boundaries.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Review
- Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial Intelligence Enabled Lifestyle Medicine in Diabetes Care: A Narrative Review.American journal of lifestyle medicine · 2025Review
- A SuperLearner approach for predicting diabetic kidney disease upon the initial diagnosis of T2DM in hospital.BMC medical informatics and decision making · 2025Article
- Risk factors for developing osteoporosis in diabetic kidney disease and its correlation with calcium-phosphorus metabolism, FGF23, and Klotho.World journal of diabetes · 2025Article
- Setting Ranges in Potential Biomarkers for Type 2 Diabetes Mellitus Patients Early Detection By Sex-An Approach with Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2024Article
- Predictive model and risk analysis for peripheral vascular disease in type 2 diabetes mellitus patients using machine learning and shapley additive explanation.Frontiers in endocrinology · 2024Article
- Optimizing age-related hearing risk predictions: an advanced machine learning integration with HHIE-S.BioData mining · 2023Article
- Value of radiomics-based two-dimensional ultrasound for diagnosing early diabetic nephropathy.Scientific reports · 2023Article
- Using artificial intelligence algorithms to predict the overall survival of hemodialysis patients during the COVID-19 pandemic: A prospective cohort study.Journal of the Chinese Medical Association : JCMA · 2023Article
- Predicting three-month fasting blood glucose and glycated hemoglobin changes in patients with type 2 diabetes mellitus based on multiple machine learning algorithms.Scientific reports · 2023Article
- Improved Survival Analyses Based on Characterized Time-Dependent Covariates to Predict Individual Chronic Kidney Disease Progression.Biomedicines · 2023Article
- Artificial Intelligence Models in Diagnosis and Treatment of Kidney Diseases: Current Status and Prospects.Kidney diseases (Basel, Switzerland)Article
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
Abstract
objectivesType 2 diabetes mellitus (T2DM) imposes a great burden on healthcare systems, and these patients experience higher long-term risks for developing end-stage renal disease (ESRD). Managing diabetic nephropathy becomes more challenging when kidney function starts declining. Therefore, developing predictive models for the risk of developing ESRD in newly diagnosed T2DM patients may be helpful in clinical settings.
methodsWe established machine learning models constructed from a subset of clinical features collected from 53,477 newly diagnosed T2DM patients from January 2008 to December 2018 and then selected the best model. The cohort was divided, with 70% and 30% of patients randomly assigned to the training and testing sets, respectively.
resultsThe discriminative ability of our machine learning models, including logistic regression, extra tree classifier, random forest, gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine were evaluated across the cohort. XGBoost yielded the highest area under the receiver operating characteristic curve (AUC) of 0.953, followed by extra tree and GBDT, with AUC values of 0.952 and 0.938 on the testing dataset. The SHapley Additive explanation summary plot in the XGBoost model illustrated that the top five important features included baseline serum creatinine, mean serum creatine within 1 year before the diagnosis of T2DM, high-sensitivity C-reactive protein, spot urine protein-to-creatinine ratio and female gender.
conclusionsBecause our machine learning prediction models were based on routinely collected clinical features, they can be used as risk assessment tools for developing ESRD. By identifying high-risk patients, intervention strategies may be provided at an early stage.
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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 Socratic graph.