ArticleBMJ open diabetes research & care2022
Prediction of diabetic kidney disease with machine learning algorithms, upon the initial diagnosis of type 2 diabetes mellitus.
Article in BMJ open diabetes research & care, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine learning-based risk predictive models for diabetic kidney disease in type 2 diabetes mellitus patients: a systematic review and meta-analysis.Frontiers in endocrinology · 2025Pooled it
- 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
- Predicting End-Stage Renal Disease and Mortality in Chronic Kidney Disease Using Machine Learning: Retrospective Cohort Study.JMIR medical informatics · 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
- Risk stratification in diabetic kidney disease: a review of prediction models for methodological advances and clinical application.Journal of translational medicine · 2026Review
- Machine learning algorithms for diabetic kidney disease risk predictive model of Chinese patients with type 2 diabetes mellitus.Renal failure · 2025Article
- Multimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare.Nanomaterials (Basel, Switzerland) · 2025Review
- Explainable machine learning model incorporating social determinants of health to predict chronic kidney disease in type 2 diabetes patients.Journal of diabetes and metabolic disorders · 2025Article
- Article
- A SuperLearner approach for predicting diabetic kidney disease upon the initial diagnosis of T2DM in hospital.BMC medical informatics and decision making · 2025Article
- Interpretable machine learning method to predict the risk of pre-diabetes using a national-wide cross-sectional data: evidence from CHNS.BMC public health · 2025Article
- A Machine Learning-Based Prediction Model for Diabetic Kidney Disease in Korean Patients with Type 2 Diabetes Mellitus.Journal of clinical medicine · 2025Article
- Diabetic peripheral neuropathy detection of type 2 diabetes using machine learning from TCM features: a cross-sectional study.BMC medical informatics and decision making · 2025Observational
- From bytes to nephrons: AI's journey in diabetic kidney disease.Journal of nephrology · 2025Review
- Predicting the onset of chronic kidney disease (CKD) for diabetic patients with aggregated longitudinal EMR data.PLOS digital health · 2025Article
- Artificial intelligence in chronic kidney diseases: methodology and potential applications.International urology and nephrology · 2025Review
- Construction of Risk Prediction Model of Type 2 Diabetic Kidney Disease Based on Deep Learning (Diabetes Metab J 2024;48:771-9).Diabetes & metabolism journal · 2024Article
- 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
- An Ensemble Machine Learning and Data Mining Approach to Enhance Stroke Prediction.Bioengineering (Basel, Switzerland) · 2024Article
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Authors and funding
7 authors.
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Abstract
introductionDiabetic kidney disease (DKD) accounts for the majority of increased risk of mortality for patients with diabetes, and eventually manifests in approximately half of those patients diagnosed with type 2 diabetes mellitus (T2DM). Although increased screening frequency can avoid delayed diagnoses, this is not uniformly implemented. The purpose of this study was to develop and retrospectively validate a machine learning algorithm (MLA) that predicts stages of DKD within 5 years upon diagnosis of T2DM. RESEARCH DESIGN AND
methodsTwo MLAs were trained to predict stages of DKD severity, and compared with the Centers for Disease Control and Prevention (CDC) risk score to evaluate performance. The models were validated on a hold-out test set as well as an external dataset sourced from separate facilities.
resultsThe MLAs outperformed the CDC risk score in both the hold-out test and external datasets. Our algorithms achieved an area under the receiver operating characteristic curve (AUROC) of 0.75 on the hold-out set for prediction of any-stage DKD and an AUROC of over 0.82 for more severe endpoints, compared with the CDC risk score with an AUROC <0.70 on all test sets and endpoints.
conclusionThis retrospective study shows that an MLA can provide timely predictions of DKD among patients with recently diagnosed T2DM.
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