ArticleJournal of translational medicine2022
Prediction of 3-year risk of diabetic kidney disease using machine learning based on electronic medical records.
Article in Journal of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 4 of them syntheses that pooled it.
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Who cites it
49 citing papers in PubMed, 4 syntheses or guidelines pooled it, 99 citations in OpenAlex.
- 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
- Risk prediction models for diabetic nephropathy among type 2 diabetes patients in China: a systematic review and meta-analysis.Frontiers in endocrinology · 2024Pooled it
- Leveraging time-series electronic health records with large language models for chronic kidney disease diagnosis in primary care.BMJ health & care informatics · 2026Article
- Bibliometrics and visualization analysis: Big data research on diabetic kidney disease from 2000 to 2025.Medicine · 2026Article
- Machine learning model for intravenous immunoglobulin resistance in Kawasaki disease: model development and validation study.World journal of pediatrics : WJP · 2026Article
- Development and validation of prediction model for intrapulmonary metastasis in lung adenocarcinoma based on machine learning.Journal of thoracic disease · 2026Article
- SuperLearner approach for predicting imminent risk of fracture in older Chinese patients with newly diagnosed osteoporosis based on their routine blood test markers.BMC musculoskeletal disorders · 2026Article
- Low atherogenic index of plasma is associated with spontaneous hemorrhagic transformation in acute ischemic stroke patients: a retrospective cohort study and predictive modeling based on machine learning.Lipids in health and disease · 2026Article
- Systemic immune-inflammation (SII) index as a novel prognostic biomarker in critically ill patients with sepsis: analysis of the MIMIC-IV cohort and predictive modeling based on machine learning.BMC infectious diseases · 2026Article
- Risk stratification in diabetic kidney disease: a review of prediction models for methodological advances and clinical application.Journal of translational medicine · 2026Review
- Article
- Development and validation of an explainable neural network model for predicting progression in type 2 diabetic kidney disease.Frontiers in endocrinology · 2026Article
- Detection of Nondiabetic Kidney Disease in Patients with Diabetes Using Machine Learning and Electronic Medical Record Data.Kidney & blood pressure research · 2026Article
- Development of Machine Learning-Based Models to Predict Mortality in Intensive Care Unit Patients with Sepsis.Journal of inflammation research · 2026Article
- Artificial Intelligence for Diabetes Complication Prediction: A Systematic Review of Current Applications and Future Directions.Journal of diabetes science and technology · 2025Review
- Fused federated learning framework for secure and decentralized patient monitoring in healthcare 5.0 using IoMT.Scientific reports · 2025Article
- A Simplified Machine Learning Model for Predicting Reduced Kidney Function in Thai Patients with Type 2 Diabetes: A Retrospective Study.Journal of clinical medicine · 2025Article
- A SuperLearner approach for predicting diabetic kidney disease upon the initial diagnosis of T2DM in hospital.BMC medical informatics and decision making · 2025Article
Corrections and comments
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Authors and funding
21 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEstablished prediction models of Diabetic kidney disease (DKD) are limited to the analysis of clinical research data or general population data and do not consider hospital visits. Construct a 3-year diabetic kidney disease risk prediction model in patients with type 2 diabetes mellitus (T2DM) using machine learning, based on electronic medical records (EMR).
methodsData from 816 patients (585 males) with T2DM and 3 years of follow-up at the PLA General Hospital. 46 medical characteristics that are readily available from EMR were used to develop prediction models based on seven machine learning algorithms (light gradient boosting machine [LightGBM], eXtreme gradient boosting, adaptive boosting, artificial neural network, decision tree, support vector machine, logistic regression). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). Shapley additive explanation (SHAP) was used to interpret the results of the best performing model.
resultsThe LightGBM model had the highest AUC (0.815, 95% CI 0.747-0.882). Recursive feature elimination with random forest and SHAP plot based on LightGBM showed that older patients with T2DM with high homocysteine (Hcy), poor glycemic control, low serum albumin (ALB), low estimated glomerular filtration rate (eGFR), and high bicarbonate had an increased risk of developing DKD over the next 3 years.
conclusionsThis study constructed a 3-year DKD risk prediction model in patients with T2DM and normo-albuminuria using machine learning and EMR. The LightGBM model is a tool with potential to facilitate population management strategies for T2DM care in the EMR era.
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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.