ArticleFrontiers in endocrinology2022
Using Machine Learning Techniques to Develop Risk Prediction Models for the Risk of Incident Diabetic Retinopathy Among Patients With Type 2 Diabetes Mellitus: A Cohort Study.
Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 4 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 4 syntheses or guidelines pooled it, 44 citations in OpenAlex.
- Performance and limitation of machine learning algorithms for diabetic retinopathy screening and its application in health management: a meta-analysis.Biomedical engineering online · 2025Pooled it
- Risk prediction models for diabetic retinopathy: a systematic review.Frontiers in endocrinology · 2025Pooled it
- Scoring and validation of a simple model for predicting diabetic retinopathy in patients with type 2 diabetes based on a meta-analysis approach of 21 cohorts.Annals of medicine · 2024Pooled it
- Machine Learning Models for Prediction of Diabetic Microvascular Complications.Journal of diabetes science and technology · 2024Pooled it
- Evaluation and Comparison of Machine Learning Methods for Type 2 Diabetes Classification and Associated Factors.Healthcare informatics research · 2026Article
- Identifying Alzheimer's Disease Progression Subphenotypes Via a Graph-based Framework Using Electronic Health Records.Journal of healthcare informatics research · 2026Article
- Artificial intelligence in fundus photography for type 2 diabetes: a scoping review of systemic biomarkers and multi-organ risk prediction.Frontiers in digital health · 2026Review
- Association of serum uric acid with incident vascular calcification in patients with type 2 diabetes mellitus: a retrospective cohort study.Frontiers in endocrinology · 2026Article
- Artificial Intelligence for Diabetes Complication Prediction: A Systematic Review of Current Applications and Future Directions.Journal of diabetes science and technology · 2025Review
- Characterize Disease Progression Subphenotypes in Real World Populations with Overweight and Obesity using a Graph-based Neural Network Framework.medRxiv : the preprint server for health sciences · 2025Article
- Predicting diabetic retinopathy based on routine laboratory tests by machine learning algorithms.European journal of medical research · 2025Article
- Automated and code-free development of a risk calculator using ChatGPT-4 for predicting diabetic retinopathy and macular edema without retinal imaging.International journal of retina and vitreous · 2025Article
- Predict and prevent microvascular complications of type 2 diabetes: a cross-sectional and longitudinal study in Chinese communities.Frontiers in endocrinology · 2025Article
- A machine learning-based prediction of diabetic retinopathy using the Korea national health and nutrition examination survey (2008-2012, 2017-2021).Frontiers in medicine · 2025Article
- Cardiovascular Risk Factors as Independent Predictors of Diabetic Retinopathy in Type II Diabetes Mellitus: The Development of a Predictive Model.Medicina (Kaunas, Lithuania) · 2024Article
- Exploring the relationship between heavy metals and diabetic retinopathy: a machine learning modeling approach.Scientific reports · 2024Article
- A machine learning approach to predicting vascular calcification risk of type 2 diabetes: A retrospective study.Clinical cardiology · 2024Article
- Review
- Artificial intelligence in individualized retinal disease management.International journal of ophthalmology · 2024Review
- A Fair Individualized Polysocial Risk Score for Identifying Increased Social Risk in Type 2 Diabetes.Research square · 2023Article
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Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To construct and validate prediction models for the risk of diabetic retinopathy (DR) in patients with type 2 diabetes mellitus. Methods: Patients with type 2 diabetes mellitus hospitalized over the period between January 2010 and September 2018 were retrospectively collected. Eighteen baseline demographic and clinical characteristics were used as predictors to train five machine-learning models. The model that showed favorable predictive efficacy was evaluated at annual follow-ups. Multi-point data of the patients in the test set were utilized to further evaluate the model's performance. We also assessed the relative prognostic importance of the selected risk factors for DR outcomes. Results: Of 7943 collected patients, 1692 (21.30%) developed DR during follow-up. Among the five models, the XGBoost model achieved the highest predictive performance with an AUC, accuracy, sensitivity, and specificity of 0.803, 88.9%, 74.0%, and 81.1%, respectively. The XGBoost model's AUCs in the different follow-up periods were 0.834 to 0.966. In addition to the classical risk factors of DR, serum uric acid (SUA), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), estimated glomerular filtration rate (eGFR), and triglyceride (TG) were also identified to be important and strong predictors for the disease. Compared with the clinical diagnosis method of DR, the XGBoost model achieved an average of 2.895 years prior to the first diagnosis. Conclusion: The proposed model achieved high performance in predicting the risk of DR among patients with type 2 diabetes mellitus at each time point. This study established the potential of the XGBoost model to facilitate clinicians in identifying high-risk patients and making type 2 diabetes management-related decisions.
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