ArticleEuropean journal of medical research2025
Predicting diabetic retinopathy based on routine laboratory tests by machine learning algorithms.
Article in European journal of medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Application of construction and validation of logistic regression model in risk prediction of non-proliferative diabetic retinopathy in type 2 diabetes mellitus.Diabetic medicine : a journal of the British Diabetic Association · 2026Article
- Foveal Density and Multi-Domain OCTA Biomarkers May Help Identify Preclinical Diabetic Microvasculopathy in Type 2 Diabetes Mellitus.Medicina (Kaunas, Lithuania) · 2026Article
- Machine learning-based prediction of diabetic retinopathy using clinlabomics: a multi-center study.BMC medical informatics and decision making · 2026Article
- Prediction of diabetic peripheral neuropathy in type 2 diabetes using high-frequency ultrasound and shear-wave elastography of the median and tibial nerves: a nomogram study.European journal of medical research · 2026Article
- Integrating Ophthalmology, Endocrinology, and Digital Health: A Bibliometric Analysis of Telemedicine for Diabetic Retinopathy.Healthcare (Basel, Switzerland) · 2026Review
- Association of the platelet-to-albumin ratio with diabetic nephropathy lesions via a fine-tuning-free large language model framework.Frontiers in medicine · 2026Article
- A machine learning model for diabetic retinopathy risk stratification using routine blood and urine parameters: insights into kidney-eye crosstalk.Frontiers in endocrinology · 2026Article
- Identifying Patients with Rapid Progression to Diabetic Retinopathy in Type 2 Diabetes: A Simple Prediction Model for Community Settings.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Article
- Artificial Intelligence for Diabetes Complication Prediction: A Systematic Review of Current Applications and Future Directions.Journal of diabetes science and technology · 2025Review
- Nonlinear association between visceral fat metabolism score and heart failure: insights from LightGBM modeling and SHAP-Driven feature interpretation in NHANES.BMC medical informatics and decision making · 2025Article
- Optimized prediction of diabetes complications using ensemble learning with Bayesian optimization: a cost-efficient laboratory-based approach.Frontiers in endocrinology · 2025Article
- Validation and interpretation of machine-learning models for rapid identification of active tuberculosis infection using routine laboratory indicators.Frontiers in cellular and infection microbiology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
objectivesThis study aimed to identify risk factors for diabetic retinopathy (DR) and develop machine learning (ML)-based predictive models using routine laboratory data in patients with type 2 diabetes mellitus (T2DM).
methodsClinical data from 4259 T2DM inpatients at Beijing Tongren Hospital were analyzed, divided into a model construction data set (N = 3936) and an external validation data set (N = 323). Using 39 optimal variables, a prediction model was constructed using the eXtreme Gradient Boosting (XGBoost) algorithm and compared with four other algorithms: support vector machine (SVM), gradient boosting decision tree (GBDT), neural network (NN), and logistic regression (LR). The Shapley Additive exPlanation (SHAP) method was employed to interpret the XGBoost model. External validation was performed to assess model performance.
resultsDR was present in 47.69% (N = 1877) of T2DM patients in the model construction data set. Among the models tested, the XGBoost model performed best with an AUC of 0.831, accuracy of 0.757, sensitivity of 0.754, specificity of 0.759, and F1-score of 0.752. SHAP explained feature importance for XGBoost model and identified key risk factors for DR. External validation yielded an accuracy of 0.650 for the XGBoost model.
conclusionsThe XGBoost-based prediction model effectively assesses DR risk in T2DM patients using routine laboratory data, aiding clinicians in identifying high-risk individuals and guiding personalized management strategies, especially in medically underserved areas.
Indexed as
Identifiers
What Socratic holds
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.