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References from SSR-stacking: a hybrid GNN–ensemble framework for high-myopia classification under simulated baseline-SE missingness. Local targets link to admitted publications; unresolved targets remain external evidence.
Global prevalence of myopia and high myopia and temporal trends from 2000 through 2050
10.1016/j.ophtha.2016.01.006 · 2016 · External reference
Myopia
10.1016/s0140-6736(12)60272-4 · 2012 · External reference
The myopia boom
10.1038/519276a · 2015 · External reference
The complications of myopia: a review and meta-analysis
10.1167/iovs.61.4.49 · 2020 · External reference
mice: multivariate imputation by chained equations in R
10.18637/jss.v045.i03 · 2011 · External reference
Missing value estimation methods for DNA microarrays
10.1093/bioinformatics/17.6.520 · 2001 · External reference
Effect of time spent outdoors at school on the development of myopia among children in china: a randomized clinical trial
10.1001/jama.2015.10803 · 2015 · External reference
The association between near work activities and myopia in children–a systematic review and meta-analysis
10.1371/journal.pone.0140419 · 2015 · External reference
Progression of myopia in school-aged children after COVID-19 home confinement
10.1001/jamaophthalmol.2020.6239 · 2021 · External reference
Perspectives on genetic and environmental factors in myopia, its prediction, and the future direction of research
10.1167/iovs.66.7.4 · 2025 · External reference
Semi-supervised classification with graph convolutional networks
2017 · External reference
Inductive representation learning on large graphs
2017 · External reference
Graph attention networks
2018 · External reference
GAIN: missing data imputation using generative adversarial nets
2018 · External reference
Handling missing data with graph representation learning
2020 · External reference
Comparison of noncycloplegic and cycloplegic autorefraction in categorizing refractive error data in children
10.1111/aos.13569 · 2017 · External reference
IMI–defining and classifying myopia: a proposed set of standards for clinical and epidemiologic studies
10.1167/iovs.18-25957 · 2019 · External reference
XGBoost: a scalable tree boosting system
10.1145/2939672.2939785 · 2016 · External reference
Random forests
10.1023/a:1010933404324 · 2001 · External reference
10.1017/cbo9780511801389
10.1017/cbo9780511801389 · 2000 · External reference
Applied logistic regression
10.1002/9781118548387 · 2013 · External reference
A unified approach to interpreting model predictions
2017 · External reference
The potential of SHAP and machine learning for personalized explanations of influencing factors in myopic treatment for children
10.3390/medicina61010016 · 2025 · External reference
Machine learning to determine risk factors for myopia progression in primary school children: the anyang childhood eye study
10.1007/s40123-021-00450-2 · 2022 · External reference
Development and validation of predictive models for myopia onset and progression using extensive 15-year refractive data in children and adolescents
10.1186/s12967-024-05075-0 · 2024 · External reference
Accurate prediction of myopic progression and high myopia by machine learning
10.1093/pcmedi/pbae005 · 2024 · External reference
Prediction of myopia onset and shift in premyopic school-aged children: a machine learning-based algorithm
10.3389/fmed.2025.1646277 · 2025 · External reference
Deep learning prediction of childhood myopia progression using fundus image and refraction data
10.1001/jamanetworkopen.2025.53543 · 2026 · External reference
Interpretable machine learning models for predicting childhood myopia from school-based screening data
10.1038/s41598-025-05021-0 · 2025 · External reference
Machine learning approaches in high myopia: systematic review and meta-analysis
10.2196/57644 · 2025 · External reference
Artificial intelligence for myopia: current update and concern
10.4103/ijo.ijo_2632_25 · 2026 · External reference
Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence
10.1136/bmjopen-2020-048008 · 2021 · External reference
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
10.1136/bmj-2023-078378 · 2024 · External reference
AI-guided personalized predictions on myopia progression and interventions
10.1038/s41746-025-02308-4 · 2026 · External reference
