This paper is in the following e-collection/theme issue:

Public (e)Health, Digital Epidemiology and Public Health Informatics (641) Prevention and Health Promotion (895)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/39507, first published .
Prevalence Patterns and Onset Prediction of High Myopia for Children and Adolescents in Southern China via Real-World Screening Data: Retrospective School-Based Study

Prevalence Patterns and Onset Prediction of High Myopia for Children and Adolescents in Southern China via Real-World Screening Data: Retrospective School-Based Study

Prevalence Patterns and Onset Prediction of High Myopia for Children and Adolescents in Southern China via Real-World Screening Data: Retrospective School-Based Study

Authors of this article:

Jieying Guan1 Author Orcid Image ;   Yingting Zhu1 Author Orcid Image ;   Qiuyue Hu2 Author Orcid Image ;   Shuyue Ma2 Author Orcid Image ;   Jingfeng Mu3 Author Orcid Image ;   Zhidong Li1 Author Orcid Image ;   Dong Fang3 Author Orcid Image ;   Xiaohua Zhuo1 Author Orcid Image ;   Haifei Guan2 Author Orcid Image ;   Qianhui Sun2 Author Orcid Image ;   Lin An2 Author Orcid Image ;   Shaochong Zhang3 Author Orcid Image ;   Peiwu Qin2 Author Orcid Image ;   Yehong Zhuo1 Author Orcid Image
Article Authors Cited by (2) Tweetations (4) Metrics

Journals

  1. Li W, Tu Y, Zhou L, Ma R, Li Y, Hu D, Zhang C, Lu Y. Study of myopia progression and risk factors in Hubei children aged 7–10 years using machine learning: a longitudinal cohort. BMC Ophthalmology 2024;24(1) View
  2. Xu S, Li L, Han W, Zhu Y, Hu Y, Li Z, Ruan Z, Zhou Z, Zhuo Y, Fu M, Yang X. Association Between Myopia and Pupil Diameter in Preschoolers: Evidence from a Machine Learning Approach Based on a Real-World Large-Scale Dataset. Ophthalmology and Therapy 2024;13(7):2009 View