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

AI in Older Adult Care (13) Aging with Chronic Disease (100) Computer-Aided Diagnosis (115) Machine Learning (1427) Secondary Use of Clinical Data for Research and Surveillance (356)

Published on in Vol 7 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/59810, first published .
Determinants of Visual Impairment Among Chinese Middle-Aged and Older Adults: Risk Prediction Model Using Machine Learning Algorithms

Determinants of Visual Impairment Among Chinese Middle-Aged and Older Adults: Risk Prediction Model Using Machine Learning Algorithms

Determinants of Visual Impairment Among Chinese Middle-Aged and Older Adults: Risk Prediction Model Using Machine Learning Algorithms

Authors of this article:

Lijun Mao1 Author Orcid Image ;   Zhen Yu2 Author Orcid Image ;   Luotao Lin3 Author Orcid Image ;   Manoj Sharma4, 5 Author Orcid Image ;   Hualing Song1 Author Orcid Image ;   Hailei Zhao1 Author Orcid Image ;   Xianglong Xu1, 6, 7 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Lijun Mao   1 , BSc ;   Zhen Yu   2 , PhD ;   Luotao Lin   3 , PhD ;   Manoj Sharma   4, 5 , PhD ;   Hualing Song   1 , MSc ;   Hailei Zhao   1 * , MD ;   Xianglong Xu   1, 6, 7 * , PhD

1 School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China

2 Monash e-Research Centre, Faculty of Engineering, Airdoc Research, Nvidia AI Technology Research Centre, Monash University, Melbourne, Australia

3 Nutrition and Dietetics Program, Department of Individual, Family, and Community Education, University of New Mexico, Albuquerque, NM, United States

4 Department of Social and Behavioral Health, School of Public Health, University of Nevada, Las Vegas, NV, United States

5 Department of Internal Medicine, Kirk Kerkorian School of Medicine, University of Nevada, Las Vegas, NV, United States

6 School of Translational Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia

7 Artificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Carlton, Victoria, Australia

*these authors contributed equally

Corresponding Author:

  • Xianglong Xu, PhD
  • School of Public Health
  • Shanghai University of Traditional Chinese Medicine
  • 1200 Cai Lun Road
  • Shanghai, 201203
  • China
  • Phone: 86 18721538966
  • Fax: 86 021-51322421
  • Email: xianglongxu@shutcm.edu.cn