Journal article

Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: A retrospective, multicentre machine learning study

Haotian Lin, Erping Long, Xiaohu Ding, Hongxing Diao, Zicong Chen, Runzhong Liu, Jialing Huang, Jingheng Cai, Shuangjuan Xu, Xiayin Zhang, Dongni Wang, Kexin Chen, Tongyong Yu, Dongxuan Wu, Xutu Zhao, Zhenzhen Liu, Xiaohang Wu, Yuzhen Jiang, Xiao Yang, Dongmei Cui Show all

PLoS Medicine | PUBLIC LIBRARY SCIENCE | Published : 2018

Abstract

BACKGROUND: Electronic medical records provide large-scale real-world clinical data for use in developing clinical decision systems. However, sophisticated methodology and analytical skills are required to handle the large-scale datasets necessary for the optimisation of prediction accuracy. Myopia is a common cause of vision loss. Current approaches to control myopia progression are effective but have significant side effects. Therefore, identifying those at greatest risk who should undergo targeted therapy is of great clinical importance. The objective of this study was to apply big data and machine learning technology to develop an algorithm that can predict the onset of high myopia, at s..

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University of Melbourne Researchers

Grants

Awarded by National Key R&D Program of China


Awarded by National Natural Science Foundation of China


Awarded by Guangdong Science and Technology Innovation Leading Talents


Funding Acknowledgements

This study was funded by the National Key R&D Program of China (2018YFC0116500), the National Natural Science Foundation of China (91546101, 81822010), the Guangdong Science and Technology Innovation Leading Talents (2017TX04R031), and Youth Pearl River Scholar in Guangdong (2016). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.