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

作者：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 ke Yang, Dongmei Cui, Wenyan Liu, Yingfeng Zheng, Lixia Luo, Haibo Wang, Chi‐Chao Chan, Ian George Morgan, Mingguang He, Yizhi Liu · 发表于：PLoS Medicine · 年份：2018 · DOI：10.1371/journal.pmed.1002674 · 被引用次数：173 · 研究领域：Ophthalmology and Visual Impairment Studies、Retinopathy of Prematurity Studies、Retinal Diseases and Treatments

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 specific future time points, among Chinese school-aged children. METHODS AND FINDINGS: Real-world clinical refraction data were derived from electronic medical record systems in 8 ophthalmic centres from January 1, 2005, to December 30, 2015. The variables of age, spherical equivalent (SE), and annual progression rate were used to develop an algorithm to predict SE and onset of high myopia (SE ≤ -6.0 dioptres) up to 10 years in the future. Random forest machine learning was used for algorithm training and validation. Electronic medical records from the Zhongshan Ophthalmic Centre (a major tertiary ophthalmic centre in China) were used as the training set. Ten-fold cross-validation and out-of-bag (OOB) methods were applied for internal validation. The remaining 7 independent datasets were us...