Discrimination of indoor versus outdoor environmental state with machine learning algorithms in myopia observational studies.

Journal: Journal of translational medicine
Published Date: Sep 18, 2019

Abstract

BACKGROUND: Wearable smart watches provide large amount of real-time data on the environmental state of the users and are useful to determine risk factors for onset and progression of myopia. We aim to evaluate the efficacy of machine learning algorithm in differentiating indoor and outdoor locations as collected by use of smart watches.

Authors

  • Bin Ye
    Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.
  • Kangping Liu
    Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, China.
  • Siting Cao
    Department of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, China.
  • Padmaja Sankaridurg
    Brien Holden Vision Institute, Sydney, NSW, Australia.
  • Wayne Li
    Brien Holden Vision Institute, Sydney, NSW, Australia.
  • Mengli Luan
    Department of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, China.
  • Bo Zhang
    Department of Clinical Pharmacology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China.
  • Jianfeng Zhu
    Department of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, China.
  • Haidong Zou
    Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China.
  • Xun Xu
    BGI-Shenzhen, Shenzhen 518083, China.
  • Xiangui He
    Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China. xianhezi@163.com.

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