Predicting diabetic retinopathy based on routine laboratory tests by machine learning algorithms.

Journal: European journal of medical research
PMID: 40102923

Abstract

OBJECTIVES: This study aimed to identify risk factors for diabetic retinopathy (DR) and develop machine learning (ML)-based predictive models using routine laboratory data in patients with type 2 diabetes mellitus (T2DM).

Authors

  • Xiaohua Wan
    High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. wanxiaohua@ict.ac.cn.
  • Ruihuan Zhang
    The Inner Mongolia Medical Intelligent Diagnostics Big Data Research Institute, Inner Mongolia, People's Republic of China.
  • Yanan Wang
    Vasculocardiology Department, The Third People's Hospital of Datong, Datong, Shanxi, China.
  • Wei Wei
    Dept. Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
  • Biao Song
    Inner Mongolia Wesure Date Technology Co., Ltd, Inner Mongolia, P.R. China.
  • Lin Zhang
    Laboratory of Molecular Translational Medicine, Centre for Translational Medicine, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Clinical Research Center for Birth Defects of Sichuan Province, West China Second Hospital, Sichuan University, Chengdu, Sichuan, 610041, China. Electronic address: zhanglin@scu.edu.cn.
  • Yanwei Hu
    Department of Clinical Laboratory, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People's Republic of China. ywhu@mail.ccmu.edu.cn.

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