Machine Learning-Based Prediction of Large-for-Gestational-Age Infants in Mothers With Gestational Diabetes Mellitus.

Journal: The Journal of clinical endocrinology and metabolism
PMID:

Abstract

CONTEXT: Large-for-gestational-age (LGA), one of the most common complications of gestational diabetes mellitus (GDM), has become a global concern. The predictive performance of common continuous glucose monitoring (CGM) metrics for LGA is limited.

Authors

  • Mei Kang
    Clinical Research Center, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China.
  • Chengguang Zhu
    MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China.
  • Mengyu Lai
    Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China.
  • Jianrong Weng
    Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China.
  • Yan Zhuang
    Medical Psychology Department, Taiyuan Mental Hospital, Taiyuan, China.
  • Huichen He
    Clinical Research Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China.
  • Yan Qiu
    Clinical Research Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China.
  • Yixia Wu
    Clinical Research Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China.
  • Zhangxuan Qi
    Center for Medical Artificial Intelligence and Engineering, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China.
  • Weixia Zhang
  • Xianming Xu
    Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China.
  • Yanhong Zhu
    Department of Anesthesiology, The First People's Hospital of Pinghu, 500 Sangang Road, Danghu Street, Zhejiang, 314200, Zhejiang, China. 740005742@qq.com.
  • Yufan Wang
    Engineering Research Center for Digital Medicine of the Ministry of Education, Shanghai, China.
  • Xiaokang Yang