Identification and Optimization of Contributing Factors for Precocious Puberty by Machine/Deep Learning Methods in Chinese Girls.

Journal: Frontiers in endocrinology
PMID: 35846287

Abstract

BACKGROUND AND OBJECTIVES: As the worldwide secular trends are toward earlier puberty, identification of contributing factors for precocious puberty is critical. We aimed to identify and optimize contributing factors responsible for onset of precocious puberty machine learning/deep learning algorithms in girls.

Authors

  • Bo Pang
    College of Water Sciences, Beijing Normal University; Beijing 100875, China; Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China. Electronic address: pb@bnu.edu.cn.
  • Qiong Wang
    Beijing Meiling Biotechnology Corporation, Beijing, 102600, PR China.
  • Min Yang
    College of Food Science and Engineering, Ocean University of China, Qingdao, 266003, Shandong, China.
  • Mei Xue
    Beijing Engineering Research Center of Diagnosis and Treatment of Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Beijing 100043, China.
  • Yicheng Zhang
    Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China.
  • Xiangling Deng
    Graduate School, Beijing University of Chinese Medicine, Beijing, China.
  • Zhixin Zhang
    School of Mathematics Sciences, Anhui University, Hefei 230601, China.
  • Wenquan Niu
    Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing 100029, China. Electronic address: niuwenquan_shcn@163.com.

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