Explainable predictive models of short stature and exploration of related environmental growth factors: a case-control study.

Journal: BMC endocrine disorders
PMID: 40355909

Abstract

BACKGROUND: Short stature is a prevalent pediatric endocrine disorder for which early detection and prediction are pivotal for improving treatment outcomes. However, existing diagnostic criteria often lack the necessary sensitivity and specificity because of the complex etiology of the disorder. Hence, this study aims to employ machine learning techniques to develop an interpretable predictive model for normal-variant short stature and to explore how growth environments influence its development.

Authors

  • Jiani Liu
    School of Public Health, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.
  • Xin Zhang
    First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China.
  • Wei Li
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Francis Manyori Bigambo
    Clinical Medical Research Center, Children's Hospital of Nanjing Medical University, 72 Guangzhou Rd, Nanjing, 210008, China.
  • Dandan Wang
    Department of Traditional Chinese Medicine Orthopedics and Traumatology, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
  • Xu Wang
    Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907.
  • Beibei Teng
    Department of pediatric , Nanjing Luhe People's Hospital, Yangzhou University, No. 28, Yan'an Road, Xiongzhou Town, Luhe District, Nanjing, 211500, Jiangsu, China. tengbeibei2006@126.com.

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