Predictive value of machine learning for the progression of gestational diabetes mellitus to type 2 diabetes: a systematic review and meta-analysis.

Journal: BMC medical informatics and decision making
PMID: 39806461

Abstract

BACKGROUND: This systematic review aims to explore the early predictive value of machine learning (ML) models for the progression of gestational diabetes mellitus (GDM) to type 2 diabetes mellitus (T2DM).

Authors

  • Meng Zhao
    School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China.
  • Zhixin Yao
    Department of Endocrinology and Metabolic Diseases, The Affiliated Hospital of Medical College Qingdao University, Qingdao, Shandong, 266003, China.
  • Yan Zhang
    Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, 110032, China.
  • Lidan Ma
    Department of Endocrinology and Metabolic Diseases, The Affiliated Hospital of Medical College Qingdao University, Qingdao, Shandong, 266003, China.
  • Wenquan Pang
    Department of Endocrinology and Metabolic Diseases, The Affiliated Hospital of Medical College Qingdao University, Qingdao, Shandong, 266003, China.
  • Shuyin Ma
    Department of Emergency Pediatric, The Affiliated Hospital of Medical College Qingdao University, Qingdao, Shandong, 266003, China.
  • Yijun Xu
    Department of Gastroenterology, Nanjing First Hospital, Nanjing Medical University, Nanjing 210006, China.
  • Lili Wei
    Shandong Institute for Food and Drug Control, Ji'nan 250101, China.

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