Novelelectronic health records applied for prediction of pre-eclampsia: Machine-learning algorithms.

Journal: Pregnancy hypertension
Published Date: Dec 1, 2021

Abstract

OBJECTIVE: To predict risk of pre-eclampsia (PE) in women using machine learning (ML) algorithms, based on electronic health records (EHR) collected at the early second trimester.

Authors

  • Yi-Xin Li
    Department of Obstetrics and Gynecology, Xinhua Hospital Chongming Branch, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Xiao-Ping Shen
    Department of Obstetrics and Gynecology, Xinhua Hospital Chongming Branch, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Chao Yang
    Translational Institute for Cancer Pain, Chongming Hospital Affiliated to Shanghai University of Health & Medicine Sciences (Xinhua Hospital Chongming Branch), Shanghai 202155, P. R. China.
  • Zuo-Zeng Cao
    Department of Obstetrics and Gynecology, Xinhua Hospital Chongming Branch, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Rui Du
    Department of Obstetrics and Gynecology, Xinhua Hospital Chongming Branch, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Min-da Yu
    Department of Obstetrics and Gynecology, Xinhua Hospital Chongming Branch, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Jun-Ping Wang
    Department of Obstetrics and Gynecology, Xinhua Hospital Chongming Branch, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Mei Wang
    Natural Products Utilization Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Oxford, MS, 38677, USA.

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