This paper is in the following e-collection/theme issue:

Clinical Information and Decision Making (1920) Machine Learning (2079) Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (87) Artificial Intelligence (2136) Decision Support for Health Professionals (1543) Obstetrics, Gynecology, and Reproductive Medicine (332)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/70068, first published .
Predictive Models Using Machine Learning to Identify Fetal Growth Restriction in Patients With Preeclampsia: Development and Evaluation Study

Predictive Models Using Machine Learning to Identify Fetal Growth Restriction in Patients With Preeclampsia: Development and Evaluation Study

Predictive Models Using Machine Learning to Identify Fetal Growth Restriction in Patients With Preeclampsia: Development and Evaluation Study

Authors of this article:

Qing Hua1 Author Orcid Image ;   Fengchun Yang2, 3 Author Orcid Image ;   Yadan Zhou1 Author Orcid Image ;   Fenglian Shi1 Author Orcid Image ;   Xiaoyan You1 Author Orcid Image ;   Jing Guo1 Author Orcid Image ;   Li Li1 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Qing Hua   1 * , MMed ;   Fengchun Yang   2, 3 * , MSc ;   Yadan Zhou   1 , MMed ;   Fenglian Shi   1 , MMed ;   Xiaoyan You   1 , MMed ;   Jing Guo   1 , MMed ;   Li Li   1 * , MMed

1 Department of Obstetrics and Gynecology, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China

2 Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China

3 Department of Infectious Diseases, Tongji Hospital, Tongji Medical College and State Key Laboratory for Diagnosis and Treatment of Severe Zoonostic Infectious Disease, Huazhong University of Science and Technology, Wuhan, China

*these authors contributed equally

Corresponding Author:

  • Li Li, MMed
  • Department of Obstetrics and Gynecology
  • Zhengzhou Central Hospital Affiliated to Zhengzhou University
  • No. 16, Tongbai North Road, Zhongyuan District
  • Zhengzhou 450007
  • China
  • Phone: 86 13683816225
  • Fax: 86 13683816225
  • Email: zzsylili@zzu.edu.cn