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

Clinical Information and Decision Making (2004) Machine Learning (2166) Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (93) Artificial Intelligence (2248) Decision Support for Health Professionals (1591) Obstetrics, Gynecology, and Reproductive Medicine (336)

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 (3) Tweetations Metrics

Journals

  1. Hoyos W, García R, Aguilar J. Multistage Training of Fuzzy Cognitive Maps to Predict Preeclampsia and Fetal Growth Restriction. IEEE Access 2025;13:136779 View
  2. Gao Z, Luo L, Han Y, Sun Y, Huang Y, Li S, Chen X, Yang H, Peng Z, Wang X, Zhao W, Wu X, Wu H, Bai J, Sun W, Zhou L, Ba Y. Serum Pepsinogen as a Biomarker of Gastrointestinal Stromal Tumors (GIST) in Stomach. Cancer Medicine 2025;14(17) View
  3. Ługowski F, Babińska J, Stanirowski P. Application of artificial intelligence in diagnosis and management of fetal growth disorders: a comprehensive review. Frontiers in Medicine 2026;12 View