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

Formative Evaluation of Digital Health Interventions (3262) Clinical Informatics (1333) Clinical Information and Decision Making (1930) Pregnancy Information, Education and Lifestyle Interventions (408) Obstetrics, Gynecology, and Reproductive Medicine (332) Machine Learning (2088)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/54109, first published .
Fast Healthcare Interoperability Resources–Based Support System for Predicting Delivery Type: Model Development and Evaluation Study

Fast Healthcare Interoperability Resources–Based Support System for Predicting Delivery Type: Model Development and Evaluation Study

Fast Healthcare Interoperability Resources–Based Support System for Predicting Delivery Type: Model Development and Evaluation Study

Authors of this article:

João Coutinho-Almeida1, 2, 3 Author Orcid Image ;   Alexandrina Cardoso4 Author Orcid Image ;   Ricardo Cruz-Correia1, 2, 3 Author Orcid Image ;   Pedro Pereira-Rodrigues1, 2, 3 Author Orcid Image
Article Authors Cited by (4) Tweetations (3) Metrics

Journals

  1. Bai J, Lu Y, Liu H, He F, Guo X. Editorial: New technologies improve maternal and newborn safety. Frontiers in Medical Technology 2024;6 View
  2. Bai J, Kang X, Wang W, Yang Z, Ou W, Huang Y, Lu Y. A multimodal model in the prediction of the delivery mode using data from a digital twin-empowered labor monitoring system. DIGITAL HEALTH 2024;10 View
  3. Wei W. Clinical study of two reversible arterial blockade methods in the treatment of scar pregnancy under combined hysterolaparoscopy. American Journal of Translational Research 2024;16(11):6770 View
  4. Yang M, Long D, Li Y, Liu X, Bai Z, Li Z. An explainable machine learning model in predicting vaginal birth after cesarean section. The Journal of Maternal-Fetal & Neonatal Medicine 2025;38(1) View