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

Clinical Information and Decision Making (1927) Machine Learning (2081) Decision Support for Health Professionals (1548)

Published on in Vol 24, No 7 (2022): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34108, first published .
Prediction of Maternal Hemorrhage Using Machine Learning: Retrospective Cohort Study

Prediction of Maternal Hemorrhage Using Machine Learning: Retrospective Cohort Study

Prediction of Maternal Hemorrhage Using Machine Learning: Retrospective Cohort Study

Authors of this article:

Jill M Westcott1 Author Orcid Image ;   Francine Hughes2 Author Orcid Image ;   Wenke Liu3, 4 Author Orcid Image ;   Mark Grivainis3, 4 Author Orcid Image ;   Iffath Hoskins1 Author Orcid Image ;   David Fenyo3, 4 Author Orcid Image
Article Authors Cited by (23) Tweetations (11) Metrics

Journals

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  11. Dogru S, Ezveci H, Akkus F, Bahçeci P, Karanfil Yaman F, Acar A. Artificial Intelligence in Predicting Postpartum Hemorrhage in Twin Pregnancies Undergoing Cesarean Section. Twin Research and Human Genetics 2025;28(1):53 View
  12. Huang X, Di X, Lin S, Yao M, Zheng S, Liu S, Lau W, Ye Z, Wang Z, Liu B. Artificial intelligence-based prediction of second stage duration in labor: a multicenter retrospective cohort analysis. eClinicalMedicine 2025;80:103072 View
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  14. Li X, Zhou Y, Gao F, Cheng D, Li W, Xia K, Yin H. Multi-class data augmentation for prediction of postpartum hemorrhage using improved ACGAN. Alexandria Engineering Journal 2025;128:426 View
  15. Wakefield B, Zapf M, Ende H. Artificial intelligence in prediction of postpartum hemorrhage: a primer and review. International Journal of Obstetric Anesthesia 2025;63:104694 View
  16. Li M, Su X, Liao W, Huang L, Yang Y, Wu X, Fan Y, Liu J, Yang X, Zeng Z, Ding W, Zeng W, Xu X. Development and Validation of An Interpretable Machine Learning-Based Prediction Model of Postpartum Hemorrhage in Placenta Previa Following Cesarean Section: A Multicenter Study. Reproductive Sciences 2025;32(9):3062 View
  17. Vasudevan L, Kibria M, Kucirka L, Shieh K, Wei M, Masoumi S, Balasubramanian S, Victor A, Conklin J, Gurcan M, Stuebe A, Page D. Machine Learning Models to Predict Risk of Maternal Morbidity and Mortality From Electronic Medical Record Data: Scoping Review. Journal of Medical Internet Research 2025;27:e68225 View
  18. Li X, Zhou Y, Gao F, Cheng D, Li W, Xia K, Yin H. MDGAIN-IFC: An intelligent construction method for full/refined benchmark dataset of postpartum hemorrhage based on MDGAIN and information fidelity. Alexandria Engineering Journal 2025;128:1057 View
  19. Sirichaisit K, Kaladee K, Chankong W, Romsaiyud W. ARTIFICIAL INTELLIGENCE PREDICTION MODELS FOR POSTPARTUM HEMORRHAGE: A SYSTEMATIC REVIEW AND META-ANALYSIS. Journal of Southeast Asian Medical Research 2025;9:e0240 View
  20. Correia V, Mascarenhas T, Mascarenhas M. Smart Pregnancy: AI-Driven Approaches to Personalised Maternal and Foetal Health—A Scoping Review. Journal of Clinical Medicine 2025;14(19):6974 View
  21. Coelho H, Silva F, Correia M, Rodrigues P. Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications. Journal of Clinical Medicine 2025;14(23):8479 View

Conference Proceedings

  1. Thakkar D, Gandhi V, Trivedi D. 2024 International Conference on Inventive Computation Technologies (ICICT). Forecasting Maternal Women's Health Risks using Random Forest Classifier View
  2. Li X, Zhou Y, Gao F, Cheng D, Xia K, Yin H. 2024 3rd International Conference on Health Big Data and Intelligent Healthcare (ICHIH). Non-local Catboost-based Prediction for Postpartum Hemorrhage View