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

Theme Issue 2024: Machine Learning-Based Predictive Models Using Genomic Data (5) Clinical Information and Decision Making (1638) Machine Learning (1720) Development and Evaluation of Research Methods, Instruments and Tools (631) Artificial Intelligence (1707) Decision Support for Health Professionals (1334) Bioinformatics, genomics, tools and databases (24)

Published on in Vol 5 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/52059, first published .
Machine Learning Models for Prediction of Maternal Hemorrhage and Transfusion: Model Development Study

Machine Learning Models for Prediction of Maternal Hemorrhage and Transfusion: Model Development Study

Machine Learning Models for Prediction of Maternal Hemorrhage and Transfusion: Model Development Study

Authors of this article:

Homa Khorrami Ahmadzia1, 2 Author Orcid Image ;   Alexa C Dzienny3 Author Orcid Image ;   Mike Bopf4 Author Orcid Image ;   Jaclyn M Phillips1 Author Orcid Image ;   Jerome Jeffrey Federspiel5 Author Orcid Image ;   Richard Amdur6 Author Orcid Image ;   Madeline Murguia Rice7 Author Orcid Image ;   Laritza Rodriguez4 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Journals

  1. Boldina Y, Ivshin A. Machine learning opportunities to predict obstetric haemorrhages. Obstetrics, Gynecology and Reproduction 2024;18(3):365 View

Conference Proceedings

  1. Nahatkar S, Belhe A, Ganthade V, Uravane P, Pattewar T. 2025 International Conference on Computational, Communication and Information Technology (ICCCIT). Collating Random Forest Classifier and Artificial Neural Networks for the Risk Detection of Maternal Health View