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

Machine Learning (1934) Electronic Health Records (1258) Secondary Use of Clinical Data for Research and Surveillance (447) Tools, Programs and Algorithms (352) Clinical Informatics (1241) Clinical Information and Decision Making (1789) Decision Support for Health Professionals (1449)

Published on in Vol 10, No 2 (2022): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34932, first published .
Early Identification of Maternal Cardiovascular Risk Through Sourcing and Preparing Electronic Health Record Data: Machine Learning Study

Early Identification of Maternal Cardiovascular Risk Through Sourcing and Preparing Electronic Health Record Data: Machine Learning Study

Early Identification of Maternal Cardiovascular Risk Through Sourcing and Preparing Electronic Health Record Data: Machine Learning Study

Authors of this article:

Nawar Shara1 Author Orcid Image ;   Kelley M Anderson2 Author Orcid Image ;   Noor Falah3 Author Orcid Image ;   Maryam F Ahmad3 Author Orcid Image ;   Darya Tavazoei3 Author Orcid Image ;   Justin M Hughes3 Author Orcid Image ;   Bethany Talmadge4 Author Orcid Image ;   Samantha Crovatt4 Author Orcid Image ;   Ramon Dempers4 Author Orcid Image
Article Authors Cited by (2) Tweetations (8) Metrics

Journals

  1. Shara N, Mirabal-Beltran R, Talmadge B, Falah N, Ahmad M, Dempers R, Crovatt S, Eisenberg S, Anderson K. Use of Machine Learning for Early Detection of Maternal Cardiovascular Conditions: Retrospective Study Using Electronic Health Record Data. JMIR Cardio 2024;8:e53091 View
  2. 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