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

Clinical Informatics (1335) Obstetrics, Gynecology, and Reproductive Medicine (335) Midwifery, Nurse-midwifery and Care for Pregnant or New Mothers (78) Decision Support for Health Professionals (1555) Machine Learning (2095) Secondary Use of Clinical Data for Research and Surveillance (475)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48997, first published .
Five-Feature Models to Predict Preeclampsia Onset Time From Electronic Health Record Data: Development and Validation Study

Five-Feature Models to Predict Preeclampsia Onset Time From Electronic Health Record Data: Development and Validation Study

Five-Feature Models to Predict Preeclampsia Onset Time From Electronic Health Record Data: Development and Validation Study

Authors of this article:

Hailey K Ballard1, 2 Author Orcid Image ;   Xiaotong Yang1 Author Orcid Image ;   Aditya D Mahadevan3, 4 Author Orcid Image ;   Dominick J Lemas2, 3, 5 Author Orcid Image ;   Lana X Garmire1 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Du Y, Benny P, Shao Y, Schlueter R, Gurary A, Lum-Jones A, Lassiter C, AlAkwaa F, Tiirikainen M, Towner D, Ward W, Garmire L. Multiomics analysis of umbilical cord hematopoietic stem cells from a multiethnic cohort of Hawaii reveals the intergenerational effect of maternal prepregnancy obesity and risks for cancers. GigaScience 2025;14 View
  2. Ivshin A, Malyshev N. Preeclampsia early risk stratification based on a multiparametric machine learning model and routinely collected clinical data. Obstetrics, Gynecology and Reproduction 2025 View