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

Clinical Informatics (2391) Obstetrics, Gynecology, and Reproductive Medicine (434) Decision Support for Health Professionals (2348) Secondary Use of Clinical Data for Research and Surveillance (659) Machine Learning (3340) Midwifery, Nurse-midwifery and Care for Pregnant or New Mothers (106)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48997, first published .
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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
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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 2026;20(1):111 View
  3. Barada S, Selvanambi R. Artificial intelligence for early prediction of gestational diabetes mellitus and preeclampsia: a systematic review of machine learning models and clinical decision support systems. Frontiers in Artificial Intelligence 2026;9 View
  4. Pérez M, Bastidas-Fuertes A, Intriago-Pazmiño M, Falconi L, Benavides J. From Signals to Symptoms: An Abstract-Level Systematic Mapping Review of Machine Learning for Preeclampsia Prediction. Machine Learning and Knowledge Extraction 2026;8(9):259 View
  5. Scotto J, Beckmeyer‐Borowko A, Guleria S, Alghamdi E, Andrade S, Grandi S, Mølgaard‐Nielsen D, Le Noan‐Lainé M, Rivero‐Ferrer E, Espin M, Lopez‐Leon S. Algorithms to Identify Preeclampsia in Routinely Collected Healthcare Data: A Systematic Review. Pharmacoepidemiology and Drug Safety 2026;35(10) View