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

Electronic Health Records (1322) Gestational Diabetes (40) Machine Learning (2081) Ontologies, Classifications, and Coding (328)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/72938, first published .
Label Accuracy in Electronic Health Records and Its Impact on Machine Learning Models for Early Prediction of Gestational Diabetes: 3-Step Retrospective Validation Study

Label Accuracy in Electronic Health Records and Its Impact on Machine Learning Models for Early Prediction of Gestational Diabetes: 3-Step Retrospective Validation Study

Label Accuracy in Electronic Health Records and Its Impact on Machine Learning Models for Early Prediction of Gestational Diabetes: 3-Step Retrospective Validation Study

Authors of this article:

Mark Germaine1, 2 Author Orcid Image ;   Amy C O'Higgins3 Author Orcid Image ;   Brendan Egan2 Author Orcid Image ;   Graham Healy1 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Della Corte A, Barbierato E, Gatti A. Deployable Machine Learning on MIMIC-IV: Leakage-Safe Prediction and Calibration for Incident Diabetes. Electronics 2025;14(22):4415 View
  2. Rong X, Zhang W, Luan S, Feng D, Wang N, Xiao J, He J, Liu J, Shu L. Real‐world prediction of early‐onset dementia by health record data: A multi‐center machine learning study. Alzheimer's & Dementia 2025;21(11) View