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

Discrepancies between EHR and CTD diagnoses had a limited impact on model training but significantly affected performance evaluation when present in the test set, emphasizing the importance of accurate data validation.

Authors

Amy O’Higgins (ORCID: https://orcid.org/0000-0002-2020-1585)
Graham Healy (ORCID: https://orcid.org/0000-0001-6429-6339)
Brendan Egan (ORCID: https://orcid.org/0000-0001-8327-9016)
Mark Germaine (ORCID: https://orcid.org/0000-0002-7862-7714)

Institutions

Coombe Women & Infants University Hospital (IE)
Dublin City University (IE)

Publication Details

Journal: JMIR Medical Informatics
Published: 2025-06-17
DOI: https://doi.org/10.2196/72938
Citations: 6
Primary Topic: Machine Learning in Healthcare
Type: article
Field-Weighted Citation Impact: 14.89

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article

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

Amy O’Higgins, Graham Healy, Brendan Egan, Mark Germaine

Jun 17, 2025

6 citations

JMIR Medical Informatics

Machine Learning in Healthcare

14.89

article

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

Amy O’Higgins, Graham Healy, Brendan Egan, Mark Germaine

June 17, 2025article en

6 citations

Abstract

Discrepancies between EHR and CTD diagnoses had a limited impact on model training but significantly affected performance evaluation when present in the test set, emphasizing the importance of accurate data validation.

JMIR Medical InformaticsVol. 13

Coombe Women & Infants University Hospital (IE), Dublin City University (IE)

Openalex Percentile: Top 1%

Machine Learning in Healthcare

14.89

DOI Open Access PDF OpenAlex

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