Associations of maternal and paternal characteristics with cardiac malformations using real-world data and machine learning
Screening associations, with interpretation aided by unsupervised machine learning methods, identifies, in this study, both known risk factors and potential signals. Signals might be explained by confounding, other systematic errors, or chance, and warrant further investigation.
Authors
- Sonia Hernández–Dı́az (ORCID: https://orcid.org/0000-0003-1458-7642)
- Jeremy Philip Brown (ORCID: https://orcid.org/0000-0002-9941-7759)
- Loreen Straub (ORCID: https://orcid.org/0000-0003-2623-2726)
- Krista F. Huybrechts (ORCID: https://orcid.org/0000-0001-5805-8430)
- Brian T. Bateman (ORCID: https://orcid.org/0000-0001-5950-8683)
- Dominik Heider (ORCID: https://orcid.org/0000-0002-3108-8311)
Institutions
- Brigham and Women's Hospital (US)
- Harvard University (US)
- University of Münster (DE)
- Stanford University (US)
Publication Details
- Journal
- Communications Medicine
- Published
- 2025-10-15
- DOI
- https://doi.org/10.1038/s43856-025-01139-4
- Primary Topic
- Congenital Heart Disease Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institute of Child Health and Human Development