-
Loading metrics
Open Access
Peer-reviewed
Research Article
Development and validation of an interpretable longitudinal preeclampsia risk prediction using machine learning
-
Braden W. Eberhard,
Roles Formal analysis, Investigation, Methodology, Software, Validation, Writing – original draft, Writing – review & editing
Affiliation Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
⨯ -
Raphael Y. Cohen,
Roles Conceptualization, Data curation, Investigation, Software, Writing – review & editing
Affiliations Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America, Health Data Analytics Institute, Dedham, Massachusetts, United States of America
⨯ -
Nolan Wheeler,
Roles Data curation, Methodology, Software, Writing – review & editing
Affiliation Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
⨯ -
Ricardo Kleinlein,
Roles Data curation, Formal analysis, Methodology, Software, Writing – review & editing
Affiliation Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
⨯ -
John Rigoni,
Roles Data curation, Methodology, Software, Writing – review & editing
Affiliation Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
⨯ -
David W. Bates,
Roles Conceptualization, Investigation, Writing – review & editing
Affiliations Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America, Department of Health Care Policy and Management, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
⨯ -
Kathryn J. Gray ,
Contributed equally to this work with: Kathryn J. Gray, Vesela P. Kovacheva
Roles Conceptualization, Investigation, Methodology, Supervision, Writing – review & editing
* E-mail: kgray5@uw.edu (KJG); vkovacheva@bwh.harvard.edu (VPK).
Affiliation Department of Obstetrics and Gynecology, University of Washington, Seattle, Washington, United States of America
⨯ -
Vesela P. Kovacheva
Contributed equally to this work with: Kathryn J. Gray, Vesela P. Kovacheva
Roles Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing
* E-mail: kgray5@uw.edu (KJG); vkovacheva@bwh.harvard.edu (VPK).
Affiliation Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
⨯
Development and validation of an interpretable longitudinal preeclampsia risk prediction using machine learning
- Braden W. Eberhard,
- Raphael Y. Cohen,
- Nolan Wheeler,
- Ricardo Kleinlein,
- John Rigoni,
- David W. Bates,
- Kathryn J. Gray,
- Vesela P. Kovacheva
- Published: June 10, 2025
- https://doi.org/10.1371/journal.pone.0323873