A comprehensive and bias-free machine learning approach for risk prediction of preeclampsia with severe features in a nulliparous study cohort

A comprehensive and bias-free machine learning approach for risk prediction of preeclampsia with severe features in a nulliparous study cohort

Abstract Preeclampsia is one of the leading causes of maternal morbidity, with consequences during and after pregnancy. Because of its diverse clinical presentation, preeclampsia is an adverse pregnancy outcome that is uniquely challenging to predict and manage. In this paper, we developed racial bi...

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Bibliographic Details
Main Authors: Yun C. Lin, Daniel Mallia, Andrea O. Clark-Sevilla, Adam Catto, Alisa Leshchenko, Qi Yan, David M. Haas, Ronald Wapner, Itsik Pe’er, Anita Raja, Ansaf Salleb-Aouissi
Format: Article
Language:English
Published: BMC 2024-12-01
Series:BMC Pregnancy and Childbirth
Subjects:
Preeclampsia
Machine learning
PlGF
Fairness in machine learning
Preeclampsia with severe features
Ensemble model
Online Access:https://doi.org/10.1186/s12884-024-06988-w
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https://doi.org/10.1186/s12884-024-06988-w

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