Predicting preterm birth using electronic medical records from multiple prenatal visits

Predicting preterm birth using electronic medical records from multiple prenatal visits

Abstract This study aimed to predict preterm birth in nulliparous women using machine learning and easily accessible variables from prenatal visits. Elastic net regularized logistic regression models were developed and evaluated using 5-fold cross-validation on data from 8,830 women in the Nulliparo...

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Bibliographic Details
Main Authors: Chenyan Huang, Xi Long, Myrthe van der Ven, Maurits Kaptein, S. Guid Oei, Edwin van den Heuvel
Format: Article
Language:English
Published: BMC 2024-12-01
Series:BMC Pregnancy and Childbirth
Subjects:
Preterm birth
Prediction
Machine learning
Logistics regression
Online Access:https://doi.org/10.1186/s12884-024-07049-y
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