Development and external validation of a machine learning model to predict bronchopulmonary dysplasia using dynamic factors

Development and external validation of a machine learning model to predict bronchopulmonary dysplasia using dynamic factors

Abstract We hypothesized that incorporating postnatal dynamic factors would enhance the prediction accuracy of bronchopulmonary dysplasia in preterm infants. This retrospective cohort study included neonates born before 32 weeks of gestation at Seoul National University Hospital between 2013 and 202...

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Bibliographic Details
Main Authors: Ho Jung Choi, Garam Lee, Seung Han Shin, Seung Mi Lee, Hyung-Chul Lee, Jin A. Sohn, Jin A. Lee, Han-suk Kim
Format: Article
Language:English
Published: Nature Portfolio 2025-04-01
Series:Scientific Reports
Subjects:
Bronchopulmonary dysplasia
Prediction model
Machine learning
Preterm infants
Online Access:https://doi.org/10.1038/s41598-025-98087-9
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https://doi.org/10.1038/s41598-025-98087-9

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