Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers

Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers

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<h4>Background</h4> Accurate estimates of gestational age (GA) at birth are important for preterm birth surveillance but can be challenging to obtain in low income countries. Our objective was to develop machine learning models to accurately estimate GA shortly after birth using clinical...

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Bibliographic Details
Main Authors: Steven Hawken, Robin Ducharme, Malia S. Q. Murphy, Brieanne Olibris, A. Brianne Bota, Lindsay A. Wilson, Wei Cheng, Julian Little, Beth K. Potter, Kathryn M. Denize, Monica Lamoureux, Matthew Henderson, Katelyn J. Rittenhouse, Joan T. Price, Humphrey Mwape, Bellington Vwalika, Patrick Musonda, Jesmin Pervin, A. K. Azad Chowdhury, Anisur Rahman, Pranesh Chakraborty, Jeffrey S. A. Stringer, Kumanan Wilson
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2023-01-01
Series:PLoS ONE
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9987787/?tool=EBI

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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9987787/?tool=EBI

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