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

<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 and metabolo...

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Hlavní autoři:	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
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Public Library of Science (PLoS) 2023-01-01
Edice:	PLoS ONE
On-line přístup:	https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0281074&type=printable
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