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

BACKGROUND: 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 metabolomic dat...

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Detalles Bibliográficos
Autores principales: Hawken, Steven, Ducharme, Robin, Murphy, Malia S. Q., Olibris, Brieanne, Bota, A. Brianne, Wilson, Lindsay A., Cheng, Wei, Little, Julian, Potter, Beth K., Denize, Kathryn M., Lamoureux, Monica, Henderson, Matthew, Rittenhouse, Katelyn J., Price, Joan T., Mwape, Humphrey, Vwalika, Bellington, Musonda, Patrick, Pervin, Jesmin, Chowdhury, A. K. Azad, Rahman, Anisur, Chakraborty, Pranesh, Stringer, Jeffrey S. A., Wilson, Kumanan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9987787/
https://www.ncbi.nlm.nih.gov/pubmed/36877673
http://dx.doi.org/10.1371/journal.pone.0281074

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9987787/
https://www.ncbi.nlm.nih.gov/pubmed/36877673
http://dx.doi.org/10.1371/journal.pone.0281074

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