Machine learning guided postnatal gestational age assessment using new-born screening metabolomic data in South Asia and sub-Saharan Africa

BACKGROUND: Babies born early and/or small for gestational age in Low and Middle-income countries (LMICs) contribute substantially to global neonatal and infant mortality. Tracking this metric is critical at a population level for informed policy, advocacy, resources allocation and program evaluatio...

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Detalles Bibliográficos
Autores principales: Sazawal, Sunil, Ryckman, Kelli K., Das, Sayan, Khanam, Rasheda, Nisar, Imran, Jasper, Elizabeth, Dutta, Arup, Rahman, Sayedur, Mehmood, Usma, Bedell, Bruce, Deb, Saikat, Chowdhury, Nabidul Haque, Barkat, Amina, Mittal, Harshita, Ahmed, Salahuddin, Khalid, Farah, Raqib, Rubhana, Manu, Alexander, Yoshida, Sachiyo, Ilyas, Muhammad, Nizar, Ambreen, Ali, Said Mohammed, Baqui, Abdullah H., Jehan, Fyezah, Dhingra, Usha, Bahl, Rajiv
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8424940/
https://www.ncbi.nlm.nih.gov/pubmed/34493237
http://dx.doi.org/10.1186/s12884-021-04067-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8424940/
https://www.ncbi.nlm.nih.gov/pubmed/34493237
http://dx.doi.org/10.1186/s12884-021-04067-y

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