Machine learning prediction of gestational age from metabolic screening markers resistant to ambient temperature transportation: Facilitating use of this technology in low resource settings of South Asia and East Africa

BACKGROUND: Knowledge of gestational age is critical for guiding preterm neonatal care. In the last decade, metabolic gestational dating approaches emerged in response to a global health need; because in most of the developing world, accurate antenatal gestational age estimates are not feasible. The...

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Detalles Bibliográficos
Autores principales: Sazawal, Sunil, Das, Sayan, Ryckman, Kelli K, Khanam, Rasheda, Nisar, Imran, Deb, Saikat, Jasper, Elizabeth A, Rahman, Sayedur, Mehmood, Usma, Dutta, Arup, Chowdhury, Nabidul Haque, Barkat, Amina, Mittal, Harshita, Ahmed, Salahuddin, Khalid, Farah, Ali, Said Mohammed, Raqib, Rubhana, Ilyas, Muhammad, Nizar, Ambreen, Manu, Alexander, Russell, Donna, Yoshida, Sachiyo, Baqui, Abdullah H, Jehan, Fyezah, Dhingra, Usha, Bahl, Rajiv
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Society of Global Health 2022
Materias:
Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9022771/
https://www.ncbi.nlm.nih.gov/pubmed/35493781
http://dx.doi.org/10.7189/jogh.12.04021

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9022771/
https://www.ncbi.nlm.nih.gov/pubmed/35493781
http://dx.doi.org/10.7189/jogh.12.04021

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