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

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

Abstract 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 e...

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Bibliographic Details
Main Authors: Sunil Sazawal, Kelli K. Ryckman, Sayan Das, Rasheda Khanam, Imran Nisar, Elizabeth Jasper, Arup Dutta, Sayedur Rahman, Usma Mehmood, Bruce Bedell, Saikat Deb, Nabidul Haque Chowdhury, Amina Barkat, Harshita Mittal, Salahuddin Ahmed, Farah Khalid, Rubhana Raqib, Alexander Manu, Sachiyo Yoshida, Muhammad Ilyas, Ambreen Nizar, Said Mohammed Ali, Abdullah H. Baqui, Fyezah Jehan, Usha Dhingra, Rajiv Bahl
Format: Article
Language:English
Published: BMC 2021-09-01
Series:BMC Pregnancy and Childbirth
Subjects:
Pre-term births
Machine learning
Gestational age
New born screening
Online Access:https://doi.org/10.1186/s12884-021-04067-y

Internet

https://doi.org/10.1186/s12884-021-04067-y

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