Can machine learning methods be used for identification of at-risk neonates in low-resource settings? A prospective cohort study

INTRODUCTION: Timely identification of at-risk neonates (ARNs) in the community is essential to reduce mortality in low-resource settings. Tools such as American Academy of Pediatrics pulse oximetry (POx) and WHO Young Infants Clinical Signs (WHOS) have high specificity but low sensitivity to identi...

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Detalles Bibliográficos
Autores principales: Hasan, Babar S, Hoodbhoy, Zahra, Khan, Amna, Nogueira, Mariana, Bijnens, Bart, Chowdhury, Devyani
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2023
Materias:
Neonatology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10626794/
https://www.ncbi.nlm.nih.gov/pubmed/37918940
http://dx.doi.org/10.1136/bmjpo-2023-002134

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10626794/
https://www.ncbi.nlm.nih.gov/pubmed/37918940
http://dx.doi.org/10.1136/bmjpo-2023-002134

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