Characterising paediatric mortality during and after acute illness in Sub-Saharan Africa and South Asia: a secondary analysis of the CHAIN cohort using a machine learning approach

<p><strong>Background</strong> A better understanding of which children are likely to die during acute illness will help clinicians and policy makers target resources at the most vulnerable children. We used machine learning to characterise mortality in the 30-days following admiss...

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Main Authors:	Diallo, AH, Sayeem Bin Shahid, ASM, Khan, AF, Saleem, AF, Singa, BO, Gnoumou, BS, Tigoi, C, Otieno, CA, Bourdon, C, Oduol, CO, Lancioni, CL, Manyasi, C, McGrath, CJ, Maronga, C, Lwanga, C, Brals, D, Ahmed, D, Mondal, D, Denno, DM, Mangale, DI, Chimezi, E, Mbale, E, Mupere, E, Mamun, GMS, Ouedraogo, I, Githinji, G, Berkley, JA, Njirammadzi, J, Mukisa, J, Thitiri, J, Haggstrom, J, Carreon, JD, Walson, JL, Jemutai, J, Tickell, KD, Shahrin, L, Mallewa, M, Hossain, MI, Chisti, MJ, Timbwa, M, Mburu, M, Ngari, MM, Ngao, N, Aber, P, Harawa, PP, Sukhtankar, P, Bandsma, RHJ, Bamouni, RM, Molyneux, S, Feldman, S
Format:	Journal article
Language:	English
Published:	Elsevier 2023

Characterising paediatric mortality during and after acute illness in Sub-Saharan Africa and South Asia: a secondary analysis of the CHAIN cohort using a machine learning approach

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