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 approachResearch in context

Summary: Background: 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 admission and the 180-days after...

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Main Authors: Abdoulaye Hama Diallo, Abu Sadat Mohammad Sayeem Bin Shahid, Ali Fazal Khan, Ali Faisal Saleem, Benson O. Singa, Blaise Siezanga Gnoumou, Caroline Tigoi, Catherine Achieng Otieno, Celine Bourdon, Chris Odhiambo Oduol, Christina L. Lancioni, Christine Manyasi, Christine J. McGrath, Christopher Maronga, Christopher Lwanga, Daniella Brals, Dilruba Ahmed, Dinesh Mondal, Donna M. Denno, Dorothy I. Mangale, Emmanuel Chimezi, Emmie Mbale, Ezekiel Mupere, Gazi Md. Salahuddin Mamun, Issaka Ouedraogo, George Githinji, James A. Berkley, Jenala Njirammadzi, John Mukisa, Johnstone Thitiri, Jonas Haggstrom, Joseph D. Carreon, Judd L. Walson, Julie Jemutai, Kirkby D. Tickell, Lubaba Shahrin, MacPherson Mallewa, Md. Iqbal Hossain, Mohammod Jobayer Chisti, Molly Timbwa, Moses Mburu, Moses M. Ngari, Narshion Ngao, Peace Aber, Philliness Prisca Harawa, Priya Sukhtankar, Robert H.J. Bandsma, Roseline Maimouna Bamouni, Sassy Molyneux, Sergey Feldman, Shalton Mwaringa, Shamsun Nahar Shaima, Syed Asad Ali, Syeda Momena Afsana, Syera Banu, Tahmeed Ahmed, Wieger P. Voskuijl, Zaubina Kazi
Format: Article
Language:English
Published: Elsevier 2023-03-01
Series:EClinicalMedicine
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Online Access:http://www.sciencedirect.com/science/article/pii/S2589537023000159