Detection of acute dengue virus infection, with and without concurrent malaria infection, in a cohort of febrile children in Kenya, 2014–2019, by clinicians or machine learning algorithms

Poor access to diagnostic testing in resource limited settings restricts surveillance for emerging infections, such as dengue virus (DENV), to clinician suspicion, based on history and exam observations alone. We investigated the ability of machine learning to detect DENV based solely on data availa...

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Detalles Bibliográficos
Autores principales: Vu, David M., Krystosik, Amy R., Ndenga, Bryson A., Mutuku, Francis M., Ripp, Kelsey, Liu, Elizabeth, Bosire, Carren M., Heath, Claire, Chebii, Philip, Maina, Priscilla Watiri, Jembe, Zainab, Malumbo, Said Lipi, Amugongo, Jael Sagina, Ronga, Charles, Okuta, Victoria, Mutai, Noah, Makenzi, Nzaro G., Litunda, Kennedy A., Mukoko, Dunstan, King, Charles H., LaBeaud, A. Desiree
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10370704/
https://www.ncbi.nlm.nih.gov/pubmed/37494331
http://dx.doi.org/10.1371/journal.pgph.0001950

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10370704/
https://www.ncbi.nlm.nih.gov/pubmed/37494331
http://dx.doi.org/10.1371/journal.pgph.0001950

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