Machine learning approaches classify clinical malaria outcomes based on haematological parameters

BACKGROUND: Malaria is still a major global health burden, with more than 3.2 billion people in 91 countries remaining at risk of the disease. Accurately distinguishing malaria from other diseases, especially uncomplicated malaria (UM) from non-malarial infections (nMI), remains a challenge. Further...

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Detalles Bibliográficos
Autores principales: Morang’a, Collins M., Amenga–Etego, Lucas, Bah, Saikou Y., Appiah, Vincent, Amuzu, Dominic S. Y., Amoako, Nicholas, Abugri, James, Oduro, Abraham R., Cunnington, Aubrey J., Awandare, Gordon A., Otto, Thomas D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7702702/
https://www.ncbi.nlm.nih.gov/pubmed/33250058
http://dx.doi.org/10.1186/s12916-020-01823-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7702702/
https://www.ncbi.nlm.nih.gov/pubmed/33250058
http://dx.doi.org/10.1186/s12916-020-01823-3

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