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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/8585| COVID-19 diagnosis prediction by symptoms of tested individuals: a machine learning approach | |
| Yazeed Zoabi Noam Shomron | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://doi.org/10.1101/2020.05.07.20093948 | |
| https://www.medrxiv.org/content/10.1101/2020.05.07.20093948v2 | |
| Motivation Effective screening of SARS-CoV-2 enables quick and efficient diagnosis of COVID-19 and can mitigate the burden on healthcare systems. Prediction models that combine several features to estimate the risk of infection have been developed in hopes of assisting medical staff worldwide in triaging patients when allocating limited healthcare resources. | |
| bioRxiv | |
| 14-05-2020 | |
| Preimpreso | |
| Inglés | |
| Público en general | |
| VIRUS RESPIRATORIOS | |
| Versión publicada | |
| publishedVersion - Versión publicada | |
| Aparece en las colecciones: | Materiales de Consulta y Comunicados Técnicos |
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| Fichero | Tamaño | Formato | |
|---|---|---|---|
| COVID-19 diagnosis prediction by symptoms of tested individual a machine learning approach.pdf | 587.6 kB | Adobe PDF | Visualizar/Abrir |