Unraveling complex relationships between COVID-19 risk factors using machine learning based models for predicting mortality of hospitalized patients and identification of high-risk group: a large retrospective study.

Banoei, Mohammad Mehdi; Rafiepoor, Haniyeh; Zendehdel, Kazem; Seyyedsalehi, Monireh Sadat; Nahvijou, Azin; Allameh, Farshad; Amanpour, Saeid

Banoei, Mohammad Mehdi; Rafiepoor, Haniyeh; Zendehdel, Kazem; Seyyedsalehi, Monireh Sadat; Nahvijou, Azin; Allameh, Farshad; Amanpour, Saeid.

Afiliación

Banoei MM; Department of Biological Sciences, University of Calgary, Calgary, AB, Canada.
Rafiepoor H; Cancer Biology Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran.
Zendehdel K; Cancer Biology Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran.
Seyyedsalehi MS; Cancer Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran.
Nahvijou A; Cancer Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran.
Allameh F; Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.
Amanpour S; Cancer Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran.

Front Med (Lausanne) ; 10: 1170331, 2023.

Article en En | MEDLINE | ID: mdl-37215714

Palabras clave

COVID-19; COVID-19 risk factors; clustering COVID-19 patients; machine learning; prediction model

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Med (Lausanne) Año: 2023 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Suiza

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