Automated prediction of COVID-19 mortality outcome using clinical and laboratory data based on hierarchical feature selection and random forest classifier.

Amini, Nasrin; Mahdavi, Mahdi; Choubdar, Hadi; Abedini, Atefeh; Shalbaf, Ahmad; Lashgari, Reza

Amini, Nasrin; Mahdavi, Mahdi; Choubdar, Hadi; Abedini, Atefeh; Shalbaf, Ahmad; Lashgari, Reza.

Amini N; Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Mahdavi M; Institute of Medical Science and Technology (IMSAT), Shahid Beheshti University, Tehran, Iran.
Choubdar H; School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Abedini A; Institute of Medical Science and Technology (IMSAT), Shahid Beheshti University, Tehran, Iran.
Shalbaf A; School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Lashgari R; Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Comput Methods Biomech Biomed Engin ; 26(2): 160-173, 2023 Feb.

Article en En | MEDLINE | ID: mdl-35297747

Asunto(s)

COVID-19; Humanos; COVID-19/diagnóstico; Bosques Aleatorios; Algoritmos; Redes Neurales de la Computación; Curva ROC

Palabras clave

COVID-19; forward feature selection; laboratory features; mortality prediction; random forest

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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: COVID-19 Tipo de estudio: Clinical_trials / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Año: 2023 Tipo del documento: Article

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