A machine learning model to identify early stage symptoms of SARS-Cov-2 infected patients.

Ahamad, Md Martuza; Aktar, Sakifa; Rashed-Al-Mahfuz, Md; Uddin, Shahadat; Liò, Pietro; Xu, Haoming; Summers, Matthew A; Quinn, Julian M W; Moni, Mohammad Ali

Ahamad, Md Martuza; Aktar, Sakifa; Rashed-Al-Mahfuz, Md; Uddin, Shahadat; Liò, Pietro; Xu, Haoming; Summers, Matthew A; Quinn, Julian M W; Moni, Mohammad Ali.

Afiliación

Ahamad MM; Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science & Technology University, Gopalganj 8100, Bangladesh.
Aktar S; Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science & Technology University, Gopalganj 8100, Bangladesh.
Rashed-Al-Mahfuz M; Department of Computer Science and Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh.
Uddin S; Complex Systems Research Group, Faculty of Engineering, The University of Sydney, Darlington, NSW 2008, Australia.
Liò P; Computer Laboratory, The University of Cambridge, 15 JJ Thomson Avenue, Cambridge, UK.
Xu H; Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
Summers MA; Chengdu Institute of Public Administration, Sichuan, 610110, China.
Quinn JMW; The Garvan Institute of Medical Research, Healthy Ageing Theme, Darlinghurst, NSW, Australia.
Moni MA; St Vincent's Clinical School, University of New South Wales, Faculty of Medicine, Sydney, Australia.

Expert Syst Appl ; 160: 113661, 2020 Dec 01.

Article en En | MEDLINE | ID: mdl-32834556

Palabras clave

COVID-19; Coronavirus; Early stage symptom; Machine learning; SARS-Cov-2

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Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Expert Syst Appl Año: 2020 Tipo del documento: Article País de afiliación: Bangladesh

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