Machine learning based predictive modeling and risk factors for prolonged SARS-CoV-2 shedding

Autor: Yani Zhang, Qiankun Li, Haijun Duan, Liang Tan, Ying Cao, Junxin Chen
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Journal of Translational Medicine, Vol 22, Iss 1, Pp 1-10 (2024)
Druh dokumentu: article
ISSN: 1479-5876
DOI: 10.1186/s12967-024-05872-7
Popis: Abstract Background The global outbreak of the coronavirus disease 2019 (COVID-19) has been enormously damaging, in which prolonged shedding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, previously 2019-nCoV) infection is a challenge in the prevention and treatment of COVID-19. However, there is still incomplete research on the risk factors that affect delayed shedding of SARS-CoV-2. Methods In a retrospective analysis of 56,878 hospitalized patients in the Fangcang Shelter Hospital (National Convention and Exhibition Center) in Shanghai, China, we compared patients with the duration of SARS-CoV-2 viral shedding > 12 days with those days
Databáze: Directory of Open Access Journals
Nepřihlášeným uživatelům se plný text nezobrazuje