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A predictive model for respiratory distress in patients with COVID-19: a retrospective study.
Zhang, Xin; Wang, Wei; Wan, Cheng; Cheng, Gong; Yin, Yuechuchu; Cao, Kaidi; Zhang, Xiaoliang; Wang, Zhongmin; Miao, Shumei; Yu, Yun; Hu, Jie; Huang, Ruochen; Ge, Yun; Chen, Ying; Liu, Yun.
Afiliação
  • Zhang X; Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
  • Wang W; Department of Information, the First Affiliated Hospital, Nanjing Medical University, Nanjing, China.
  • Wan C; Institute of Medical Informatics and Management, Nanjing Medical University, Nanjing, China.
  • Cheng G; School of Electronic Science and Engineering, Nanjing University, Nanjing, China.
  • Yin Y; Network Information Center, Wuhan No. 1 Hospital, Wuhan, China.
  • Cao K; Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
  • Zhang X; Institute of Medical Informatics and Management, Nanjing Medical University, Nanjing, China.
  • Wang Z; State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China.
  • Miao S; Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
  • Yu Y; Department of Information, the First Affiliated Hospital, Nanjing Medical University, Nanjing, China.
  • Hu J; Institute of Medical Informatics and Management, Nanjing Medical University, Nanjing, China.
  • Huang R; Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
  • Ge Y; Department of Information, the First Affiliated Hospital, Nanjing Medical University, Nanjing, China.
  • Chen Y; Institute of Medical Informatics and Management, Nanjing Medical University, Nanjing, China.
  • Liu Y; Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
Ann Transl Med ; 8(23): 1585, 2020 Dec.
Article em En | MEDLINE | ID: mdl-33437784
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Ann Transl Med Ano de publicação: 2020 Tipo de documento: Article País de afiliação: China
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Ann Transl Med Ano de publicação: 2020 Tipo de documento: Article País de afiliação: China