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Ensemble learning for poor prognosis predictions: A case study on SARS-CoV-2.
Wu, Honghan; Zhang, Huayu; Karwath, Andreas; Ibrahim, Zina; Shi, Ting; Zhang, Xin; Wang, Kun; Sun, Jiaxing; Dhaliwal, Kevin; Bean, Daniel; Cardoso, Victor Roth; Li, Kezhi; Teo, James T; Banerjee, Amitava; Gao-Smith, Fang; Whitehouse, Tony; Veenith, Tonny; Gkoutos, Georgios V; Wu, Xiaodong; Dobson, Richard; Guthrie, Bruce.
Afiliação
  • Wu H; Institute of Health Informatics, University College London, London, United Kingdom.
  • Zhang H; Health Data Research UK, University College London, London, United Kingdom.
  • Karwath A; Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom.
  • Ibrahim Z; Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom.
  • Shi T; Health Data Research UK, University of Birmingham, Birmingham, United Kingdom.
  • Zhang X; Health Data Research UK, University College London, London, United Kingdom.
  • Wang K; Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
  • Sun J; Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom.
  • Dhaliwal K; Department of Pulmonary and Critical Care Medicine, People's Liberation Army Joint Logistic Support Force 920th Hospital, Kunming, China.
  • Bean D; Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital, Tongji University, Shanghai, China.
  • Cardoso VR; Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital, Tongji University, Shanghai, China.
  • Li K; Centre for Inflammation Research, Queens Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom.
  • Teo JT; Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
  • Banerjee A; Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom.
  • Gao-Smith F; Health Data Research UK, University of Birmingham, Birmingham, United Kingdom.
  • Whitehouse T; Institute of Health Informatics, University College London, London, United Kingdom.
  • Veenith T; Department of Stroke and Neurology, King's College Hospital NHS Foundation Trust, London, United Kingdom.
  • Gkoutos GV; Institute of Health Informatics, University College London, London, United Kingdom.
  • Wu X; Department of Intensive Care Medicine, Queen Elizabeth Hospital Birmingham, Birmingham, United Kingdom.
  • Dobson R; Birmingham Acute Care Research, University of Birmingham, Birmingham, United Kingdom.
  • Guthrie B; Department of Intensive Care Medicine, Queen Elizabeth Hospital Birmingham, Birmingham, United Kingdom.
J Am Med Inform Assoc ; 28(4): 791-800, 2021 03 18.
Article em En | MEDLINE | ID: mdl-33185672
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Prognóstico / Modelos Estatísticos / COVID-19 Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Aged / Aged80 / Female / Humans / Male / Middle aged País/Região como assunto: Asia / Europa Idioma: En Revista: J Am Med Inform Assoc Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Reino Unido País de publicação: Reino Unido
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Prognóstico / Modelos Estatísticos / COVID-19 Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Aged / Aged80 / Female / Humans / Male / Middle aged País/Região como assunto: Asia / Europa Idioma: En Revista: J Am Med Inform Assoc Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Reino Unido País de publicação: Reino Unido