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Evolution of Clinical Phenotypes of COVID-19 Patients During Intensive Care Treatment: An Unsupervised Machine Learning Analysis.
Siepel, Sander; Dam, Tariq A; Fleuren, Lucas M; Girbes, Armand R J; Hoogendoorn, Mark; Thoral, Patrick J; Elbers, Paul W G; Bennis, Frank C.
Afiliação
  • Siepel S; Quantitative Data Analytics Group, Department of Computer Science, Faculty of Science, Vrije Universiteit, Amsterdam, the Netherlands.
  • Dam TA; Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands.
  • Fleuren LM; Quantitative Data Analytics Group, Department of Computer Science, Faculty of Science, Vrije Universiteit, Amsterdam, the Netherlands.
  • Girbes ARJ; Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands.
  • Hoogendoorn M; Quantitative Data Analytics Group, Department of Computer Science, Faculty of Science, Vrije Universiteit, Amsterdam, the Netherlands.
  • Thoral PJ; Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands.
  • Elbers PWG; Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands.
  • Bennis FC; Quantitative Data Analytics Group, Department of Computer Science, Faculty of Science, Vrije Universiteit, Amsterdam, the Netherlands.
J Intensive Care Med ; 38(7): 612-629, 2023 Jul.
Article em En | MEDLINE | ID: mdl-36744415
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: COVID-19 Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: J Intensive Care Med Assunto da revista: TERAPIA INTENSIVA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Holanda
Texto completo
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XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: COVID-19 Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: J Intensive Care Med Assunto da revista: TERAPIA INTENSIVA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Holanda