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Causal Bayesian machine learning to assess treatment effect heterogeneity by dexamethasone dose for patients with COVID-19 and severe hypoxemia.
Blette, Bryan S; Granholm, Anders; Li, Fan; Shankar-Hari, Manu; Lange, Theis; Munch, Marie Warrer; Møller, Morten Hylander; Perner, Anders; Harhay, Michael O.
Afiliação
  • Blette BS; Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Granholm A; Clinical Trials Methods and Outcomes Lab, Palliative and Advanced Illness Research (PAIR) Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Li F; Department of Intensive Care, Rigshospitalet-Copenhagen University Hospital, Copenhagen, Denmark.
  • Shankar-Hari M; Collaboration for Research in Intensive Care, Copenhagen, Denmark.
  • Lange T; Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA.
  • Munch MW; Center for Methods in Implementation and Prevention Science, Yale University School of Public Health, New Haven, CT, USA.
  • Møller MH; Centre for Inflammation Research, University of Edinburgh, Edinburgh, UK.
  • Perner A; Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
  • Harhay MO; Department of Intensive Care, Rigshospitalet-Copenhagen University Hospital, Copenhagen, Denmark.
Sci Rep ; 13(1): 6570, 2023 04 21.
Article em En | MEDLINE | ID: mdl-37085591
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: COVID-19 Idioma: En Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: COVID-19 Idioma: En Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos