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Machine learning-based prediction of transfusion.
Mitterecker, Andreas; Hofmann, Axel; Trentino, Kevin M; Lloyd, Adam; Leahy, Michael F; Schwarzbauer, Karin; Tschoellitsch, Thomas; Böck, Carl; Hochreiter, Sepp; Meier, Jens.
Afiliação
  • Mitterecker A; Institute for Machine Learning, Johannes Kepler University, Linz, Austria.
  • Hofmann A; Department of Anesthesiology and Critical Care Medicine, University and University Hospital, Zürich, Switzerland.
  • Trentino KM; Data and Digital Innovation, East Metropolitan Health Service, Perth, Australia.
  • Lloyd A; Data and Digital Innovation, East Metropolitan Health Service, Perth, Australia.
  • Leahy MF; Department of Haematology, PathWest Laboratory Medicine, Royal Perth Hospital, Perth, Australia.
  • Schwarzbauer K; Institute for Machine Learning, Johannes Kepler University, Linz, Austria.
  • Tschoellitsch T; Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH and Johannes Kepler University, Linz, Austria.
  • Böck C; Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH and Johannes Kepler University, Linz, Austria.
  • Hochreiter S; Institute for Machine Learning, Johannes Kepler University, Linz, Austria.
  • Meier J; Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH and Johannes Kepler University, Linz, Austria.
Transfusion ; 60(9): 1977-1986, 2020 09.
Article em En | MEDLINE | ID: mdl-32596877
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Tomada de Decisões Assistida por Computador / Aprendizado de Máquina / Hospitalização Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Female / Humans / Male País como assunto: Oceania Idioma: En Ano de publicação: 2020 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Tomada de Decisões Assistida por Computador / Aprendizado de Máquina / Hospitalização Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Female / Humans / Male País como assunto: Oceania Idioma: En Ano de publicação: 2020 Tipo de documento: Article