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Clustering of critically ill patients using an individualized learning approach enables dose optimization of mobilization in the ICU.
Fuest, Kristina E; Ulm, Bernhard; Daum, Nils; Lindholz, Maximilian; Lorenz, Marco; Blobner, Kilian; Langer, Nadine; Hodgson, Carol; Herridge, Margaret; Blobner, Manfred; Schaller, Stefan J.
Afiliação
  • Fuest KE; Technical University Munich, School of Medicine, Klinikum Rechts der Isar, Department of Anaesthesiology & Intensive Care Medicine, Munich, Germany.
  • Ulm B; Technical University Munich, School of Medicine, Klinikum Rechts der Isar, Department of Anaesthesiology & Intensive Care Medicine, Munich, Germany.
  • Daum N; Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Department of Anesthesiology and Operative Intensive Care Medicine (CVK, CCM), Berlin, Germany.
  • Lindholz M; Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Department of Anesthesiology and Operative Intensive Care Medicine (CVK, CCM), Berlin, Germany.
  • Lorenz M; Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Department of Anesthesiology and Operative Intensive Care Medicine (CVK, CCM), Berlin, Germany.
  • Blobner K; Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Department of Anesthesiology and Operative Intensive Care Medicine (CVK, CCM), Berlin, Germany.
  • Langer N; Technical University Munich, School of Medicine, Klinikum Rechts der Isar, Department of Orthopedics, Munich, Germany.
  • Hodgson C; Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Department of Anesthesiology and Operative Intensive Care Medicine (CVK, CCM), Berlin, Germany.
  • Herridge M; Acute and Critical Care, Monash University, Melbourne, VIC, Australia.
  • Blobner M; Interdepartmental Division of Critical Care Medicine, University of Toronto, University Health Network, Toronto, ON, Canada.
  • Schaller SJ; Technical University Munich, School of Medicine, Klinikum Rechts der Isar, Department of Anaesthesiology & Intensive Care Medicine, Munich, Germany.
Crit Care ; 27(1): 1, 2023 01 03.
Article em En | MEDLINE | ID: mdl-36597110
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Estado Terminal Tipo de estudo: Guideline / Observational_studies / Prognostic_studies Limite: Humans / Middle aged Idioma: En Revista: Crit Care Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Alemanha
Texto completo
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Estado Terminal Tipo de estudo: Guideline / Observational_studies / Prognostic_studies Limite: Humans / Middle aged Idioma: En Revista: Crit Care Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Alemanha