Predicting vital sign deviations during surgery from patient monitoring data: developing and validating single-stream deep learning models.

Dubatovka, Alina; Nöthiger, Christoph B; Spahn, Donat R; Buhmann, Joachim M; Roche, Tadzio R; Tscholl, David W

Dubatovka, Alina; Nöthiger, Christoph B; Spahn, Donat R; Buhmann, Joachim M; Roche, Tadzio R; Tscholl, David W.

Afiliação

Dubatovka A; Department of Computer Science, ETH Zurich, Zurich, Switzerland.
Nöthiger CB; Institute of Anaesthesiology, University of Zurich, University Hospital Zurich, Zurich, Switzerland.
Spahn DR; Institute of Anaesthesiology, University of Zurich, University Hospital Zurich, Zurich, Switzerland.
Buhmann JM; Department of Computer Science, ETH Zurich, Zurich, Switzerland.
Roche TR; Institute of Anaesthesiology, University of Zurich, University Hospital Zurich, Zurich, Switzerland. Electronic address: Tadzioraoul.roche@usz.ch.
Tscholl DW; Institute of Anaesthesiology, University of Zurich, University Hospital Zurich, Zurich, Switzerland.

Br J Anaesth ; 133(4): 889-892, 2024 Oct.

Article em En | MEDLINE | ID: mdl-39089955

Assuntos

Aprendizado Profundo; Monitorização Intraoperatória; Sinais Vitais; Humanos; Monitorização Intraoperatória/métodos; Reprodutibilidade dos Testes

Palavras-chave

algorithms; artificial intelligence; biosignals; clinical decision support systems; digital medicine; machine learning; perioperative medicine, vital signs

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article