A system theory based digital model for predicting the cumulative fluid balance course in intensive care patients.

Polz, Mathias; Bergmoser, Katharina; Horn, Martin; Schörghuber, Michael; Lozanovic, Jasmina; Rienmüller, Theresa; Baumgartner, Christian

Polz, Mathias; Bergmoser, Katharina; Horn, Martin; Schörghuber, Michael; Lozanovic, Jasmina; Rienmüller, Theresa; Baumgartner, Christian.

Afiliación

Polz M; Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz University of Technology, Graz, STM, Austria.
Bergmoser K; Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz University of Technology, Graz, STM, Austria.
Horn M; CBmed Center for Biomarker Research in Medicine, Graz, STM, Austria.
Schörghuber M; Institute of Automation and Control, Graz University of Technology, Graz, STM, Austria.
Lozanovic J; Department of Anesthesiology and Intensive Care Medicine, Medical University of Graz, Graz, STM, Austria.
Rienmüller T; Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz University of Technology, Graz, STM, Austria.
Baumgartner C; Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz University of Technology, Graz, STM, Austria.

Front Physiol ; 14: 1101966, 2023.

Article en En | MEDLINE | ID: mdl-37123264

Palabras clave

decision support; fluid balance; intensive care; prediction; system theory; transfer function model

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Physiol Año: 2023 Tipo del documento: Article País de afiliación: Austria Pais de publicación: Suiza

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