Additional file 6 of Diagnosing capillary leak in critically ill patients: development of an innovative scoring instrument for non-invasive detection

Additional file 6 of Diagnosing capillary leak in critically ill patients: development of an innovative scoring instrument for non-invasive detection

Additional file 6: Figure S2. (A) 27-parameter machine learning models to predict CLS. (B) Serology-only machine-learning prediction of CLS.

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Bibliographische Detailangaben
Hauptverfasser: Wollborn, Jakob, Hassenzahl, Lars O., Reker, Daniel, Staehle, Hans Felix, Omlor, Anne Marie, Baar, Wolfgang, Kaufmann, Kai B., Ulbrich, Felix, Wunder, Christian, Utzolino, Stefan, Buerkle, Hartmut, Kalbhenn, Johannes, Heinrich, Sebastian, Goebel, Ulrich
Format: Bild
Sprache:eng
Schlagworte:
Biological Sciences not elsewhere classified
Cancer
FOS: Biological sciences
FOS: Health sciences
Medicine
Mental Health
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Zusammenfassung:Additional file 6: Figure S2. (A) 27-parameter machine learning models to predict CLS. (B) Serology-only machine-learning prediction of CLS.
DOI:10.6084/m9.figshare.17210575