A Novel Machine Learning-Based Point-Score Model as a Non-Invasive Decision-Making Tool for Identifying Infected Ascites in Patients with Hydropic Decompensated Liver Cirrhosis: A Retrospective Multicentre Study

Benutzer: Gast

Dokumenttyp:: Zeitschriftenaufsatz
Autor(en):: Würstle, Silvia ; Hapfelmeier, Alexander ; Karapetyan, Siranush ; Studen, Fabian ; Isaakidou, Andriana ; Schneider, Tillman ; Schmid, Roland M. ; von Delius, Stefan ; Gundling, Felix ; Triebelhorn, Julian ; Burgkart, Rainer ; Obermeier, Andreas ; Mayr, Ulrich ; Heller, Stephan ; Rasch, Sebastian ; Lahmer, Tobias ; Geisler, Fabian ; Chan, Benjamin ; Turner, Paul E. ; Rothe, Kathrin ; Spinner, Christoph D. ; Schneider, Jochen
Titel:: A Novel Machine Learning-Based Point-Score Model as a Non-Invasive Decision-Making Tool for Identifying Infected Ascites in Patients with Hydropic Decompensated Liver Cirrhosis: A Retrospective Multicentre Study
Stichworte:: Article ; ascites ; liver cirrhosis ; proton pump inhibitor ; spontaneous bacterial peritonitis ; secondary peritonitis
Zeitschriftentitel:: Antibiotics
Jahr:: 2022
Band / Volume:: 11
Heft / Issue:: 11
Volltext / DOI:: doi:10.3390/antibiotics11111610
Verlag / Institution:: MDPI
E-ISSN:: 2079-6382
Publikationsdatum:: 12.11.2022
CC-Lizenz:: by, https://creativecommons.org/licenses/by/4.0

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