> Details
Magunia, Harry
[Author];
Lederer, Simone
[Author];
Verbuecheln, Raphael
[Author];
Gilot, Bryant
[Author];
Koeppen, Michael
[Author];
Häberle, Helene
[Author];
Mirakaj, Valbona
[Author];
Hofmann, Pascal
[Author];
Marx, Gernot
[Author];
Bickenbach, Johannes
[Author];
Nohe, Boris Alexander
[Author];
Lay, Michael
[Author];
Spies, Claudia D.
[Author];
Edel, Andreas
[Author];
Schiefenhövel, Fridtjof
[Author];
Rahmel, Tim
[Author];
Putensen, Christian
[Author];
Sellmann, Timur
[Author];
Koch, Thea
[Author];
Brandenburger, Timo
[Author];
Kindgen-Milles, Detlef
[Author];
Brenner, Thorsten
[Author];
Berger, Marc
[Author];
Zacharowski, Kai
[Author];
[...]
Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort
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- Media type: E-Article
- Title: Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort
- Contributor: Magunia, Harry [Author]; Lederer, Simone [Author]; Verbuecheln, Raphael [Author]; Gilot, Bryant [Author]; Koeppen, Michael [Author]; Häberle, Helene [Author]; Mirakaj, Valbona [Author]; Hofmann, Pascal [Author]; Marx, Gernot [Author]; Bickenbach, Johannes [Author]; Nohe, Boris Alexander [Author]; Lay, Michael [Author]; Spies, Claudia D. [Author]; Edel, Andreas [Author]; Schiefenhövel, Fridtjof [Author]; Rahmel, Tim [Author]; Putensen, Christian [Author]; Sellmann, Timur [Author]; Koch, Thea [Author]; Brandenburger, Timo [Author]; Kindgen-Milles, Detlef [Author]; Brenner, Thorsten [Author]; Berger, Marc [Author]; Zacharowski, Kai [Author]; Adam, Elisabeth [Author]; Posch, Matthias Jakob [Author]; Mörer, Onnen [Author]; Scheer, Christian S. [Author]; Sedding, Daniel [Author]; Weigand, Markus A. [Author]; Fichtner, Falk [Author]; Nau, Carla [Author]; Prätsch, Florian [Author]; Wiesmann, Thomas [Author]; Koch, Christian [Author]; Schneider, Gerhard [Author]; Lahmer, Tobias [Author]; Straub, Andreas [Author]; Meiser, Andreas [Author]; Weiss, Manfred [Author]; Jungwirth, Bettina [Author]; Wappler, Frank [Author]; Meybohm, Patrick [Author]; Herrmann, Johannes Bernd [Author]; Malek, Nisar Peter [Author]; Kohlbacher, Oliver [Author]; Biergans, Stephanie [Author]; Rosenberger, Peter [Author]
-
Published:
AUG 17 2021
- Published in: Critical care ; 25(2021), Artikel-ID 295, Seite 1-14
- Language: English
- DOI: 10.1186/s13054-021-03720-4
- Identifier:
- Keywords: ARDS ; COVID-19 ; Critical care ; Outcome ; Prognostic models
- Origination:
- Footnote:
- Description: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes.
- Access State: Open Access
- Rights information: Attribution (CC BY)