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Machine learning identifies ICU outcome predictors in a mul..:
Harry Magunia
;
Simone Lederer
;
Raphael Verbuecheln
...
https://doi.org/10.1186/s13054-021-03720-4. , 2021
Link:
https://doi.org/10.1186/s13054-021-03720-4
RT Journal T1
Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort
UL https://suche.suub.uni-bremen.de/peid=base-ftdoajarticles:oai:doaj.org_article:27cef1129b254e818eeafb68b5dd77b1&Exemplar=1&LAN=DE A1 Harry Magunia A1 Simone Lederer A1 Raphael Verbuecheln A1 Bryant Joseph Gilot A1 Michael Koeppen A1 Helene A. Haeberle A1 Valbona Mirakaj A1 Pascal Hofmann A1 Gernot Marx A1 Johannes Bickenbach A1 Boris Nohe A1 Michael Lay A1 Claudia Spies A1 Andreas Edel A1 Fridtjof Schiefenhövel A1 Tim Rahmel A1 Christian Putensen A1 Timur Sellmann A1 Thea Koch A1 Timo Brandenburger A1 Detlef Kindgen-Milles A1 Thorsten Brenner A1 Marc Berger A1 Kai Zacharowski A1 Elisabeth Adam A1 Matthias Posch A1 Onnen Moerer A1 Christian S. Scheer A1 Daniel Sedding A1 Markus A. Weigand A1 Falk Fichtner A1 Carla Nau A1 Florian Prätsch A1 Thomas Wiesmann A1 Christian Koch A1 Gerhard Schneider A1 Tobias Lahmer A1 Andreas Straub A1 Andreas Meiser A1 Manfred Weiss A1 Bettina Jungwirth A1 Frank Wappler A1 Patrick Meybohm A1 Johannes Herrmann A1 Nisar Malek A1 Oliver Kohlbacher A1 Stephanie Biergans A1 Peter Rosenberger PB BMC YR 2021 K1 COVID-19 K1 Critical care K1 ARDS K1 Outcome K1 Prognostic models K1 Medical emergencies. Critical care. Intensive care. First aid K1 RC86-88.9 JF https://doi.org/10.1186/s13054-021-03720-4 LK http://dx.doi.org/https://doi.org/10.1186/s13054-021-03720-4 DO https://doi.org/10.1186/s13054-021-03720-4 SF ELIB - SuUB Bremen
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