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Surgomics: personalized prediction of morbidity, mortality and long-term outcome in surgery using machine learning on multimodal data
24
Zitationen
24
Autoren
2022
Jahr
Abstract
Surgomics is a promising concept for the analysis of intraoperative data. Surgomics may be used together with preoperative features from clinical data and Radiomics to predict postoperative morbidity, mortality and long-term outcome, as well as to provide tailored feedback for surgeons.
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Autoren
- Martin Wagner
- Johanna M. Brandenburg
- Sebastian Bodenstedt
- A. Schulze
- Alexander C. Jenke
- Antonia Stern
- Marie T. J. Daum
- Lars Mündermann
- Fiona R. Kolbinger
- Nithya Bhasker
- Gerd Schneider
- Grit Krause-Jüttler
- Hisham Alwanni
- Fleur Fritz
- Oliver Burgert
- Dirk Wilhelm
- Johannes Fallert
- Felix Nickel
- Lena Maier‐Hein
- Martin Dugas
- Marius Distler
- Jürgen Weitz
- Beat P. Müller‐Stich
- Stefanie Speidel
Institutionen
- University Hospital Heidelberg(DE)
- Heidelberg University(DE)
- National Center for Tumor Diseases(DE)
- TU Dresden(DE)
- Karl Storz (Germany)(DE)
- University Hospital Carl Gustav Carus(DE)
- Fresenius (Germany)(DE)
- Reutlingen University(DE)
- Klinikum rechts der Isar(DE)
- Technical University of Munich(DE)
- German Cancer Research Center(DE)
- Helmholtz-Zentrum Dresden-Rossendorf(DE)