Surgomics: personalized prediction of morbidity, mortality and long-term outcome in surgery using machine learning on multimodal data
Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Wagner, Martin ; Brandenburg, Johanna M. ; Bodenstedt, Sebastian ; Schulze, André ; Jenke, Alexander C. ; Stern, Antonia ; Daum, Marie T. J. ; Mündermann, Lars ; Kolbinger, Fiona R. ; Bhasker, Nithya ; Schneider, Gerd ; Krause-Jüttler, Grit ; Alwanni, Hisham ; Fritz-Kebede, Fleur ; Burgert, Oliver ; Wilhelm, Dirk ; Fallert, Johannes ; Nickel, Felix ; Maier-Hein, Lena ; Dugas, Martin ; Distler, Marius ; Weitz, Jürgen ; Müller-Stich, Beat-Peter ; Speidel, Stefanie
Stichworte:
2022 EAES Oral ; Artificial intelligence ; Minimally invasive surgery ; Radiomics ; Prediction model ; Surgical data science ; Precision medicine ; Information and Computing Sciences ; Medical and Health Sciences
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