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Surgomics: personalized prediction of morbidity, mortality and long-term outcome in surgery using machine learning on multimodal data

Crossref DOI link: https://doi.org/10.1007/s00464-022-09611-1

Published Online: 2022-09-28

Published Print: 2022-11

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Wagner, Martin https://orcid.org/0000-0002-9831-9110
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
Funding

Funding for this research was provided by:

Bundesministerium für Gesundheit (2520DAT82)

Deutsche Forschungsgemeinschaft (390696704)

Else Kröner-Fresenius-Stiftung (Else-Kröner-Fresenius-Center for Digital Health)

Universitätsklinikum Heidelberg
License Information

Text and Data Mining valid from 2022-09-28

Version of Record valid from 2022-09-28
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Article History

Received: 10 May 2022

Accepted: 3 September 2022

First Online: 28 September 2022

Declarations

:

: A. Stern, H. Alwanni, L. Mündermann, and J. Fallert are employees of KARL STORZ SE & Co. KG. M. Wagner, S. Bodenstedt, F. Fritz-Kebede, M. Dugas, M. Distler, J. Weitz, B. Müller-Stich and S. Speidel are project leaders of the Surgomics-project, funded by the German Federal Ministry of Health (Grant Number 2520DAT82) with medical device manufacturer KARL STORZ SE & Co. KG being a project partner. J. M. Brandenburg, A. Schulze, A. C. Jenke, M.T.J. Daum, F. R. Kolbinger, N. Bhasker, G. Schneider, G. Krause-Jüttler, O. Burgert, D. Wilhelm, F. Nickel, and L. Maier-Hein have no conflicts of interest or financial ties to disclose.

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