Towards clinically interpretable machine learning in emergency surgery: feature importance and insights across clinical time points in abdominal pain cases
- DOI
- 10.1007/s00423-026-04098-z
- Published
- 2026-06-16
- Container
- Langenbeck's Archives of Surgery
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1007/s00423-026-04098-z,
title = {Towards clinically interpretable machine learning in emergency surgery: feature importance and insights across clinical time points in abdominal pain cases},
author = {Jonas Henn and Simon Hatterscheidt and Svetozar Nesic and Sebastian Nowak and Wolfgang Block and Johannes Röttgen and Ingo Gräff and Jörg C. Kalff and Alois M. Sprinkart and Andreas Buness and Hanno Matthaei},
year = {2026},
journal = {Langenbeck's Archives of Surgery},
doi = {10.1007/s00423-026-04098-z},
url = {https://doi.org/10.1007/s00423-026-04098-z}
}RIS
TY - JOUR TI - Towards clinically interpretable machine learning in emergency surgery: feature importance and insights across clinical time points in abdominal pain cases AU - Jonas Henn AU - Simon Hatterscheidt AU - Svetozar Nesic AU - Sebastian Nowak AU - Wolfgang Block AU - Johannes Röttgen AU - Ingo Gräff AU - Jörg C. Kalff AU - Alois M. Sprinkart AU - Andreas Buness AU - Hanno Matthaei PY - 2026 JO - Langenbeck's Archives of Surgery DO - 10.1007/s00423-026-04098-z UR - https://doi.org/10.1007/s00423-026-04098-z ER -
APA
Henn, J., Hatterscheidt, S., Nesic, S., Nowak, S., Block, W., Röttgen, J., Gräff, I., Kalff, J. C., Sprinkart, A. M., Buness, A., & Matthaei, H. (2026). Towards clinically interpretable machine learning in emergency surgery: feature importance and insights across clinical time points in abdominal pain cases. Langenbeck's Archives of Surgery. https://doi.org/10.1007/s00423-026-04098-z
Source records
- crossref · retrieved 2026-09-25T11:52:23.915Z