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 Jun 16
- Container
- Langenbeck's archives of surgery
- Publisher
- Not recorded
- Open access
- yes
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limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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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 = {Henn J and Hatterscheidt S and Nesic S and Nowak S and Block W and Röttgen J and Gräff I and Kalff JC and Sprinkart AM and Buness A and Matthaei H},
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 - Henn J AU - Hatterscheidt S AU - Nesic S AU - Nowak S AU - Block W AU - Röttgen J AU - Gräff I AU - Kalff JC AU - Sprinkart AM AU - Buness A AU - Matthaei H 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
J, H., S, H., S, N., S, N., W, B., J, R., I, G., JC, K., AM, S., A, B., & H, M. (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
- pubmed · retrieved 2026-11-10T12:14:53.690Z