Towards clinically interpretable machine learning in emergency surgery: feature importance and insights across clinical time points in abdominal pain cases - Summary - MDSpire

Towards clinically interpretable machine learning in emergency surgery: feature importance and insights across clinical time points in abdominal pain cases

  • By

  • Jonas Henn

  • Simon Hatterscheidt

  • Svetozar Nesic

  • Sebastian Nowak

  • Wolfgang Block

  • Johannes Röttgen

  • Ingo Gräff

  • Jörg C. Kalff

  • Alois M. Sprinkart

  • Andreas Buness

  • Hanno Matthaei

  • June 16, 2026

  • 0 min

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Objective:

To evaluate the performance of a random-forest classifier for urgent abdominal surgery in acute abdominal pain (AAP) while quantifying the attribution of sequentially available feature sets.

Approach:
    Key Findings:
    • ML models can predict urgent surgery in AAP with high discriminatory performance.
    • Sequentially available information improves model accuracy.
    • Explainability of feature importance at specific time points is crucial for clinical trust.
    Interpretation:

    The study emphasizes the integration of temporal data and explainability in ML models for emergency surgical decision-making.

    Limitations:
    • Retrospective design may introduce bias.
    • Determining the necessity of surgery retrospectively is challenging and subjective.
    Conclusion:

    The study aims to enhance the interpretability of ML models in emergency surgery by linking predictive accuracy to clinically meaningful, timepoint-specific explanations.

    Sources:

Original Source(s)

Towards clinically interpretable machine learning in emergency surgery: feature importance and insights across clinical time points in abdominal pain cases

  • by Jonas Henn, Simon Hatterscheidt, Svetozar Nesic, Sebastian Nowak, Wolfgang Block, Johannes Röttgen, Ingo Gräff, Jörg C. Kalff, Alois M. Sprinkart, Andreas Buness, Hanno Matthaei

  • June 16, 2026

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