SBC-SHAP: Increasing the Accessibility and Interpretability of Machine Learning Algorithms for Sepsis Prediction.

Journal: The journal of applied laboratory medicine
Published Date: Jul 11, 2025

Abstract

BACKGROUND: Sepsis is a life-threatening condition that is one of the major causes of death worldwide. Early detection of sepsis is required for fast initialization of an appropriate therapy. Complete blood count data containing information about white blood cells, platelets, hemoglobin, red blood cells, and mean corpuscular volume could serve as early indicators. However, previous approaches are limited by their interpretability (i.e., investigating the influence of feature values on individual predictions) and accessibility (i.e., easy accessibility for clinicians without programming expertise).

Authors

  • Daniel Walke
    Bioprocess Engineering, Otto von Guericke University, Universitätsplatz 2, Magdeburg, Germany.
  • Daniel Steinbach
    Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, Leipzig University Hospital, Leipzig, Germany.
  • Thorsten Kaiser
    Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, 04103 Leipzig, Germany.
  • Alexander Schönhuth
    Centrum Wiskunde & Informatica, Life Sciences & Health, XG Amsterdam, The Netherlands.
  • Gunter Saake
    Databases and Software Engineering, Otto-von-Guericke-University, Magdeburg, Germany.
  • David Broneske
    German Centre for Higher Education Research and Science Studies, Hannover, Germany.
  • Robert Heyer
    Multidimensional Omics Analyses group, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Bunsen-Kirchhoff-Straße 11, Dortmund, Germany.

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