Comparative analysis of explainable machine learning prediction models for hospital mortality.

Journal: BMC medical research methodology
Published Date: Feb 27, 2022

Abstract

BACKGROUND: Machine learning (ML) holds the promise of becoming an essential tool for utilising the increasing amount of clinical data available for analysis and clinical decision support. However, the lack of trust in the models has limited the acceptance of this technology in healthcare. This mistrust is often credited to the shortage of model explainability and interpretability, where the relationship between the input and output of the models is unclear. Improving trust requires the development of more transparent ML methods.

Authors

  • Eline Stenwig
    Department of Circulation and Medical Imaging, The Norwegian University of Science and Technology, Trondheim, Norway. eline.stenwig@ntnu.no.
  • Giampiero Salvi
    Department of Electronic Systems, The Norwegian University of Science and Technology, Trondheim, Norway.
  • Pierluigi Salvo Rossi
    Department of Electronic Systems, The Norwegian University of Science and Technology, Trondheim, Norway.
  • Nils Kristian Skjærvold
    Department of Circulation and Medical Imaging, The Norwegian University of Science and Technology, Trondheim, Norway.

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