Knowledge Uncertainty Estimation for Reliable Clinical Decision Support: A Delirium Risk Prognosis Case Study.

Journal: Studies in health technology and informatics
PMID: 40270416

Abstract

INTRODUCTION: Predictive models hold significant potential in healthcare, but their adoption in clinical settings is hampered by limited trust due to their inability to recognize when presented with unfamiliar data. Estimating knowledge uncertainty (KU) can mitigate this issue. This study aims to assess the capabilities of two targeted approaches, specifically Ensemble Neural Networks (ENN) and Spectral Normalized Neural Gaussian Processes (SNGP), in quantifying KU and detecting out-of-distribution (OoD) data within the context of delirium risk prediction.

Authors

  • Adrian Lindenmeyer
    Innovation Center Computer Assisted Surgery (ICCAS), Leipzig University, Leipzig, Germany.
  • Sai Veeranki
    AIT Austrian Institute of Technology.
  • Stefan Franke
    c Innovation Center Computer Assisted Surgery (ICCAS) , University of Leipzig , Leipzig , Germany.
  • Thomas Neumuth
    Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, Leipzig, Germany.
  • Diether Kramer
    Steiermärkische Krankenanstaltengesellschaft m.b.H. (KAGes), Graz, Austria.
  • Daniel Schneider
    Diagnostic and Interventional Radiology, University Hospital Ulm, Germany.

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