Evaluating the SWIFT algorithm's efficacy in predicting hypoxemia across multiple critical care datasets.

Journal: Journal of critical care
Published Date: May 19, 2025

Abstract

BACKGROUND: Machine learning models to predict hypoxia in patients could improve timely interventions. Due to the diversity and limited generalizability of approaches, external validation is required.

Authors

  • Leon Schmidt
    Department of Anesthesiology and operative intensive care medicine, University Hospital of Augsburg, Augsburg, Germany. Electronic address: leon.schmidt@uk-augsburg.de.
  • Lena Pigat
    Digital Medicine, University Hospital of Augsburg, Augsburg, Germany. Electronic address: lena.pigat@uk-augsburg.de.
  • Seyedmostafa Sheikhalishahi
    University of Trento, Trento, Italy.
  • Julia Sander
    Digital Medicine, University Hospital of Augsburg, Augsburg, Germany. Electronic address: julia.sander@uk-augsburg.de.
  • Mathias Kaspar
    University Hospital of Würzburg, Comprehensive Heart Failure Center.
  • Baocheng Wang
    Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of Sciences Beijing China.
  • Ludwig Christian Hinske
    Institute for Digital Medicine, University Hospital Augsburg, Augsburg, Germany.

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