Machine learning-derived prediction of in-hospital mortality in patients with severe acute respiratory infection: analysis of claims data from the German-wide Helios hospital network.

Journal: Respiratory research
Published Date:

Abstract

BACKGROUND: Severe acute respiratory infections (SARI) are the most common infectious causes of death. Previous work regarding mortality prediction models for SARI using machine learning (ML) algorithms that can be useful for both individual risk stratification and quality of care assessment is scarce. We aimed to develop reliable models for mortality prediction in SARI patients utilizing ML algorithms and compare its performances with a classic regression analysis approach.

Authors

  • Johannes Leiner
    Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany.
  • Vincent Pellissier
    Helios Health Institute, Real World Evidence & Health Technology Assessment, Schwanebecker Chaussee 50, 13125 Berlin, Germany.
  • Sebastian König
    Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany.
  • Sven Hohenstein
    Helios Health Institute, Real World Evidence & Health Technology Assessment, Schwanebecker Chaussee 50, 13125 Berlin, Germany.
  • Laura Ueberham
    Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Strümpellstraße 39, Leipzig, 04289, Germany.
  • Irit Nachtigall
    Department of Infectious Diseases and Infection Prevention, Helios Hospital Emil-von-Behring, Berlin, Germany.
  • Andreas Meier-Hellmann
    Helios Hospitals, Berlin, Germany.
  • Ralf Kuhlen
    Helios Health, Berlin, Germany.
  • Gerhard Hindricks
    Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany.
  • Andreas Bollmann
    Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany.