Machine ​learning algorithms for claims data-based prediction of in-hospital mortality in patients with heart failure.

Journal: ESC heart failure
Published Date: Jun 4, 2021

Abstract

AIMS: Models predicting mortality in heart failure (HF) patients are often limited with regard to performance and applicability. The aim of this study was to develop a reliable algorithm to compute expected in-hospital mortality rates in HF cohorts on a population level based on administrative data comparing regression analysis with different machine learning (ML) models.

Authors

  • Sebastian König
    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.
  • Sven Hohenstein
    Helios Health Institute, Real World Evidence & Health Technology Assessment, Schwanebecker Chaussee 50, 13125 Berlin, Germany.
  • Andres Bernal
    Leipzig Heart Institute, Leipzig, Germany.
  • Laura Ueberham
    Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Strümpellstraße 39, Leipzig, 04289, 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.

Keywords

External Resources

Popular Topics
Recent Journals