Predicting emergency department admissions using a machine-learning algorithm: a proof of concept with retrospective study.

Journal: BMC emergency medicine
PMID: 39762754

Abstract

INTRODUCTION: Overcrowding in emergency departments (ED) is a major public health issue, leading to increased workload and exhaustion for the teams, resulting poor outcomes. It seems interesting to be able to predict the admissions of patients in the ED.

Authors

  • Cyrielle Brossard
    Emergency department, CHR Metz-Thionville, Metz, 57000, France.
  • Christophe Goetz
    Clinical Research Support Unit, CHR Metz-Thionville, Metz, 57000, France.
  • Pierre Catoire
    Emergency department, CHU Bordeaux, Bordeaux, France.
  • Lauriane Cipolat
    Emergency department, CHR Metz-Thionville, Metz, 57000, France.
  • Christophe Guyeux
    Institut Femto-ST, UMR 6174 CNRS, Université de Bourgogne Franche-Comté, Dijon, France.
  • Cédric Gil Jardine
    Emergency department, CHU Bordeaux, Bordeaux, France.
  • Mahuna Akplogan
    Extome, Research & Development Team, Paris, 75008, France.
  • Laure Abensur Vuillaume
    Emergency department, CHR Metz-Thionville, Metz, 57000, France. laure.abensur-vuillaume@chr-metz-thionville.fr.

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