[Artificial Intelligence in internal medicine : development of a model predicting length of stay for non-elective admissions].

Journal: Revue medicale suisse
Published Date:

Abstract

Efficient management of hospitalized patients requires carefully planning each stay by taking into account patients' pathologies and hospital constraints. Therefore, the ability to accurately estimate length of stays allows for better interprofessional tasks coordination, improved patient flow management, and anticipated discharge preparation. This article presents how we built and evaluated a predictive model of length of stay based on clinical data available upon admission to a division of internal medicine. We show that Machine Learning-based approaches can predict lengths of stay with a similar level of accuracy as field experts.

Authors

  • Jérémie Despraz
    Groupe Data Science, Direction des systèmes d'information, Département infrastructures, Centre hospitalier universitaire vaudois, 1011 Lausanne.
  • Antoine Garnier
    Service de médecine interne, Département de médecine, Centre hospitalier universitaire vaudois, 1011 Lausanne.
  • Marie Méan
    Service de médecine interne, Département de médecine, Centre hospitalier universitaire vaudois, 1011 Lausanne.
  • Julien Vaucher
    Service de médecine interne, Département de médecine, Centre hospitalier universitaire vaudois, 1011 Lausanne.
  • Vanessa Kraege
    Service de médecine interne, Département de médecine, Centre hospitalier universitaire vaudois, 1011 Lausanne.
  • Peter Vollenweider
    Service de médecine interne, Département de médecine, Centre hospitalier universitaire vaudois, 1011 Lausanne.