Understanding deep learning models for Length of Stay prediction on critically ill patients through latent space visualization.

Journal: Computer methods and programs in biomedicine
Published Date: May 19, 2025

Abstract

BACKGROUND AND OBJECTIVE: Continuous, real-time monitoring of Length of Stay (LoS) for critically ill patients in Intensive Care Units (ICUs) is essential for anticipating patient needs, reduce the risk of adverse events, optimize resource allocation, plan incoming patients, and improve overall care. While previous research has focused primarily on predicting LoS, less attention has been given to how these prediction systems can be used by non-machine learning experts in real hospital environments for capacity planning.

Authors

  • Lyse Naomi Wamba Momo
    KU Leuven, Department of Electrical Engineering (ESAT), Stadius Center for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium.
  • Vincent Scheltjens
    Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Kasteelpark Arenberg 10, Leuven, 3001, Belgium. vincent.scheltjens@kuleuven.be.
  • Wouter Verbeke
    Faculty of Economics and Business, KU Leuven, Naamsestraat 69, Leuven, 3000, Belgium.
  • Frank Rademakers
    Department of Cardiology, KU Leuven, Leuven, Belgium.
  • Bart De Moor

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