Development and validation of a machine learning model for real-time prediction of invasive mechanical ventilation weaning readiness.

Journal: Journal of critical care
Published Date: May 26, 2025

Abstract

PURPOSE: To develop and validate a bedside machine learning (ML) decision support tool for prediction of invasive mechanical ventilation (IMV) weaning readiness.

Authors

  • Simone Zappalà
    U-Care Medical srl, Corso Castelfidardo 30A, 10129, Turin, Italy.
  • Vittorio Scaravilli
    Department of Anesthesia and Critical Care, Fondazione IRCCS Ca' Granda - Ospedale Maggiore Policlinico, Milan, Italy; Department of Biomedical Surgical and Dental Sciences, University of Milan, Milan, Italy. Electronic address: vittorio.scaravilli@unimi.it.
  • Lucrezia Rovati
    Department of Emergency Medicine, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy; School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy.
  • Marco Bosone
    School of Medicine and Surgery, University of Milan, Milan, Italy; Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada.
  • Francesca Alfieri
    Department of Applied Science and Technology, Politecnico Di Torino, C.so Duca degli Abruzzi 24, 10129, Turin, Italy.
  • Andrea Ancona
    Department of Applied Science and Technology, Politecnico Di Torino, C.so Duca degli Abruzzi 24, 10129, Turin, Italy.
  • Giacomo Grasselli
    Department of Anesthesia and Critical Care, Fondazione IRCCS Ca' Granda - Ospedale Maggiore Policlinico, Milan, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.

Keywords

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