Non-Invasive Ventilation Failure in Pediatric ICU: A Machine Learning Driven Prediction

Non-Invasive Ventilation Failure in Pediatric ICU: A Machine Learning Driven Prediction

<b>Background/Objectives</b>: Non-invasive ventilation (NIV) has emerged as a possible first-step treatment to avoid invasive intubation in pediatric intensive care units (PICUs) due to its advantages in reducing intubation-associated risks. However, the timely identification of NIV fail...

Full description

Saved in:
Bibliographic Details
Main Authors: Maria Vittoria Chiaruttini, Giulia Lorenzoni, Marco Daverio, Luca Marchetto, Francesca Izzo, Giovanna Chidini, Enzo Picconi, Claudio Nettuno, Elisa Zanonato, Raffaella Sagredini, Emanuele Rossetti, Maria Cristina Mondardini, Corrado Cecchetti, Pasquale Vitale, Nicola Alaimo, Denise Colosimo, Francesco Sacco, Giulia Genoni, Daniela Perrotta, Camilla Micalizzi, Silvia Moggia, Giosuè Chisari, Immacolata Rulli, Andrea Wolfler, Angela Amigoni, Dario Gregori
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Diagnostics
Subjects:
non-invasive ventilation
NIV
NIV failure
PICU
TIPNet
machine learning
Online Access:https://www.mdpi.com/2075-4418/14/24/2857
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items