The development of a C5.0 machine learning model in a limited data set to predict early mortality in patients with ARDS undergoing an initial session of prone positioning

The development of a C5.0 machine learning model in a limited data set to predict early mortality in patients with ARDS undergoing an initial session of prone positioning

Abstract Background Acute Respiratory Distress Syndrome (ARDS) has a high morbidity and mortality. One therapy that can decrease mortality is ventilation in the prone position (PP). Patients undergoing PP are amongst the sickest, and there is a need for early identification of patients at particular...

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Bibliographic Details
Main Authors: David M. Hannon, Jaffar David Abbas Syed, Bairbre McNicholas, Michael Madden, John G. Laffey
Format: Article
Language:English
Published: SpringerOpen 2024-11-01
Series:Intensive Care Medicine Experimental
Subjects:
ARDS
Prone positioning
Machine learning
Critical care
Mechanical ventilation
Online Access:https://doi.org/10.1186/s40635-024-00682-z
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