Machine and Deep Learning Models for Hypoxemia Severity Triage in CBRNE Emergencies

Machine and Deep Learning Models for Hypoxemia Severity Triage in CBRNE Emergencies

Background/Objectives: This study develops machine learning (ML) models to predict hypoxemia severity during emergency triage, particularly in Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) scenarios, using physiological data from medical-grade sensors. Methods: Tree-based models...

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Bibliographic Details
Main Authors: Santino Nanini, Mariem Abid, Yassir Mamouni, Arnaud Wiedemann, Philippe Jouvet, Stephane Bourassa
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Diagnostics
Subjects:
hypoxemia
machine learning
patient triage
disaster management
CBRNE events
VIMY Multi-System
Online Access:https://www.mdpi.com/2075-4418/14/23/2763
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https://www.mdpi.com/2075-4418/14/23/2763

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