Machine learning predicts mortality based on analysis of ventilation parameters of critically ill patients: multi-centre validation

Machine learning predicts mortality based on analysis of ventilation parameters of critically ill patients: multi-centre validation

Abstract Background Mechanical Ventilation (MV) is a complex and central treatment process in the care of critically ill patients. It influences acid–base balance and can also cause prognostically relevant biotrauma by generating forces and liberating reactive oxygen species, negatively affecting ou...

Full description

Bibliographic Details
Main Authors: Behrooz Mamandipoor, Fernando Frutos-Vivar, Oscar Peñuelas, Richard Rezar, Konstantinos Raymondos, Alfonso Muriel, Bin Du, Arnaud W. Thille, Fernando Ríos, Marco González, Lorenzo del-Sorbo, Maria del Carmen Marín, Bruno Valle Pinheiro, Marco Antonio Soares, Nicolas Nin, Salvatore M. Maggiore, Andrew Bersten, Malte Kelm, Raphael Romano Bruno, Pravin Amin, Nahit Cakar, Gee Young Suh, Fekri Abroug, Manuel Jibaja, Dimitros Matamis, Amine Ali Zeggwagh, Yuda Sutherasan, Antonio Anzueto, Bernhard Wernly, Andrés Esteban, Christian Jung, Venet Osmani
Format: Article
Language:English
Published: BMC 2021-05-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Critical care medicine
Machine learning
ICU
Risk stratification
Mechanical ventilation
Online Access:https://doi.org/10.1186/s12911-021-01506-w

Internet

https://doi.org/10.1186/s12911-021-01506-w

Similar Items