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Machine Learning for Extubation Success by eyob mulugeta is a document available to read on EtoBox.

What is Machine Learning for Extubation Success about?

This study evaluates the predictive capacity of five machine learning techniques to estimate extubation success in critically ill patients using data from the COBRE-US trial. The results indicate that decision trees and artificial neural networks provide the best diagnostic performance, achieving up to 95.7% accuracy in predicting extubation success. These findings suggest that machine learning models can effectively classify patients likely to succeed in spontaneous breathing trials and extubation, potenti

Author

eyob mulugeta

Language

EN