Machine Learning for Prediction of Successful Extubation of Mechanical Ventilated Patients in an Intensive Care Unit: A Retrospective Observational Study
Author:
Affiliation:
1. Department of Emergency and Critical Care Medicine, Nippon Medical School
2. Department of Industrial Administration, Tokyo University of Science
Publisher
Medical Association of Nippon Medical School
Subject
General Medicine
Link
https://www.jstage.jst.go.jp/article/jnms/88/5/88_JNMS.2021_88-508/_pdf
Reference44 articles.
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2. 2. Rady MY, Ryan T. Perioperative predictors of extubation failure and the effect on clinical outcome after cardiac surgery. Crit Care Med [Internet]. 1999 Feb;27(2):340-7. Available from: https://www.ncbi.nlm.nih.gov/pubmed/10075059
3. 3. Epstein SK, Ciubotaru RL. Independent effects of etiology of failure and time to reintubation on outcome for patients failing extubation. Am J Respir Crit Care Med [Internet]. 1998 Aug;158(2):489-93. Available from: https://www.ncbi.nlm.nih.gov/pubmed/9700126
4. 4. Esteban A, Frutos-Vivar F, Ferguson ND, et al. Noninvasive positive-pressure ventilation for respiratory failure after extubation. N Engl J Med [Internet]. 2004 Jun 10;350(24):2452-60. Available from: https://www.ncbi.nlm.nih.gov/pubmed/15190137
5. 5. Epstein SK, Ciubotaru RL, Wong JB. Effect of failed extubation on the outcome of mechanical ventilation. Chest [Internet]. 1997 Jul;112(1):186-92. Available from: https://www.ncbi.nlm.nih.gov/pubmed/9228375
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