Predicting Extubation Readiness in Preterm Infants Utilizing Machine Learning: A Diagnostic Utility Study-Reference-Cited by-同舟云学术

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Predicting Extubation Readiness in Preterm Infants Utilizing Machine Learning: A Diagnostic Utility Study

Published:2024-08 Issue: Volume:271 Page:114043
ISSN:0022-3476
Container-title:The Journal of Pediatrics
language:en
Short-container-title:The Journal of Pediatrics

Author:

Brasher Mandy^ORCID,Virodov Alexandr,Raffay Thomas M.,Bada Henrietta S.,Cunningham M. Douglas,Bumgardner Cody,Abu Jawdeh Elie G.

Publisher

Elsevier BV

Reference26 articles.

1. Extremely low birthweight neonates with protracted ventilation: mortality and 18-month neurodevelopmental outcomes;Walsh;J Pediatr,2005

2. Pulmonary complications of mechanical ventilation in neonates;Miller;Clin Perinatol,2008

3. Effects of multiple ventilation courses and duration of mechanical ventilation on respiratory outcomes in extremely low-birth-weight infants;Jensen;JAMA Pediatr,2015

4. Impact of prolonged mechanical ventilation in very low birth weight infants: results from a national cohort study;Choi;J Pediatr,2018

5. Extubating extremely preterm infants: predictors of success and outcomes following failure;Manley;J Pediatr,2016

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