Predicting the need for intubation within 3 h in the neonatal intensive care unit using a multimodal deep neural network.

ABSTRACT: Respiratory distress is a common chief complaint in neonates admitted to the neonatal intensive care unit. Despite the increasing use of non-invasive ventilation in neonates with respiratory difficulty, some of them require advanced airway support. Delayed intubation is associated with increased morbidity, particularly in urgent unplanned cases. Early and accurate prediction of the need for intubation may provide more time for preparation and increase safety margins by avoiding the late intubation at high-risk infants. This study aimed to predict the need for intubation within 3 h in neonates initially managed with non-invasive ventilation for respiratory distress during the first 48 h of life using a multimodal deep neural network. We developed a multimodal deep neural network model to si

SUBMITTER: Im JE 

PROVIDER: S-EPMC10106895 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature