Machine learning to predict late respiratory support in preterm infants: a retrospective cohort study.

ABSTRACT: Bronchopulmonary dysplasia (BPD) has been a critical morbidity in preterm infants. To improve our definition and prediction of BPD is challenging yet indispensable. We aimed to apply machine learning (ML) to investigate effective models by using the recently-proposed and data-driven definition to predict late respiratory support modalities at 36 weeks' post menstrual age (PMA). We collected data on very-low-birth-weight infants born between 2016 and 2019 from the Taiwan Neonatal Network database. Twenty-four attributes associated with their early life and seven ML algorithms were used in our analysis. The target outcomes were overall mortality, death before 36 weeks' PMA, and severity of BPD under the new definition, which served as a proxy for respiratory support modalities. Of the 4103 i

SUBMITTER: Wu TY 

PROVIDER: S-EPMC9938227 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature