Early Prediction of Mechanical Ventilation Needs in Very Preterm Neonates Using Machine Learning.

ABSTRACT:

Background

The use of invasive mechanical ventilation (IMV) increases morbidity in patients but may be required for survival on a subset of patients. Delayed, as well as unnecessary, initiation of IMV increases morbidity and mortality among preterm neonates.

Objective

To assess the use of machine learning in predicting need for IMV among patients on lower levels of respiratory support (CPAP failure) using the earliest clinical data captured in the electronic health records across multiple health systems in the United States. Accurate predictions may enable earlier proactive intervention to minimize CPAP failure and improve clinical outcomes.

Methods

This study was conducted using the Oracle EHR Real-World Data (OERWD) database including preterm neonatal intensive care

SUBMITTER: Gates Q 

PROVIDER: S-EPMC12305751 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature