Predicting 2-year neurodevelopmental outcomes in extremely preterm infants using graphical network and machine learning approaches.

ABSTRACT:

Background

Infants born extremely preterm (<28 weeks' gestation) are at high risk of neurodevelopmental impairment (NDI) with 50% of survivors showing moderate or severe NDI when at 2 years of age. We sought to develop novel models by which to predict neurodevelopmental outcomes, hypothesizing that combining baseline characteristics at birth with medical care and environmental exposures would produce the most accurate model.

Methods

Using a prospective database of 692 infants from the Preterm Epo Neuroprotection (PENUT) Trial, which was carried out between December 2013 and September 2016, we developed three predictive algorithms of increasing complexity using a Bayesian Additive Regression Trees (BART) machine learning approach to predict both NDI and continuous Bayley Scal

SUBMITTER: Juul SE 

PROVIDER: S-EPMC9813758 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature