Predictive modelling of hypoxic ischaemic encephalopathy risk following perinatal asphyxia.

ABSTRACT: Hypoxic Ischemic Encephalopathy (HIE) remains a major cause of neurological disability. Early intervention with therapeutic hypothermia improves outcome, but prediction of HIE is difficult and no single clinical marker is reliable. Machine learning algorithms may allow identification of patterns in clinical data to improve prognostic power. Here we examine the use of a Random Forest machine learning algorithm and five-fold cross-validation to predict the occurrence of HIE in a prospective cohort of infants with perinatal asphyxia. Infants with perinatal asphyxia were recruited at birth and neonatal course was followed for the development of HIE. Clinical variables were recorded for each infant including maternal demographics, delivery details and infant's condition at birth. We found that

SUBMITTER: Mooney C 

PROVIDER: S-EPMC8261660 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature