Machine learning models for identifying preterm infants at risk of cerebral hemorrhage.

Machine learning models for identifying preterm infants at risk of cerebral hemorrhage.

Blog Article

Intracerebral hemorrhage in preterm infants is a major cause of brain damage and cerebral palsy.The pathogenesis of cerebral hemorrhage is multifactorial.Among the risk factors are impaired cerebral autoregulation, infections, and coagulation disorders.Machine learning methods allow the identification of combinations of clinical factors to best differentiate CUSHION preterm infants with intra-cerebral bleeding and the development of models for patients at risk of cerebral hemorrhage.In the current study, a Random Forest approach is applied to develop such models for extremely and Accessory very preterm infants (23-30 weeks gestation) based on data collected from a cohort of 229 individuals.

The constructed models exhibit good prediction accuracy and might be used in clinical practice to reduce the risk of cerebral bleeding in prematurity.