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

PLoS One. 2020 Jan 15;15(1):e0227419. doi: 10.1371/journal.pone.0227419. eCollection 2020.

Abstract

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 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 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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cerebral Hemorrhage* / diagnosis
  • Cerebral Hemorrhage* / physiopathology
  • Female
  • Humans
  • Infant, Extremely Premature*
  • Infant, Newborn
  • Machine Learning*
  • Male
  • Models, Cardiovascular*
  • Retrospective Studies
  • Risk Factors

Grants and funding

This work was funded by the Buhl-Strohmaier-Foundation to VT and AAP; the Klaus Tschira Foundation to IS and LE and the Markus Würth Foundation to RL. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.