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

Autoři: Varvara Turova aff001;  Irina Sidorenko aff002;  Laura Eckardt aff003;  Esther Rieger-Fackeldey aff004;  Ursula Felderhoff-Müser aff003;  Ana Alves-Pinto aff001;  Renée Lampe aff001
Působiště autorů: Research Unit for Pediatric Neuroorthopedics and Cerebral Palsy of the Buhl-Strohmaier Foundation, Orthopedic Department, Klinikum Rechts der Isar, Technical University of Munich, München, Germany aff001;  Chair of Mathematical Modelling, Mathematical Faculty, Technical University of Munich, Garching bei München, Germany aff002;  Departments of Pediatrics and Neonatology, University Hospital Essen, University of Duisburg‐Essen, Essen, Germany aff003;  Department of Pediatrics, Neonatology, Klinikum Rechts der Isar, Technical University of Munich, München, Germany aff004
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0227419


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.

Klíčová slova:

Birth weight – Blood vessels – Cerebral blood flow assay – Hemorrhage – Machine learning – Neonates – Oxygen – Ultrasound imaging


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