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Titel:

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

Dokumenttyp:
Article
Autor(en):
Turova, Varvara; Sidorenko, Irina; Eckardt, Laura; Rieger-Fackeldey, Esther; Felderhoff-Mueser, Ursula; Alves-Pinto, Ana; Lampe, Renee
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 s...     »
Zeitschriftentitel:
PLoS One
Jahr:
2020
Band / Volume:
15
Monat:
JAN 15
Heft / Issue:
1
Sprache:
English
Volltext / DOI:
doi:10.1371/journal.pone.0227419
Verlag / Institution:
PUBLIC LIBRARY SCIENCE
Print-ISSN:
1932-6203
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