Machine Learning Detects Intraventricular Haemorrhage in Extremely Preterm Infants

Machine Learning Detects Intraventricular Haemorrhage in Extremely Preterm Infants

Objective: To test the potential utility of applying machine learning methods to regional cerebral (rcSO<sub>2</sub>) and peripheral oxygen saturation (SpO<sub>2</sub>) signals to detect brain injury in extremely preterm infants. Study design: A subset of infants enrolled in...

Full description

Bibliographic Details
Main Authors: Minoo Ashoori, John M. O’Toole, Ken D. O’Halloran, Gunnar Naulaers, Liesbeth Thewissen, Jan Miletin, Po-Yin Cheung, Afif EL-Khuffash, David Van Laere, Zbyněk Straňák, Eugene M. Dempsey, Fiona B. McDonald
Format: Article
Language:English
Published: MDPI AG 2023-05-01
Series:Children
Subjects:
near-infrared spectroscopy (NIRS)
regional cerebral oxygen saturation (rcSO<sub>2</sub>)
peripheral oxygen saturation (SpO<sub>2</sub>)
prolonged relative desaturation (PRD)
extreme gradient boosting (XGBoost)
Online Access:https://www.mdpi.com/2227-9067/10/6/917

Internet

https://www.mdpi.com/2227-9067/10/6/917

Similar Items