Development of a machine learning model to identify intraventricular hemorrhage using time-series analysis in preterm infants

Development of a machine learning model to identify intraventricular hemorrhage using time-series analysis in preterm infants

Although the prevalence of intraventricular hemorrhage (IVH) has remained high, no optimal strategy has been established to prevent it. This study included preterm newborns born at a gestational age of

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Veröffentlicht in:Scientific reports 2024-10, Vol.14 (1), p.23740-8, Article 23740
Hauptverfasser: Han, Hye-Ji, Ji, Hyunmin, Choi, Ji-Eun, Chung, Yoon Gi, Kim, Hunmin, Choi, Chang Won, Kim, Kyunghoon, Jung, Young Hwa
Format: Artikel
Sprache:eng
Schlagworte:
631/114/1305
692/700/1720/3185
692/700/1720/3186
692/700/1720/3195
Birth weight
Cerebral Hemorrhage - diagnosis
Cerebral intraventricular hemorrhage
Cerebral Intraventricular Hemorrhage - diagnosis
Female
Gestational Age
Hemorrhage
Humanities and Social Sciences
Humans
Infant, Newborn
Infant, Premature
Infant, Premature, Diseases - diagnosis
Infants
Intensive Care Units, Neonatal
Learning algorithms
Machine Learning
Male
multidisciplinary
Neonatal intensive care
Neonates
Newborn babies
Retrospective Studies
Risk groups
ROC Curve
Science
Science (multidisciplinary)
Time series
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Zusammenfassung:Although the prevalence of intraventricular hemorrhage (IVH) has remained high, no optimal strategy has been established to prevent it. This study included preterm newborns born at a gestational age of
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-74298-4