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- W4382394735 abstract "The neonatal period is a critical stage where physiological adaptations for extra-uterine life occur, and newborns are vulnerable to various diseases and disorders. Among these conditions, preterm neonates (PN) born before 37 weeks’ gestation are at higher risk of developing intraventricular hemorrhage (IVH), a common complication that can result in severe neurological complications such as cerebral palsy, developmental delays, and cognitive impairments. Early detection and intervention are essential to prevent long-term consequences. Non-invasive cardiac output monitors (NICOM) have been widely accepted in the neonatal intensive care unit (NICU) for monitoring hemodynamic parameters and have provided vast amounts of data. However, further research is required to explore their predictive tendencies in relation to IVH. The present study aimed to evaluate the potential of deep learning models to enhance early detection and prevention of IVH in preterm neonates using NICOM parameters. From this study, it was shown that by the LSTM autoencoders are able to predict IVH with moderate precision and accuracy but poor specificity. Nonetheless, this study represents a significant step towards developing a non-invasive, accurate, and timely method for monitoring and preventing IVH in preterm neonates, especially in low-resource settings." @default.
- W4382394735 created "2023-06-29" @default.
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- W4382394735 date "2023-01-01" @default.
- W4382394735 modified "2023-09-27" @default.
- W4382394735 title "Detecting Intra Ventricular Haemorrhage in Preterm Neonates Using LSTM Autoencoders" @default.
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- W4382394735 doi "https://doi.org/10.1007/978-3-031-34953-9_36" @default.
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