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Erschienen in:
Recognition of Conformational States of a G Protein-Coupled Receptor from Molecular Dynamic Simulations Using Sampling Techniques Kapitel lesen Erstes Kapitel lesen

2023 | OriginalPaper | Buchkapitel

Detecting Intra Ventricular Haemorrhage in Preterm Neonates Using LSTM Autoencoders

verfasst von : Idris Oladele Muniru, Jacomine Grobler, Lizelle Van Wyk

Erschienen in: Bioinformatics and Biomedical Engineering

Verlag: Springer Nature Switzerland

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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.

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Vorheriges Kapitel Using Digital Biomarkers for Objective Assessment of Perfusionists’ Workload and Acute Stress During Cardiac Surgery
Nächstes Kapitel Measurement of Acute Pain in the Pediatric Emergency Department Through Automatic Detection of Behavioral Parameters: A Pilot Study
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Metadaten
Titel
Detecting Intra Ventricular Haemorrhage in Preterm Neonates Using LSTM Autoencoders
verfasst von
Idris Oladele Muniru
Jacomine Grobler
Lizelle Van Wyk
Copyright-Jahr
2023
Buch
Bioinformatics and Biomedical Engineering

Print ISBN: 978-3-031-34952-2

Electronic ISBN: 978-3-031-34953-9

Copyright-Jahr: 2023

DOI
https://doi.org/10.1007/978-3-031-34953-9_36

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