Development of a machine learning model for prediction of intraventricular hemorrhage in premature neonates.

Journal: Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery

PMID: 39680160

Abstract

PURPOSE: Intraventricular hemorrhage (IVH) is a common and severe complication in premature neonates, leading to long-term neurological impairments. Early prediction and identification of risk factors for IVH in premature neonates are crucial for improving clinical outcomes. This study aimed to predict IVH in premature neonates and determine risk factors using machine learning (ML) algorithms.

Authors

Emad Saeedi

Department of Neurosurgery, Mashhad University of Medical Sciences, Mashhad, Iran.
Mojtaba Mashhadinejad

Department of Neurosurgery, Mashhad University of Medical Sciences, Mashhad, Iran.
Amin Tavallaii

Department of Neurosurgery, Akbar Children's Hospital, Mashhad University of Medical Sciences, Mashhad, Iran. dr.amin.tavallaii@gmail.com.

Keywords

Algorithms Cerebral Hemorrhage Cerebral Intraventricular Hemorrhage Female Gestational Age Humans Infant, Newborn Infant, Premature Infant, Premature, Diseases Machine Learning Male Retrospective Studies Risk Factors

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View on PubMed Access via DOI PubMed (39680160)

Development of a machine learning model for prediction of intraventricular hemorrhage in premature neonates.

Abstract

Authors

Keywords

External Resources

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