Machine Learning-Based Clinical Adjusted Selection of Predicting Risk Factors for Shunt Infection in Children

Background: Shunt Infection is a common complication of shunt insertion in children which can lead to bad neuro-developmental conditions and impose a considerable economic burden for the health care system. So, identifying predictive factors of shunt infection could help us in the proper improvement...

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Autores principales:	Ehsan Moradi, Malihe Sabeti, Nasrin Shahbazi, Zohreh Habibi, Farideh Nejat
Formato:	article
Lenguaje:	EN
Publicado:	Shahid Beheshti University of Medical Sciences 2021
Materias:	hydrocephalus shunt infection sparsity correlation redundancy Medicine R
Acceso en línea:	https://doaj.org/article/8efee45589b647e8ad4596c5f4d5d76f
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spelling	oai:doaj.org-article:8efee45589b647e8ad4596c5f4d5d76f2021-11-16T10:51:17ZMachine Learning-Based Clinical Adjusted Selection of Predicting Risk Factors for Shunt Infection in Children2383-18712383-209610.34172/icnj.2021.28https://doaj.org/article/8efee45589b647e8ad4596c5f4d5d76f2021-07-01T00:00:00Zhttps://journals.sbmu.ac.ir/neuroscience/article/view/34895/27356https://doaj.org/toc/2383-1871https://doaj.org/toc/2383-2096Background: Shunt Infection is a common complication of shunt insertion in children which can lead to bad neuro-developmental conditions and impose a considerable economic burden for the health care system. So, identifying predictive factors of shunt infection could help us in the proper improvement of this deteriorating condition. Methods: In this study, related risk factors of 68 patients with history of shunt infection and 80 matched controls without any history of shunt infection, who were all operated in a single referral hospital were assessed. Three machine learning (ML)-based measures including sparsity, correlation, and redundancy along with specialist’s score were applied to select the most important predictive risk factors for shunt infection. ML was determined by summation of sparsity, correlation and redundancy measures, and the final total score was considered as normalization (ML-based score + specialist score). Results: According to the total score, prematurity, first ventriculoperitoneal shunting (VPS) age, intraventricular hemorrhage (IVH), myelomeningocele (MMC) and low birth weight had higher weights as shunt infection risk factors. icterus, trauma, co-infection and tumor had the lowest weights and history of meningitis and number of shunt revisions were defined as intermediate risk factors. Conclusion: The "ML-based clinical adjusted" method may be used as a complementary tool to help neurosurgeons in better patient selection and more accurate follow-up of children with higher risk of shunt infection.Ehsan MoradiMalihe SabetiNasrin ShahbaziZohreh HabibiFarideh NejatShahid Beheshti University of Medical Sciencesarticlehydrocephalusshunt infectionsparsitycorrelationredundancyMedicineRENInternational Clinical Neuroscience Journal, Vol 8, Iss 3, Pp 135-143 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hydrocephalus shunt infection sparsity correlation redundancy Medicine R
spellingShingle	hydrocephalus shunt infection sparsity correlation redundancy Medicine R Ehsan Moradi Malihe Sabeti Nasrin Shahbazi Zohreh Habibi Farideh Nejat Machine Learning-Based Clinical Adjusted Selection of Predicting Risk Factors for Shunt Infection in Children
description	Background: Shunt Infection is a common complication of shunt insertion in children which can lead to bad neuro-developmental conditions and impose a considerable economic burden for the health care system. So, identifying predictive factors of shunt infection could help us in the proper improvement of this deteriorating condition. Methods: In this study, related risk factors of 68 patients with history of shunt infection and 80 matched controls without any history of shunt infection, who were all operated in a single referral hospital were assessed. Three machine learning (ML)-based measures including sparsity, correlation, and redundancy along with specialist’s score were applied to select the most important predictive risk factors for shunt infection. ML was determined by summation of sparsity, correlation and redundancy measures, and the final total score was considered as normalization (ML-based score + specialist score). Results: According to the total score, prematurity, first ventriculoperitoneal shunting (VPS) age, intraventricular hemorrhage (IVH), myelomeningocele (MMC) and low birth weight had higher weights as shunt infection risk factors. icterus, trauma, co-infection and tumor had the lowest weights and history of meningitis and number of shunt revisions were defined as intermediate risk factors. Conclusion: The "ML-based clinical adjusted" method may be used as a complementary tool to help neurosurgeons in better patient selection and more accurate follow-up of children with higher risk of shunt infection.
format	article
author	Ehsan Moradi Malihe Sabeti Nasrin Shahbazi Zohreh Habibi Farideh Nejat
author_facet	Ehsan Moradi Malihe Sabeti Nasrin Shahbazi Zohreh Habibi Farideh Nejat
author_sort	Ehsan Moradi
title	Machine Learning-Based Clinical Adjusted Selection of Predicting Risk Factors for Shunt Infection in Children
title_short	Machine Learning-Based Clinical Adjusted Selection of Predicting Risk Factors for Shunt Infection in Children
title_full	Machine Learning-Based Clinical Adjusted Selection of Predicting Risk Factors for Shunt Infection in Children
title_fullStr	Machine Learning-Based Clinical Adjusted Selection of Predicting Risk Factors for Shunt Infection in Children
title_full_unstemmed	Machine Learning-Based Clinical Adjusted Selection of Predicting Risk Factors for Shunt Infection in Children
title_sort	machine learning-based clinical adjusted selection of predicting risk factors for shunt infection in children
publisher	Shahid Beheshti University of Medical Sciences
publishDate	2021
url	https://doaj.org/article/8efee45589b647e8ad4596c5f4d5d76f
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Machine Learning-Based Clinical Adjusted Selection of Predicting Risk Factors for Shunt Infection in Children

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