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Machine learning models for identifying preterm infants at risk of cerebral hemorrhage

Intracerebral hemorrhage in preterm infants is a major cause of brain damage and cerebral palsy. The pathogenesis of cerebral hemorrhage is multifactorial. Among the risk factors are impaired cerebral autoregulation, infections, and coagulation disorders. Machine learning methods allow the identific...

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Autores principales: Turova, Varvara, Sidorenko, Irina, Eckardt, Laura, Rieger-Fackeldey, Esther, Felderhoff-Müser, Ursula, Alves-Pinto, Ana, Lampe, Renée
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6961932/
https://www.ncbi.nlm.nih.gov/pubmed/31940391
http://dx.doi.org/10.1371/journal.pone.0227419
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author Turova, Varvara
Sidorenko, Irina
Eckardt, Laura
Rieger-Fackeldey, Esther
Felderhoff-Müser, Ursula
Alves-Pinto, Ana
Lampe, Renée
author_facet Turova, Varvara
Sidorenko, Irina
Eckardt, Laura
Rieger-Fackeldey, Esther
Felderhoff-Müser, Ursula
Alves-Pinto, Ana
Lampe, Renée
author_sort Turova, Varvara
collection PubMed
description Intracerebral hemorrhage in preterm infants is a major cause of brain damage and cerebral palsy. The pathogenesis of cerebral hemorrhage is multifactorial. Among the risk factors are impaired cerebral autoregulation, infections, and coagulation disorders. Machine learning methods allow the identification of combinations of clinical factors to best differentiate preterm infants with intra-cerebral bleeding and the development of models for patients at risk of cerebral hemorrhage. In the current study, a Random Forest approach is applied to develop such models for extremely and very preterm infants (23–30 weeks gestation) based on data collected from a cohort of 229 individuals. The constructed models exhibit good prediction accuracy and might be used in clinical practice to reduce the risk of cerebral bleeding in prematurity.
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spelling pubmed-69619322020-01-26 Machine learning models for identifying preterm infants at risk of cerebral hemorrhage Turova, Varvara Sidorenko, Irina Eckardt, Laura Rieger-Fackeldey, Esther Felderhoff-Müser, Ursula Alves-Pinto, Ana Lampe, Renée PLoS One Research Article Intracerebral hemorrhage in preterm infants is a major cause of brain damage and cerebral palsy. The pathogenesis of cerebral hemorrhage is multifactorial. Among the risk factors are impaired cerebral autoregulation, infections, and coagulation disorders. Machine learning methods allow the identification of combinations of clinical factors to best differentiate preterm infants with intra-cerebral bleeding and the development of models for patients at risk of cerebral hemorrhage. In the current study, a Random Forest approach is applied to develop such models for extremely and very preterm infants (23–30 weeks gestation) based on data collected from a cohort of 229 individuals. The constructed models exhibit good prediction accuracy and might be used in clinical practice to reduce the risk of cerebral bleeding in prematurity. Public Library of Science 2020-01-15 /pmc/articles/PMC6961932/ /pubmed/31940391 http://dx.doi.org/10.1371/journal.pone.0227419 Text en © 2020 Turova et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Turova, Varvara
Sidorenko, Irina
Eckardt, Laura
Rieger-Fackeldey, Esther
Felderhoff-Müser, Ursula
Alves-Pinto, Ana
Lampe, Renée
Machine learning models for identifying preterm infants at risk of cerebral hemorrhage
title Machine learning models for identifying preterm infants at risk of cerebral hemorrhage
title_full Machine learning models for identifying preterm infants at risk of cerebral hemorrhage
title_fullStr Machine learning models for identifying preterm infants at risk of cerebral hemorrhage
title_full_unstemmed Machine learning models for identifying preterm infants at risk of cerebral hemorrhage
title_short Machine learning models for identifying preterm infants at risk of cerebral hemorrhage
title_sort machine learning models for identifying preterm infants at risk of cerebral hemorrhage
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6961932/
https://www.ncbi.nlm.nih.gov/pubmed/31940391
http://dx.doi.org/10.1371/journal.pone.0227419
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