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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| Published in | PloS one Vol. 15; no. 1; p. e0227419 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
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United States
Public Library of Science
01.01.2020
Public Library of Science (PLoS) |
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| ISSN | 1932-6203 1932-6203 |
| DOI | 10.1371/journal.pone.0227419 |
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| Abstract | 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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| AbstractList | 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. 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.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. |
| Author | Eckardt, Laura Alves-Pinto, Ana Sidorenko, Irina Lampe, Renée Felderhoff-Müser, Ursula Turova, Varvara Rieger-Fackeldey, Esther |
| AuthorAffiliation | 1 Research Unit for Pediatric Neuroorthopedics and Cerebral Palsy of the Buhl-Strohmaier Foundation, Orthopedic Department, Klinikum Rechts der Isar, Technical University of Munich, München, Germany Western University, CANADA 3 Departments of Pediatrics and Neonatology, University Hospital Essen, University of Duisburg‐Essen, Essen, Germany 2 Chair of Mathematical Modelling, Mathematical Faculty, Technical University of Munich, Garching bei München, Germany 4 Department of Pediatrics, Neonatology, Klinikum Rechts der Isar, Technical University of Munich, München, Germany |
| AuthorAffiliation_xml | – name: 3 Departments of Pediatrics and Neonatology, University Hospital Essen, University of Duisburg‐Essen, Essen, Germany – name: 2 Chair of Mathematical Modelling, Mathematical Faculty, Technical University of Munich, Garching bei München, Germany – name: Western University, CANADA – name: 4 Department of Pediatrics, Neonatology, Klinikum Rechts der Isar, Technical University of Munich, München, Germany – name: 1 Research Unit for Pediatric Neuroorthopedics and Cerebral Palsy of the Buhl-Strohmaier Foundation, Orthopedic Department, Klinikum Rechts der Isar, Technical University of Munich, München, Germany |
| Author_xml | – sequence: 1 givenname: Varvara orcidid: 0000-0002-6613-3311 surname: Turova fullname: Turova, Varvara – sequence: 2 givenname: Irina surname: Sidorenko fullname: Sidorenko, Irina – sequence: 3 givenname: Laura surname: Eckardt fullname: Eckardt, Laura – sequence: 4 givenname: Esther surname: Rieger-Fackeldey fullname: Rieger-Fackeldey, Esther – sequence: 5 givenname: Ursula surname: Felderhoff-Müser fullname: Felderhoff-Müser, Ursula – sequence: 6 givenname: Ana surname: Alves-Pinto fullname: Alves-Pinto, Ana – sequence: 7 givenname: Renée surname: Lampe fullname: Lampe, Renée |
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| Copyright | 2020 Turova et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2020 Turova et al 2020 Turova et al |
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| SubjectTerms | Age Biology and Life Sciences Birth weight Bleeding Brain damage Brain injury Brain research Cerebral Hemorrhage - diagnosis Cerebral Hemorrhage - physiopathology Cerebral palsy Coagulation Computer and Information Sciences Departments Ethics Female Forest management Gestation Hemorrhage Humans Identification methods Infant, Extremely Premature Infant, Newborn Infants Learning algorithms Machine Learning Male Medical prognosis Medicine and Health Sciences Model accuracy Models, Cardiovascular Newborn babies Obstetrics Paralysis Pathogenesis Pediatrics Physical Sciences Premature babies Research and Analysis Methods Retrospective Studies Risk analysis Risk Factors Risk reduction Software Ultrasonic imaging |
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| Title | Machine learning models for identifying preterm infants at risk of cerebral hemorrhage |
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