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Cardiopulmonary resuscitation of infants at birth: predictable or unpredictable?
BACKGROUND: Anticipating the need for at-birth cardiopulmonary resuscitation (CPR) in neonates is very important and complex. Timely identification and rapid CPR for neonates in the delivery room significantly reduce mortality and other neurological disabilities. The aim of this study was to create...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Korean Society of Critical Care Medicine
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9475154/ https://www.ncbi.nlm.nih.gov/pubmed/36102005 http://dx.doi.org/10.4266/acc.2021.01501 |
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author | Zarkesh, Mohammad Reza Moradi, Raheleh Orooji, Azam |
author_facet | Zarkesh, Mohammad Reza Moradi, Raheleh Orooji, Azam |
author_sort | Zarkesh, Mohammad Reza |
collection | PubMed |
description | BACKGROUND: Anticipating the need for at-birth cardiopulmonary resuscitation (CPR) in neonates is very important and complex. Timely identification and rapid CPR for neonates in the delivery room significantly reduce mortality and other neurological disabilities. The aim of this study was to create a prediction system for identifying the need for at-birth CPR in neonates based on Machine Learning (ML) algorithms. METHODS: In this study, 3,882 neonatal medical records were retrospectively reviewed. A total of 60 risk factors was extracted, and five ML algorithms of J48, Naïve Bayesian, multilayer perceptron, support vector machine (SVM), and random forest were compared to predict the need for at-birth CPR in neonates. Two types of resuscitation were considered: basic and advanced CPR. Using five feature selection algorithms, features were ranked based on importance, and important risk factors were identified using the ML algorithms. RESULTS: To predict the need for at-birth CPR in neonates, SVM using all risk factors reached 88.43% accuracy and F-measure of 88.4%, while J48 using only the four first important features reached 90.89% accuracy and F-measure of 90.9%. The most important risk factors were gestational age, delivery type, presentation, and mother’s addiction. CONCLUSIONS: The proposed system can be useful in predicting the need for CPR in neonates in the delivery room. |
format | Online Article Text |
id | pubmed-9475154 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Korean Society of Critical Care Medicine |
record_format | MEDLINE/PubMed |
spelling | pubmed-94751542022-09-19 Cardiopulmonary resuscitation of infants at birth: predictable or unpredictable? Zarkesh, Mohammad Reza Moradi, Raheleh Orooji, Azam Acute Crit Care Original Article BACKGROUND: Anticipating the need for at-birth cardiopulmonary resuscitation (CPR) in neonates is very important and complex. Timely identification and rapid CPR for neonates in the delivery room significantly reduce mortality and other neurological disabilities. The aim of this study was to create a prediction system for identifying the need for at-birth CPR in neonates based on Machine Learning (ML) algorithms. METHODS: In this study, 3,882 neonatal medical records were retrospectively reviewed. A total of 60 risk factors was extracted, and five ML algorithms of J48, Naïve Bayesian, multilayer perceptron, support vector machine (SVM), and random forest were compared to predict the need for at-birth CPR in neonates. Two types of resuscitation were considered: basic and advanced CPR. Using five feature selection algorithms, features were ranked based on importance, and important risk factors were identified using the ML algorithms. RESULTS: To predict the need for at-birth CPR in neonates, SVM using all risk factors reached 88.43% accuracy and F-measure of 88.4%, while J48 using only the four first important features reached 90.89% accuracy and F-measure of 90.9%. The most important risk factors were gestational age, delivery type, presentation, and mother’s addiction. CONCLUSIONS: The proposed system can be useful in predicting the need for CPR in neonates in the delivery room. Korean Society of Critical Care Medicine 2022-08 2022-08-29 /pmc/articles/PMC9475154/ /pubmed/36102005 http://dx.doi.org/10.4266/acc.2021.01501 Text en Copyright © 2022 The Korean Society of Critical Care Medicine https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Zarkesh, Mohammad Reza Moradi, Raheleh Orooji, Azam Cardiopulmonary resuscitation of infants at birth: predictable or unpredictable? |
title | Cardiopulmonary resuscitation of infants at birth: predictable or unpredictable? |
title_full | Cardiopulmonary resuscitation of infants at birth: predictable or unpredictable? |
title_fullStr | Cardiopulmonary resuscitation of infants at birth: predictable or unpredictable? |
title_full_unstemmed | Cardiopulmonary resuscitation of infants at birth: predictable or unpredictable? |
title_short | Cardiopulmonary resuscitation of infants at birth: predictable or unpredictable? |
title_sort | cardiopulmonary resuscitation of infants at birth: predictable or unpredictable? |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9475154/ https://www.ncbi.nlm.nih.gov/pubmed/36102005 http://dx.doi.org/10.4266/acc.2021.01501 |
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