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Artificial intelligence model comparison for risk factor analysis of patent ductus arteriosus in nationwide very low birth weight infants cohort

Despite the many comorbidities and high mortality rate in preterm infants with patent ductus arteriosus (PDA), therapeutic strategies vary depending on the clinical setting, and most studies of the related risk factors are based on small sample populations. We aimed to compare the performance of art...

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Autores principales: Na, Jae Yoon, Kim, Dongkyun, Kwon, Amy M., Jeon, Jin Yong, Kim, Hyuck, Kim, Chang-Ryul, Lee, Hyun Ju, Lee, Joohyun, Park, Hyun-Kyung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8595677/
https://www.ncbi.nlm.nih.gov/pubmed/34785709
http://dx.doi.org/10.1038/s41598-021-01640-5
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author Na, Jae Yoon
Kim, Dongkyun
Kwon, Amy M.
Jeon, Jin Yong
Kim, Hyuck
Kim, Chang-Ryul
Lee, Hyun Ju
Lee, Joohyun
Park, Hyun-Kyung
author_facet Na, Jae Yoon
Kim, Dongkyun
Kwon, Amy M.
Jeon, Jin Yong
Kim, Hyuck
Kim, Chang-Ryul
Lee, Hyun Ju
Lee, Joohyun
Park, Hyun-Kyung
author_sort Na, Jae Yoon
collection PubMed
description Despite the many comorbidities and high mortality rate in preterm infants with patent ductus arteriosus (PDA), therapeutic strategies vary depending on the clinical setting, and most studies of the related risk factors are based on small sample populations. We aimed to compare the performance of artificial intelligence (AI) analysis with that of conventional analysis to identify risk factors associated with symptomatic PDA (sPDA) in very low birth weight infants. This nationwide cohort study included 8369 very low birth weight (VLBW) infants. The participants were divided into an sPDA group and an asymptomatic PDA or spontaneously close PDA (nPDA) group. The sPDA group was further divided into treated and untreated subgroups. A total of 47 perinatal risk factors were collected and analyzed. Multiple logistic regression was used as a standard analytic tool, and five AI algorithms were used to identify the factors associated with sPDA. Combining a large database of risk factors from nationwide registries and AI techniques achieved higher accuracy and better performance of the PDA prediction tasks, and the ensemble methods showed the best performances.
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spelling pubmed-85956772021-11-17 Artificial intelligence model comparison for risk factor analysis of patent ductus arteriosus in nationwide very low birth weight infants cohort Na, Jae Yoon Kim, Dongkyun Kwon, Amy M. Jeon, Jin Yong Kim, Hyuck Kim, Chang-Ryul Lee, Hyun Ju Lee, Joohyun Park, Hyun-Kyung Sci Rep Article Despite the many comorbidities and high mortality rate in preterm infants with patent ductus arteriosus (PDA), therapeutic strategies vary depending on the clinical setting, and most studies of the related risk factors are based on small sample populations. We aimed to compare the performance of artificial intelligence (AI) analysis with that of conventional analysis to identify risk factors associated with symptomatic PDA (sPDA) in very low birth weight infants. This nationwide cohort study included 8369 very low birth weight (VLBW) infants. The participants were divided into an sPDA group and an asymptomatic PDA or spontaneously close PDA (nPDA) group. The sPDA group was further divided into treated and untreated subgroups. A total of 47 perinatal risk factors were collected and analyzed. Multiple logistic regression was used as a standard analytic tool, and five AI algorithms were used to identify the factors associated with sPDA. Combining a large database of risk factors from nationwide registries and AI techniques achieved higher accuracy and better performance of the PDA prediction tasks, and the ensemble methods showed the best performances. Nature Publishing Group UK 2021-11-16 /pmc/articles/PMC8595677/ /pubmed/34785709 http://dx.doi.org/10.1038/s41598-021-01640-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Na, Jae Yoon
Kim, Dongkyun
Kwon, Amy M.
Jeon, Jin Yong
Kim, Hyuck
Kim, Chang-Ryul
Lee, Hyun Ju
Lee, Joohyun
Park, Hyun-Kyung
Artificial intelligence model comparison for risk factor analysis of patent ductus arteriosus in nationwide very low birth weight infants cohort
title Artificial intelligence model comparison for risk factor analysis of patent ductus arteriosus in nationwide very low birth weight infants cohort
title_full Artificial intelligence model comparison for risk factor analysis of patent ductus arteriosus in nationwide very low birth weight infants cohort
title_fullStr Artificial intelligence model comparison for risk factor analysis of patent ductus arteriosus in nationwide very low birth weight infants cohort
title_full_unstemmed Artificial intelligence model comparison for risk factor analysis of patent ductus arteriosus in nationwide very low birth weight infants cohort
title_short Artificial intelligence model comparison for risk factor analysis of patent ductus arteriosus in nationwide very low birth weight infants cohort
title_sort artificial intelligence model comparison for risk factor analysis of patent ductus arteriosus in nationwide very low birth weight infants cohort
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8595677/
https://www.ncbi.nlm.nih.gov/pubmed/34785709
http://dx.doi.org/10.1038/s41598-021-01640-5
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