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

Abstract 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 performan...

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Autores principales:	Jae Yoon Na, Dongkyun Kim, Amy M. Kwon, Jin Yong Jeon, Hyuck Kim, Chang-Ryul Kim, Hyun Ju Lee, Joohyun Lee, Hyun-Kyung Park
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7d5cafaaea814502bfd292550b09a51d
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spelling	oai:doaj.org-article:7d5cafaaea814502bfd292550b09a51d2021-11-21T12:16:30ZArtificial intelligence model comparison for risk factor analysis of patent ductus arteriosus in nationwide very low birth weight infants cohort10.1038/s41598-021-01640-52045-2322https://doaj.org/article/7d5cafaaea814502bfd292550b09a51d2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01640-5https://doaj.org/toc/2045-2322Abstract 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.Jae Yoon NaDongkyun KimAmy M. KwonJin Yong JeonHyuck KimChang-Ryul KimHyun Ju LeeJoohyun LeeHyun-Kyung ParkNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jae Yoon Na Dongkyun Kim Amy M. Kwon Jin Yong Jeon Hyuck Kim Chang-Ryul Kim Hyun Ju Lee Joohyun Lee Hyun-Kyung Park Artificial intelligence model comparison for risk factor analysis of patent ductus arteriosus in nationwide very low birth weight infants cohort
description	Abstract 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.
format	article
author	Jae Yoon Na Dongkyun Kim Amy M. Kwon Jin Yong Jeon Hyuck Kim Chang-Ryul Kim Hyun Ju Lee Joohyun Lee Hyun-Kyung Park
author_facet	Jae Yoon Na Dongkyun Kim Amy M. Kwon Jin Yong Jeon Hyuck Kim Chang-Ryul Kim Hyun Ju Lee Joohyun Lee Hyun-Kyung Park
author_sort	Jae Yoon Na
title	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_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_sort	artificial intelligence model comparison for risk factor analysis of patent ductus arteriosus in nationwide very low birth weight infants cohort
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7d5cafaaea814502bfd292550b09a51d
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Artificial intelligence model comparison for risk factor analysis of patent ductus arteriosus in nationwide very low birth weight infants cohort

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