Full metadata record
DC Field | Value | Language |
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dc.contributor.author | 권보경 | - |
dc.date.accessioned | 2022-08-11T01:48:59Z | - |
dc.date.available | 2022-08-11T01:48:59Z | - |
dc.date.issued | 2021-11 | - |
dc.identifier.citation | Scientific Reports. 11/16/2021, Vol. 11 Issue 1, p1-10. 10p. | en_US |
dc.identifier.issn | 2045-2322 | - |
dc.identifier.uri | https://www.proquest.com/docview/2597936070?accountid=11283 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/172357 | - |
dc.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 artifcial 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 fve 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. | en_US |
dc.description.sponsorship | J.Y.N. and H.-K.P. had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: J.Y.N., A.M.K., J L., H.-K.P Acquisition, analysis, or interpretation of data: J.Y.N., D.K., A.M.K., H.J.L., J.L. Drafing of the manuscript: J.Y.N., D.K., A.M.K., J.L., Park Critical revision of the manuscript for important intellectual content: All authors Statistical analysis: A.M.K., D.K., J.L. Obtained funding; J.Y.J., J.L., H.-K.P. Administrative, technical, or material support: J.Y.N., D.K., J.Y.J., H.J.L. Study supervision: H.K., C.R.K., J.L., H.-K.P. Tis work was supported by the Research Program funded by the Korean Centers for Disease Control and Prevention (2019-ER7103-01#) and research funds from the Bio & Medical Technology Development Program of the National Research Foundation of Korea (NRF) funded by the Korean government (MSIT) [grant number NRF-2019M3E5D1A01069363]. | en_US |
dc.language.iso | en | en_US |
dc.publisher | NATURE RESEARCH | en_US |
dc.subject | VERY low birth weight | en_US |
dc.subject | PATENT ductus arteriosus | en_US |
dc.subject | WEIGHT in infancy | en_US |
dc.subject | ARTIFICIAL intelligence | en_US |
dc.subject | FACTOR analysis | en_US |
dc.subject | COMORBIDITY | en_US |
dc.title | Artificial intelligence model comparison for risk factor analysis of patent ductus arteriosus in nationwide very low birth weight infants cohort | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1038/s41598-021-01640-5 | - |
dc.relation.page | 1-9 | - |
dc.relation.journal | SCIENTIFIC REPORTS | - |
dc.contributor.googleauthor | Na, Jae Yoon | - |
dc.contributor.googleauthor | Kim, Dongkyun | - |
dc.contributor.googleauthor | Kwon, Amy M. | - |
dc.contributor.googleauthor | Jeon, Jin Yong | - |
dc.contributor.googleauthor | Kim, Hyuck | - |
dc.contributor.googleauthor | Kim, Chang-Ryul | - |
dc.contributor.googleauthor | Lee, Hyun Ju | - |
dc.contributor.googleauthor | Lee, Joohyun | - |
dc.contributor.googleauthor | Park, Hyun-Kyung | - |
dc.relation.code | 2021002638 | - |
dc.sector.campus | E | - |
dc.sector.daehak | [E] | - |
dc.identifier.pid | amykwon | - |
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