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
- Author
- 권보경
- Keywords
- VERY low birth weight; PATENT ductus arteriosus; WEIGHT in infancy; ARTIFICIAL intelligence; FACTOR analysis; COMORBIDITY
- Issue Date
- 2021-11
- Publisher
- NATURE RESEARCH
- Citation
- Scientific Reports. 11/16/2021, Vol. 11 Issue 1, p1-10. 10p.
- 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.
- URI
- https://www.proquest.com/docview/2597936070?accountid=11283https://repository.hanyang.ac.kr/handle/20.500.11754/172357
- ISSN
- 2045-2322
- DOI
- 10.1038/s41598-021-01640-5
- Appears in Collections:
- COLLEGE OF SPORTS AND ARTS[E](예체능대학) > ETC
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