Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 조수진 | * |
dc.date.accessioned | 2022-08-12T16:31:21Z | - |
dc.date.available | 2022-08-12T16:31:21Z | - |
dc.date.issued | 2022 | * |
dc.identifier.issn | 0743-8346 | * |
dc.identifier.other | OAK-32113 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/262406 | - |
dc.description.abstract | Background: Advances in technology, data availability, and analytics have helped improve quality of care in the neonatal intensive care unit. Objective: To provide an in-depth review of artificial intelligence (AI) and machine learning techniques being utilized to predict neonatal outcomes. Methods: The PRISMA protocol was followed that considered articles from established digital repositories. Included articles were categorized based on predictions of: (a) major neonatal morbidities such as sepsis, bronchopulmonary dysplasia, intraventricular hemorrhage, necrotizing enterocolitis, and retinopathy of prematurity; (b) mortality; and (c) length of stay. Results: A total of 366 studies were considered; 68 studies were eligible for inclusion in the review. The current set of predictor models are primarily built on supervised learning and mostly used regression models built on retrospective data. Conclusion: With the availability of EMR data and data-sharing of NICU outcomes across neonatal research networks, machine learning algorithms have shown breakthrough performance in predicting neonatal disease. © 2022, The Author(s), under exclusive licence to Springer Nature America, Inc. | * |
dc.language | English | * |
dc.publisher | Springer Nature | * |
dc.title | Predicting clinical outcomes using artificial intelligence and machine learning in neonatal intensive care units: a systematic review | * |
dc.type | Review | * |
dc.relation.issue | 12 | * |
dc.relation.volume | 42 | * |
dc.relation.index | SCIE | * |
dc.relation.index | SCOPUS | * |
dc.relation.startpage | 1561 | * |
dc.relation.lastpage | 1575 | * |
dc.relation.journaltitle | Journal of Perinatology | * |
dc.identifier.doi | 10.1038/s41372-022-01392-8 | * |
dc.identifier.scopusid | 2-s2.0-85132634718 | * |
dc.author.google | McAdams R.M. | * |
dc.author.google | Kaur R. | * |
dc.author.google | Sun Y. | * |
dc.author.google | Bindra H. | * |
dc.author.google | Cho S.J. | * |
dc.author.google | Singh H. | * |
dc.contributor.scopusid | 조수진(35200321000) | * |
dc.date.modifydate | 20231120164420 | * |