Predicting clinical outcomes using artificial intelligence and machine learning in neonatal intensive care units: a systematic review
- 등재 SCIE, SCOPUS
- 발행기관 Springer Nature
- 발행년도 2022
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000193627
- 본문언어 영어
- Published As https://doi.org/10.1038/s41372-022-01392-8
초록/요약
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.
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