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Predicting clinical outcomes using artificial intelligence and machine learning in neonatal intensive care units: a systematic review
- Title
- Predicting clinical outcomes using artificial intelligence and machine learning in neonatal intensive care units: a systematic review
- Authors
- McAdams R.M.; Kaur R.; Sun Y.; Bindra H.; Cho S.J.; Singh H.
- Ewha Authors
- 조수진
- SCOPUS Author ID
- 조수진
- Issue Date
- 2022
- Journal Title
- Journal of Perinatology
- ISSN
- 0743-8346
- Citation
- Journal of Perinatology vol. 42, no. 12, pp. 1561 - 1575
- Publisher
- Springer Nature
- Indexed
- SCIE; SCOPUS
- Document Type
- Review
- 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.
- DOI
- 10.1038/s41372-022-01392-8
- Appears in Collections:
- 의과대학 > 의학과 > Journal papers
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