Predicting clinical outcomes using artificial intelligence and machine learning in neonatal intensive care units: a systematic review

Ryan M. McAdams, Ravneet Kaur, Yao Sun, Harlieen Bindra, Su Jin Cho, Harpreet Singh

Research output: Contribution to journalReview articlepeer-review

25 Scopus citations

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.

Original languageEnglish
Pages (from-to)1561-1575
Number of pages15
JournalJournal of Perinatology
Volume42
Issue number12
DOIs
StatePublished - Dec 2022

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.

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