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

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Fig. 1: Data management in NICU: different sources of data generation in NICU and how they are shared across the research networks for quality improvement.
Fig. 2: AI in NICU: the ability of machines to learn, reason, and assess or predict clinical outcomes.
Fig. 3: Article selection according to the exclusion and inclusion criteria.
Fig. 4: The importance of a multidimensional big data perspective in the NICU.
Fig. 5: Patterns of artificial intelligence (AI) relevant in the NICU: a focus on bronchopulmonary dysplasia.

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RM, HS, RK, HB conceptualized and designed the study, analysis, or interpretation of data was done by HB, HS, RK. Drafting of the manuscript was done by RM, HS, and RK. Critical revision of the manuscript for important intellectual content was done by all authors. Statistical analysis was done by HS, HB. Funding was Obtained HS. Administrative, technical, or material support was provided by HS, RK, RM. The project was supervised by RM.

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Correspondence to Harpreet Singh.

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McAdams, R.M., Kaur, R., Sun, Y. et al. Predicting clinical outcomes using artificial intelligence and machine learning in neonatal intensive care units: a systematic review. J Perinatol 42, 1561–1575 (2022). https://doi.org/10.1038/s41372-022-01392-8

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