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Predicting Neonatal Respiratory Outcomes Using Machine Learning: A Systematic Review Through a Life Course Lens
Janet Northcote1, Sawyer Olson, Tamara G R Macieira
1Author Affiliations: University of Florida, College of Nursing, Gainesville, Florida, Department of Family, Community and Health Systems (Northcote, Macieira, and Pruinelli), and Department of Biobehavioral Nursing Science (Parker); and University of Florida, College of Medicine, Gainesville, Florida Department of Health Outcomes and Biomedical Informatics (Liu), and Department of Surgery (Pruinelli). None: (Olson).
Insights
Artificial intelligence and machine learning models show promise for predicting neonatal respiratory outcomes in the NICU. However, wider clinical use requires more external validation and consideration of developmental factors.
Area of Science:
- Neonatal Health
- Artificial Intelligence
- Machine Learning
- Health Informatics
Background:
- Neonatal respiratory outcomes are a major cause of morbidity and mortality in Neonatal Intensive Care Units (NICUs).
- Accurate early risk prediction is crucial for improving neonatal care and reducing hospital stays.
- Artificial Intelligence (AI) and Machine Learning (ML) offer potential for enhanced predictive capabilities.
Purpose of the Study:
- To systematically review AI/ML models for predicting neonatal respiratory outcomes.
- To evaluate these models through a Life Course Health Development (LCHD) framework.
- To assess the incorporation of developmental timing, cumulative processes, and contextual factors in existing models.
Main Methods:
- A systematic review guided by Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards.
- Searched PubMed, Embase, and Web of Science databases (2014-2025).
- Included 16 peer-reviewed studies developing or validating AI/ML prediction models for neonatal respiratory outcomes, assessing risk of bias using PROBAST + AI.
Main Results:
- Models focused on bronchopulmonary dysplasia, respiratory distress syndrome, and apnea of prematurity.
- Strong internal validation was observed (AUC ≥0.85 for several models).
- External validation was infrequent (2/16 studies), and calibration reporting was limited; no models predicted post-discharge outcomes or included comprehensive health determinants.
Conclusions:
- AI/ML models demonstrate potential for neonatal respiratory risk stratification in the NICU.
- Responsible clinical implementation necessitates multisite external validation and routine calibration.
- Future models should incorporate nursing insights and developmental context for improved predictive accuracy and clinical utility.
Background:
Neonatal respiratory outcomes remain leading drivers of neonatal intensive care unit (NICU) morbidity, mortality, and prolonged hospitalization, underscoring the need for accurate early risk prediction through artificial intelligence (AI) and machine learning (ML) approaches.
Purpose:
To conduct a Preferred Reporting Items for Systematic Reviews and Meta-Analyses-guided systematic review synthesizing AI/ML models predicting neonatal respiratory outcomes through a Life Course Health Development (LCHD) lens, examining whether models incorporate developmental timing, cumulative processes, and contextual factors.
Data Sources:
PubMed, Embase, and Web of Science were searched from January 1, 2014, to July 18, 2025.
Study Selection:
Included peer-reviewed studies developing or validating AI/ML prediction models for neonatal respiratory outcomes with performance metrics. Excluded non-ML regression, non-original research, and non-English studies. Of 4319 records initially identified, 1780 unique records were screened, 33 underwent full-text review (0.76%); 16 met selection criteria (0.37%).
Data Extraction:
Two reviewers independently extracted study characteristics, inputs, algorithms, validation strategies, and performance metrics. Risk of bias was assessed using PROBAST + AI. Disagreements were resolved by consensus.
Results:
Studies addressed bronchopulmonary dysplasia (n = 8), respiratory distress syndrome (n = 4), apnea of prematurity (n = 3), and massive pulmonary hemorrhage (n = 1). Discrimination was strong in internal validation (several models with an area under the receiver operating characteristic curve ≥0.85). External validation was rare (2/16); calibration reporting was sparse. No studies predicted postdischarge respiratory outcomes or included comprehensive determinants of health.
Implications For Practice And Research:
Although ML models show promise for NICU risk stratification, responsible clinical adoption requires multisite external validation, routine calibration, and nursing-informed designs incorporating development context. See Video Abstract, Supplemental Digital Content.
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