ArticleBMC medical informatics and decision making2026
Machine learning-based prognostic modeling of early clinical outcomes in very low birth weight infants: insights from a nationwide cohort on delivery room resuscitation, prematurity-related complications, and mortality.
Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundVery low birth weight infants (VLBWIs) are at high risk of requiring immediate neonatal resuscitation and developing serious complications or mortality. Early prediction based on prenatal information could improve perinatal preparedness and clinical outcomes. We aimed to develop and evaluate machine learning models that predict the need for delivery room resuscitation, major morbidities, and mortality in VLBWIs using prenatal variables.
methodsIn this population-based cohort study, we included 21,231 VLBWIs from the Korean Neonatal Network (2013–2023). Nineteen prenatal and perinatal predictors were used to construct binary classification models for 27 clinical outcomes. Three tree-based algorithms (Random Forest, eXtreme Gradient Boosting, and Light Gradient Boosting Model) were optimized using stratified five-fold cross-validation with the Synthetic Minority Oversampling Technique applied for class imbalance. Model performance was evaluated using F1-score, area under the receiver operating characteristic curve (AUC), and other standard metrics. SHapley Additive exPlanations (SHAP) analysis was used to interpret feature contributions. Final models were implemented in a Streamlit-based web application.
resultsThe models showed strong predictive performance for key outcomes, including resuscitation (F1-score ≥ 0.90, AUC ≥ 0.92), respiratory distress syndrome, and bronchopulmonary dysplasia. Mortality prediction showed moderate performance (F1-score 0.56, AUC 0.89). Predictions for rare complications (such as intraventricular hemorrhage ≥ grade III, necrotizing enterocolitis) yielded lower F1-scores despite high AUCs. Gestational age and birth weight were identified in SHAP analysis as the most influential predictors across outcomes.
conclusionMachine learning models using prenatal information quantitatively predicted neonatal outcomes in VLBWIs. These models show potential as practical tools for early identification of high-risk infants and for supporting delivery planning and clinical assessment.
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