aceImproving early risk stratification for bronchopulmonary Dysplasia in preterm infants using machine learning: development and external validation
Background: Despite advances in perinatal care and improved survival of preterm infants, bronchopulmonary dysplasia (BPD) remains a major neonatal morbidity with long-term consequences. Accurate early risk prediction is therefore clinically important. Conventional models may be limited in capturing the complex and nonlinear interactions underlying BPD, whereas machine learning offers greater flexibility for high-dimensional clinical data. However, limited interpretability remains a barrier to clinical implementa…
A gradient boosting machine model gave interpretable, individualized early BPD risk estimates for preterm infants using routinely available clinical variables, with internal AUC of 0.880.
The study was retrospective and single-center in development, and the authors state that prospective multicenter validation is required before routine clinical use; the model is for risk stratification, not treatment guidance.
Evidence
- Peer-reviewedAnnals of Medicine2026-09-25
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Truvace Impact Record TRV-2026-1521, v1: “Improving early risk stratification for bronchopulmonary Dysplasia in preterm infants using machine learning: development and external validation.” Truvace, 2026-10-16. /record/TRV-2026-1521 (accessed at citation time). sha256 3b4087943c3c38d7…
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