Machine-Learning Models Outperform Clinicians in Predicting Postnatal Growth Failure Among Very Low Birth Weight Infants
Joohee Lim·Min Soo Park·Teahyen Cha·So Jin Yoon·Jung Ho Han·Jeong Eun Shin·In Gyu Song·Soon Min Lee·ho seon Eun·Sook-Hyun Park
Background/Objectives: Early detection of postnatal growth failure (PGF) is essential for optimizing nutritional management in preterm infants, as PGF is associated with adverse neurodevelopmental outcomes. Early prediction remains difficult because postnatal growth is influenced by multiple clinical factors including gestation age, birth weight, nutritional status, and comorbidities. Machine-learning approaches have been proposed to predict complex neonatal outcomes. This study compared the pre