Machine Learning Model Predicts One-Year Rehospitalization in Preterm Infants

Preterm birth remains a significant driver of pediatric morbidity, requiring complex transitions from the neonatal intensive care unit (NICU) to home. While late preterm infants constitute the majority of these births, they remain a vulnerable population with high rates of unplanned readmission [1]. Beyond immediate clinical complications, these infants face long-term risks including respiratory impairment and developmental delays, particularly when conditions like bronchopulmonary dysplasia are present [2]. Socioeconomic disparities further complicate postnatal growth and recovery, often exacerbating health inequities in the first year of life [3]. Although primary care plays a vital role in mitigating these risks, clinicians often lack precise tools to identify which infants require the most intensive post-discharge monitoring [4]. To address this gap, researchers recently evaluated whether machine learning models applied to neonatal clinical data can accurately flag infants at the highest risk for rehospitalization, potentially allowing pediatricians to target early interventions and closer outpatient surveillance.