Machine learning-based prediction of hemodynamically significant patent ductus arteriosus in preterm neonates: a pioneering insight

The Random Forest model effectively predicts hPDA risk in premature infants, offering superior predictive power compared to classical statistical analyses. This approach has the potential to enhance early detection, facilitate timely interventions, and support personalized treatment strategies, thereby improving patient outcomes. Further validation through large-scale, multi-center prospective studies is essential for its integration into clinical practice.