Advanced computational models now calculate individualized bleeding and mortality risks for preterm infants receiving platelet transfusions, adjusting for both fixed patient characteristics and changing clinical conditions over time. The modeling approach addresses confounding variables that traditional analysis methods miss in transfusion timing decisions. This represents a significant advancement in neonatal intensive care, where platelet transfusion decisions have historically relied on population-based thresholds rather than individualized risk assessment. Preterm infants face unique bleeding risks due to immature hemostatic systems, making optimal transfusion timing critical for outcomes. The dynamic modeling framework could reduce both under-transfusion (increasing bleeding risk) and over-transfusion (potentially causing complications like necrotizing enterocolitis). However, clinical implementation requires validation across diverse NICU populations and integration with existing electronic health systems. The approach may herald broader personalized medicine applications in critical care, where real-time risk stratification could optimize interventions beyond transfusion protocols. Success depends on model accuracy across different gestational ages and clinical scenarios.