DefinePK

Abstract

Congenital disorders are a significant cause of neonatal morbidity and mortality worldwide. Predicting the progression of these conditions and planning effective surgical interventions require the integration of advanced mathematical frameworks. This paper explores the application of algebraic graph theory and differential systems to model and analyze neonatal health networks, focusing on congenital disorders. Algebraic graph theory provides tools for representing complex interactions among anatomical and physiological systems, while differential systems enable the dynamic modeling of disease progression and response to surgical treatment. By combining these two domains, we present a unified mathematical approach that enhances the prediction of outcomes, identifies critical biomarkers, and optimizes surgical planning. The study also discusses computational implementation, clinical validation, and the potential for integration into neonatal decision-support systems. Our findings suggest that this interdisciplinary methodology offers substantial improvements in the precision and personalization of neonatal care.