DefinePK

Abstract

Pediatric disease analysis requires a comprehensive understanding of how illnesses and treatment responses interact across individuals. Traditional statistical models often fall short in capturing the complexity of such interactions. This paper introduces a topological and graph-theoretic approach to model and analyze pediatric disease networks. We explore how topological data analysis (TDA), graph theory, and network science can be integrated to identify disease patterns, predict outcomes, and optimize surgical interventions. Through case studies and computational simulations, the study reveals how persistent homology, centrality measures, and community detection improve our ability to decode complex disease interactions and surgical prognoses.