Topological and Graph-Theoretic Models for Analyzing Pediatric Disease Networks and Surgical Outcomes

Authors

  • Salma Jabeen
  • Sameena Bano
  • Mohammed Kaleem
  • Nivethitha. K
  • Sandeep C. S
  • D. Rajinigirinath

Keywords:

Graph Theory, Topological Data Analysis, Pediatric Diseases, Disease Network, Surgical Outcomes, Persistent Homology, Centrality, Network Science, Data Modeling

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.

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References

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Sizemore, A. E., et al. (2018). Topological data analysis as a morphometric method: Using persistent homology to demarcate closely related neuroanatomical structures. Brain Structure and Function.

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Published

2025-07-29

How to Cite

1.
Jabeen S, Bano S, Kaleem M, K N, C. S S, Rajinigirinath D. Topological and Graph-Theoretic Models for Analyzing Pediatric Disease Networks and Surgical Outcomes. J Neonatal Surg [Internet]. 2025Jul.29 [cited 2025Sep.20];14(32S):6530-42. Available from: https://www.jneonatalsurg.com/index.php/jns/article/view/8606

Issue

Vol. 14 No. 32S (2025): Journal of Neonatal Surgery

Section

Original Article

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