Available at: https://digitalcommons.calpoly.edu/theses/2846
Date of Award
6-2024
Degree Name
MS in Computer Science
Department/Program
Computer Science
College
College of Engineering
Advisor
Theresa Migler
Advisor Department
Computer Science
Advisor College
College of Engineering
Abstract
Chronic pain is a pervasive health issue, affecting a significant portion of the population and posing complex challenges due to its diverse etiology and individualized impact. To address this complexity, there is a growing interest in grouping chronic pain patients based on their unique treatment needs. While various methodologies for patient grouping have emerged, leveraging graph-based approaches to produce and evaluate such groupings remains largely unexplored. Recent studies have shown promise in integrating knowledge graphs into exploring patient similarity across different biological domains, indicating potential avenues for research. Additionally, there is a growing interest in investigating patient similarity networks, highlighting the importance of innovative approaches to understanding chronic pain.
Graphs offer a transparent and easily interpretable framework for analyzing patient classifications, providing valuable insights into underlying patterns and connections. By leveraging graph theory, this thesis proposes a novel approach to address the terminological disparities that exist across disciplines studying chronic pain. By constructing a graph of pain-related terminology sourced from interdisciplinary literature, we aim to facilitate link prediction and clarify connections among disparate terminologies. This approach seeks to bridge disciplinary divides, fostering a cohesive understanding of chronic pain and promoting collaborative efforts toward effective management and treatment strategies.
Through the integration of graph theory and interdisciplinary research, this thesis contributes to advancing our understanding of chronic pain and lays the groundwork for future explorations in patient grouping and treatment optimization by proposing a graph-based clustering method as well as a method for evaluating the robustness of a cluster.