Analysis of Common Opioid Dependency Risk Factors in Rural Chronic Pain Patients Using KnowledgeGraph-Based Semantic Modeling

Abstract

Chronic pain is a silent epidemic that ensnares individuals in a vicious cycle of physical agony and mental anguish. The ripple effects of chronic pain extend beyond the physical aspect, as chronic pain patients are markedly more susceptible to anxiety disorders, depression, and a host of other chronic conditions. The burden is heavy, and chronic pain patients face increased medical expenses, psychological distress, and a diminished perception of their general health status. In addition, the serious issue of opioid addiction and mortality exacerbates the situation as opioid seems to be the only approachable solution for patients to seek relief from their suffering. This study aims to analyze common attributes among rural chronic pain patients with high risk of opioid addiction using the knowledge graph framework. Through converting the 2021 National Health Interview Survey data into an ontology model using Neo4j, a graph database management system that stores and queries interconnected data as nodes and relationships, we could identify complex patterns and associations within the dataset. The study revealed that high-risk chronic pain patients demonstrated significantly elevated centrality in musculoskeletal conditions (66.3% higher hip pain, 59.0% higher back pain, 48.1% higher arthritis rates) and psychological factors (33.7% higher anxiety disorders, 32.5% higher depression treatment rates). The demographic profile showed these patients were predominantly economically disadvantaged, married, obese women in their 60s living in southern regions with significant medical bill concerns.

Key Words: Semantic Knowledge Graph, Biopsychosocial Model, Ontology, Pain informatics