ORIGINAL RESEARCH article
Front. Pain Res.
Sec. Pain Research Methods
Volume 6 - 2025 | doi: 10.3389/fpain.2025.1573465
Modeling Chronic Pain Interconnections Using Bayesian Networks: Insights from the Qatar Biobank Study
Provisionally accepted
- 1Qatar University, Doha, Qatar
- 2College of Medicine, Qatar University, Doha, Qatar
- 3QU Health, Qatar University, Doha, Qatar
- 4College of Arts and Sciences, Qatar University, Doha, Qatar
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This study examines the interdependencies among different chronic pain locations and their relationships with age and gender, critical for effective clinical strategies. A Bayesian network approach was applied to 2,400 adult participants (18+ years, 50% male, 50% female) from the Qatar Biobank (QBB). They were categorized into young (18-35, 40.9%), middle-aged (36-60, 50.6%), and seniors (61+, 8.5%). The model identified direct and indirect associations among pain locations and with demographic factors, quantified by odds ratios (ORs).Younger females had the highest probability of headaches or migraines (48.6%) compared to younger males (31.2%), with probabilities decreasing across age (OR: 1.917, 95% CI: 1.609-2.284). Hand pain strongly correlated with hip pain (OR: 8.691, 95% CI: 6.074-12.434) and neck or shoulder pain (OR: 4.451, 95% CI: 3.302-6.000). Back pain was a key predictor of systemic pain, with a 37.9% probability of generalized pain when combined with hand pain (OR: 7.682, 95% CI: 5.293-11.149), dropping to 6.6% for back pain alone. Age, back pain, and foot pain collectively influenced knee pain, which reached 72.7% in older individuals with foot and back pain (OR: 4.759, 95% CI: 3.704-6.114).These Bayesian network parameters explicitly reveal probabilistic interdependencies among pain locations, suggesting targeted interventions for key anatomical regions could effectively mitigate broader chronic pain networks. Additionally, the model elucidates how demographic predispositions influence downstream pain patterns, providing a clear and actionable framework for personalized chronic pain management strategies. The findings support targeted diagnostic and therapeutic strategies for specific demographic groups. By revealing key predictors and interconnections, this approach offers a framework for understanding pain propagation, enhancing early detection, and guiding more personalized treatment.
Keywords: ayesian Network, Qatar Biobank (QBB), Pain Interdependencies, Conditional probabilities, Systemic Pain, Probabilistic Modeling
Received: 09 Feb 2025; Accepted: 28 Apr 2025.
Copyright: © 2025 Al-Khinji and Malouche. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Aisha Ahmad M A Al-Khinji, Qatar University, Doha, Qatar
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