Using a discrete mathematics approach, distinct BPS/IC phenotypes and personalized treatment targets are revealed.

This study identified subgroups of bladder pain syndrome/interstitial cystitis (BPS/IC) patients and potential treatment targets by combining validated questionnaires and patient diaries with discrete mathematical techniques. Hierarchical clustering of questionnaire data revealed three distinct patient groups. Analysis of patient diaries, employing natural language processing—a form of discrete data analysis—found keywords capturing emotional and psychological experiences, complementing the questionnaire results. Integration of questionnaire and diary data visualized the relationships between symptoms and treatment targets through a network graph. This personalized approach, akin to solving the traveling salesman problem in discrete mathematics, was validated through case studies, demonstrating its utility in guiding targeted interventions. The study emphasizes the significant potential of discrete mathematics-based data integration and visualization for personalized management of this complex condition.