AI-Driven Precision Identification of Rare Disease Patients and Effectiveness Analysis of Personalized Marketing Strategies
Zhenghao Pan · Applied and Computational Engineering · 2025
Rare diseases affect millions of patients worldwide, presenting significant challenges in patient identification and targeted marketing of therapeutic interventions. This research proposes an artificial intelligence-driven framework for precise identification of rare disease patients and develops personalized marketing strategies to enhance treatment accessibility. The study integrates multi-source data including electronic health records, social media patterns, and search behaviors to construct machine learning models capable of identifying potential rare disease patients with 89.2% accuracy across five disease categories. The personalized marketing strategies demonstrated a 73% improvement in patient engagement rates and 68% increase in treatment awareness compared to traditional broadcasting approaches. The framework addresses critical gaps in rare disease patient outreach while maintaining ethical standards for data privacy. Results indicate substantial potential for AI-enhanced precision marketing to improve healthcare resource allocation efficiency and accelerate therapeutic adoption among rare disease populations, contributing to reduced diagnostic delays and enhanced patient outcomes.