Abstract
Sexually transmitted diseases (STDs) significantly impact public health, affecting one in five U.S. adults and imposing substantial economic burdens. Many at-risk individuals seek information and support online rather than through regular testing, facing challenges due to fragmented information and a lack of personalized recommendations. This study develops an online health recommendation system (OHRS) tailored for STD patients, integrating informational and emotional support based on their disease journey. Using a BERT-based named entity recognition (NER) algorithm, the system identifies patient emotions and stages from online posts, providing relevant support resources. Data collection for designing a recommendation engine included analyzing online posts and consulting healthcare experts. The system's effectiveness was validated through user-based simulation studies. Key contributions include developing text-mining algorithms, creating a knowledge-based recommendation system, and proposing design principles for AI-driven support systems for stigmatized conditions.
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