Automated Cytokine Profiling for Personalized Immunotherapy Response Prediction via HyperScore-Driven Multi-Modal Data Fusion

**Abstract:** Predicting individual patient responses to immunotherapy remains a critical challenge in oncology. This paper introduces a novel framework for automated cytokine profiling and response prediction leveraging a multi-modal data fusion approach driven by a HyperScore evaluation metric. Our system ingests and normalizes diverse data sources, including flow cytometry, ELISA, and transcriptomic data, decomposes them into semantic structures, applies rigorous logical consistency and execution verification, quantifies novelty, and forecasts treatment impact. The resultant HyperScore, reflecting a weighted combination of these criteria, enables personalized immunotherapy response prediction with significantly improved accuracy compared to existing methods. This technology has the potential to accelerate drug development, optimize treatment strategies, and improve patient outcomes.