Advancing radiation therapy safety in cancer-care: Leveraging AI for adverse reaction prediction

Abstract

The development of advanced technologies for radiation cancer treatment plays a pivotal role in anticipating potential complications, safeguarding patient well-being, and elevating treatment satisfaction. Access to curated medical data coupled with specialized knowledge in radiation oncology is indispensable for equipping healthcare professionals with the insights needed to make informed treatment decisions. This study endeavors to provide systematic support to practitioners in predicting personalized side effects of radiation oncology treatments before their administration to patients. For this, a pioneering ontology-based methodology for evidence-based medicine is introduced, drawing upon information and evidence from various scientific outlets. This approach utilizes semantic modeling and rule-based reasoning to assess radiation therapy pathways, ensuring adjustments are made when necessary to mitigate health risks and enhance safety. Focused on radiation oncology, the intricacies of knowledge are simplified and validated through real reported cases obtained from scientific literature. The resulted predictive model demonstrated a notable compatibility rate of 76.7% with real reported risks, surpassing conventional methods by 4% during testing achieves 89% success with single therapies but faces challenges with complex regimens. Data enrichment may increase accuracy by 20% over five years. Current method accuracy is 92%, with potential for 97% through quantum-inspired methods. Testing reveals 80.1% compatibility, exceeding conventional approaches by 3%. These findings hold significant potential of ontology-based models for revolutionizing radiation oncology treatment strategies in the realm of radiation protection.