Self-Adaptive Digital Twins for Patient Health Dynamics: A Learning-Driven Simulation Framework

This paper presents a self-adaptive Digital Twin framework for real-time prediction and management of patient health dynamics. The system integrates medical history with live data from wearable IoT devices to create a continuously updated virtual patient model. Machine learning-driven disease-specific models and drug-response simulations are used to forecast outcomes and assess risks before clinical application. The framework continuously learns from new patient data, enabling rapid adaptation to changing health conditions. An embedded decision-support tool guides optimal intervention selection, while privacy is ensured through secure communication and federated learning. Experimental results show improved prediction accuracy and more personalized, risk-aware clinical decision-making compared to conventional monitoring systems.