Towards Next-Generation Digital Twins for Diabetes

Background information: Since diabetes is a complex disease, the conventional care approach fails and demands an individualized strategy. The study proposes an advanced digital twin framework that combines RNNs, synthetic data, GLAV, PPO, and VR for enhancing patient engagement and real-time monitoring. Methods: The system will integrate data using GLAV, optimize treatment using PPO, predict glucose using RNNs, use PHKG for structured data, VR for immersive teaching, and synthetic data for privacy, making real-time tailored care possible. Objectives: The aim is to have a customized diabetes care system with smooth data integration, better glucose prediction, optimum treatments, and improved patient engagement. Results: The model improved glucose prediction, therapy optimization, and patient engagement at 93.5% accuracy compared to conventional techniques. Conclusion: The digital twin framework provides proactive, individualized diabetes care by using patient-centered, predictive solutions to overcome traditional constraints.