AI-Driven Digital Twin Framework for Predictive Healthcare

There is a necessity for advanced models for predictive and personalized patient care due to the intricacy of health care systems. Predicting and improving healthcare outcomes are suggested in this study through an AI-Driven Digital Twin Framework that is a combination of virtual patient simulation and live Electronic Health Record (EHR) data. Deep learning models, such as Reinforcement Learning (RL) in simulating patient health time-series paths and Recurrent Neural Networks (RNNs) for forecasting time-series, are used in the system. In real-time, the data from EHRs is uploaded into the virtual twin to enable dynamic, precise simulations of patient states over time. The proposed approach outperforms traditional predictive models like logistic regression (accuracy: 85.2%) and decision trees (accuracy: 81.6%) for predicting long-term health outcomes with an accuracy of 93.7% when used on a dataset of more than 100,000 patient records. The model is effective in risk stratification and early disease detection, as evidenced by its F1-score of 0.92, precision of 94.5%, and recall of 92.8%. Moreover, the technology gives clinicians real-time decision support, allowing proactive intervention as well as tailored recommendations for treatment. The study shows how the AI-driven digital twin paradigm can revolutionize healthcare through improved clinical prediction accuracy, ease of treatment planning, and improved patient monitoring.