HEALTHTWIN-AI: A DIGITAL TWIN-BASED PREDICTIVE MODELING FRAMEWORK FOR PERSONALIZED HEALTHCARE

Abstract

As healthcare becomes more interconnected and as more data is available, the demand for accurate, timely, and individualized care will increase. The gap becomes especially evident when considering the case predicaments for each and every patient. Most predictive healthcare models fall short, and they model parameters in isolation over time. The present study aims to bridge the existing gap and proposes the HealthTwin-AI model as an example of Digital Twin-based predictive modeling in the realm of healthcare. The model constructs individualized digital copies of patients by utilizing Electronic Health Record data, signal data, and laboratory and lifestyle data, to name a few. The HealthTwin-AI model also adopts a phygital model and outperforms temporal deep learning as well as latency-based reinforcement learning. Getting the digital twin to evolve as the state of the patient changes becomes possible. The advanced joint learning of phygital deep learning enables predictive modeling of patient mirrors. The HealthTwin-AI model operates on jurisdictional rule-based advanced phygital modeling of the digital twin, using risk-based scoring and advanced time-ordered patients to predict the state of the patient, the treatment, and the evolution of the disease, all of which are the sources of treatment. Multiple high-dimensional, large datasets populated by artificially generated clinic data were used to evaluate the model, and the model was assessed on the basis of advanced order, improved, and time-sensitive risk stratification. Before reinforced learning was integrated, an average of 25,000 patients earned an overall predictive accuracy of 92% across the class. The phygital modeling also achieved a 14.2% improvement, and the advanced temporal phygital modeling achieved a predictive time improvement of 22% and 18% for the treatment and evolution of the disease, respectively. HealthTwin-AI embodies the shift from static predictive healthcare models and towards the bridging gap of adaptive personalized healthcare. The proposed framework is a flexible, intelligent answer to the forthcoming digital healthcare systems because it employs continuous monitoring, accurate forecasting, and individualized treatment planning.