Digital Twin Enabled Deep Learning System for Predictive Monitoring of Cardiovascular Health

Abstract

Cardiovascular diseases (CVDs) are a major cause of death globally, that require timely and personalized monitoring. This research introduces a Digital Twin Enabled Deep Learning (DT-DL) framework by incorporating an open source cardiovascular model and data driven deep learning in predictive and interpretable health monitoring. The proposed system integrates a physiological digital twin, multimodal sensor integration layer, deep residual predictor, and uncertainty quantification module into a closed loop real time feedback. The digital twin models hemodynamic dynamics with a time dependent elastance model, whilst a Temporal Convolutional Network learns residual corrections and delivers uncertainty informed predictions. Model parameters are adapted in real time through Unscented Kalman Filtering such that individual patient physiology is tracked. Empirical studies on multimodal datasets (MIMIC-IV, PTB-XL, and WESAD) show improvements in terms of both accuracy and calibration over pure deep or mechanistic baselines. Modeling a hybrid system attains a 39% RMSE reduction with an AUC of 0.94 over standalone models, and is physiologically consistent and interpretable. These findings demonstrate the promising value of hybrid digital twin intelligence in real world hospital environments for continuous cardiovascular monitoring and early risk prognosis.

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