MyDigiTwin: A Privacy-Preserving Framework for Personalized Cardiovascular Risk Prediction and Scenario Exploration

Cardiovascular disease (CVD) remain a leading cause of deaths worldwide, with many diagnoses occurring only after the onset of symptoms. This reactive, symptom-driven approach may hinder timely interventions, emphasizing secondary prevention -- managing conditions after they arise -- over proactive primary prevention. Primary prevention, which involves identifying at-risk individuals before symptoms manifest and empowering them to act on modifiable risk factors, such as lifestyle and diet, has the potential to significantly reduce cardiovascular events and alleviate healthcare burdens [3]. Achieving this, however, requires innovative frameworks that integrate personalized data, predictive models, and actionable insights to address the complexities of cardiovascular health management. One promising innovation for advancing primary prevention is the concept of health digital twin. Health digital twins provide virtual representations of patient-specific conditions, enabling personalized risk assessments, outcome predictions, and scenario exploration. By integrating predictive modeling with patient-specific data, this concept offer the potential to shift CVD prevention from a reactive approach to a proactive, data-driven strategy. However, implementing this type of digital twin at scale for CVD prevention poses several challenges [37, 11, 36], among which: - Data integration and harmonization: Health data is often distributed across various systems and formats, which requires a data unification procedure when attempting to integrate it into a cohesive predictive framework.