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

TL;DR

MyDigiTwin is a privacy-preserving framework that combines digital twins and federated learning to enable personalized cardiovascular risk prediction and scenario exploration while safeguarding patient data privacy.

Contribution

It introduces a novel framework integrating digital twins with federated learning and data harmonization for personalized, privacy-preserving cardiovascular risk assessment.

Findings

Feasibility demonstrated through a proof-of-concept study.

Effective data harmonization across diverse health datasets.

Scalable approach for personalized cardiovascular care.

Abstract

Cardiovascular disease (CVD) remains a leading cause of death, and primary prevention through personalized interventions is crucial. This paper introduces MyDigiTwin, a framework that integrates health digital twins with personal health environments to empower patients in exploring personalized health scenarios while ensuring data privacy. MyDigiTwin uses federated learning to train predictive models across distributed datasets without transferring raw data, and a novel data harmonization framework addresses semantic and format inconsistencies in health data. A proof-of-concept demonstrates the feasibility of harmonizing and using cohort data to train privacy-preserving CVD prediction models. This framework offers a scalable solution for proactive, personalized cardiovascular care and sets the stage for future applications in real-world healthcare settings.