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

Héctor Cadavid, Hyunho Mo, Bauke Arends, Katarzyna Dziopa, Esther E. Bron +3 more
1/21/2025
cs.LGcs.HC

No official code found. Best implementation: MyDigiTwinNL/FedAvg_vantage6 (0 stars, last commit 2026-02-16).

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.

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Code Implementations(1)

MyDigiTwinNL/FedAvg_vantage6Community100%

Implementation of FedAvg on vantage6

01Shell, PythonNov 8, 20248 months ago

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Cite this paper

@article{cadavid2025mydigitwin,
  title  = {MyDigiTwin: A Privacy-Preserving Framework for Personalized Cardiovascular Risk Prediction and Scenario Exploration},
  author = {Héctor Cadavid and Hyunho Mo and Bauke Arends and Katarzyna Dziopa and Esther E. Bron and Daniel Bos and Sonja Georgievska and Pim van der Harst},
  year   = {2025},
  eprint = {2501.12193},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.12193v1}
}

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