MyDigiTwin: A privacy-preserving framework for personalized cardiovascular risk prediction and scenario exploration

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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Publication Details

Journal
Computers in Biology and Medicine
Published
2025-10-09
DOI
https://doi.org/10.1016/j.compbiomed.2025.111180
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
0.00

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article

MyDigiTwin: A privacy-preserving framework for personalized cardiovascular risk prediction and scenario exploration

Pim van der Harst, Esther E. Bron, Bauke Arends, Héctor Cadavid et al.
Computers in Biology and Medicine
Machine Learning in Healthcare
article

MyDigiTwin: A privacy-preserving framework for personalized cardiovascular risk prediction and scenario exploration

Pim van der Harst, Esther E. Bron, Bauke Arends, Héctor Cadavid, Katarzyna Dziopa, Sonja Georgievska, Hyunho Mo, Daniël Bos
article en

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.

Computers in Biology and MedicineVol. 198(Pt A)
Heidelberg University (DE), Erasmus MC (NL), University Hospital Heidelberg (DE), University Medical Center Utrecht (NL), Netherlands eScience Center (NL), Amsterdam University Medical Centers (NL), University College London (GB), University of Amsterdam (NL), Erasmus University Rotterdam (NL)
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Openalex Percentile: Top 85%
Machine Learning in Healthcare
DOIOpen Access PDFOpenAlex
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MyDigiTwin: A privacy-preserving framework for personalized cardiovascular risk prediction and scenario exploration — Pim van der Harst, Esther E. Bron, et al. · Computers in Biology and Medicine (2025) | TGRS Research Map | TGRS