MyDigiTwin: A Privacy-Preserving Framework for Personalized Cardiovascular Risk Prediction and Scenario Exploration
Fuente:
arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916998880952320 |
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| author | Cadavid, Héctor Mo, Hyunho Arends, Bauke Dziopa, Katarzyna Bron, Esther E. Bos, Daniel Georgievska, Sonja van der Harst, Pim |
| author_facet | Cadavid, Héctor Mo, Hyunho Arends, Bauke Dziopa, Katarzyna Bron, Esther E. Bos, Daniel Georgievska, Sonja van der Harst, Pim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_12193 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MyDigiTwin: A Privacy-Preserving Framework for Personalized Cardiovascular Risk Prediction and Scenario Exploration Cadavid, Héctor Mo, Hyunho Arends, Bauke Dziopa, Katarzyna Bron, Esther E. Bos, Daniel Georgievska, Sonja van der Harst, Pim Machine Learning Human-Computer Interaction 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. |
| title | MyDigiTwin: A Privacy-Preserving Framework for Personalized Cardiovascular Risk Prediction and Scenario Exploration |
| topic | Machine Learning Human-Computer Interaction |
| url | https://arxiv.org/abs/2501.12193 |