Research Data Management in Medical Data Science:Integrating Multi-Source Data for Cardiological Simulations and Machine Learning: Challenges in Security, Privacy, and Legal Frameworks

This presentation was given at the E-Science-Tage 2025 at the University of Heidelberg. It highlighted the potential technical and legal challenges, as well as the benefits, of using electrophysiological models, known as digital twins in biomedical engineering, to generate synthetic data such as electrocardiograms.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-13
DOI
https://doi.org/10.5281/zenodo.15037434
Primary Topic
ECG Monitoring and Analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

Research Data Management in Medical Data Science:Integrating Multi-Source Data for Cardiological Simulations and Machine Learning: Challenges in Security, Privacy, and Legal Frameworks

Zenodo (CERN European Organization for Nuclear Research)
ECG Monitoring and Analysis
article

Research Data Management in Medical Data Science:Integrating Multi-Source Data for Cardiological Simulations and Machine Learning: Challenges in Security, Privacy, and Legal Frameworks

article en

Abstract

This presentation was given at the E-Science-Tage 2025 at the University of Heidelberg. It highlighted the potential technical and legal challenges, as well as the benefits, of using electrophysiological models, known as digital twins in biomedical engineering, to generate synthetic data such as electrocardiograms.

Zenodo (CERN European Organization for Nuclear Research)
Karlsruhe Institute of Technology (DE), FIZ Karlsruhe – Leibniz Institute for Information Infrastructure (DE)
Openalex Percentile: Top 41%
ECG Monitoring and Analysis
DOIOpen Access PDFOpenAlex
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