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  • Multiscale Cohort Modeling of Atrial Electrophysiology : Risk Stratification for Atrial Fibrillation through Machine Learning on Electrocardiograms

    Claudia Nagel

    Band 27 von Karlsruhe transactions on biomedical engineering
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    An early detection and diagnosis of atrial fibrillation sets the course for timely intervention to prevent potentially occurring comorbidities. Electrocardiogram data resulting from electrophysiological cohort modeling and simulation can be a valuable data resource for improving automated atrial fibrillation risk stratification with machine learning techniques and thus, reduces the risk of stroke in affected patients.

    Umfang: XII, 252 S.

    Preis: 49.00 €

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    Empfohlene Zitierweise
    Nagel, C. 2023. Multiscale Cohort Modeling of Atrial Electrophysiology : Risk Stratification for Atrial Fibrillation through Machine Learning on Electrocardiograms. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000155927
    Nagel, C., 2023. Multiscale Cohort Modeling of Atrial Electrophysiology : Risk Stratification for Atrial Fibrillation through Machine Learning on Electrocardiograms. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000155927
    Nagel, C. Multiscale Cohort Modeling of Atrial Electrophysiology : Risk Stratification for Atrial Fibrillation Through Machine Learning on Electrocardiograms. KIT Scientific Publishing, 2023. DOI: https://doi.org/10.5445/KSP/1000155927
    Nagel, C. (2023). Multiscale Cohort Modeling of Atrial Electrophysiology : Risk Stratification for Atrial Fibrillation through Machine Learning on Electrocardiograms. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000155927
    Nagel, Claudia. 2023. Multiscale Cohort Modeling of Atrial Electrophysiology : Risk Stratification for Atrial Fibrillation Through Machine Learning on Electrocardiograms. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000155927




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    Weitere Informationen

    Veröffentlicht am 24. April 2023

    Sprache

    Englisch

    Seitenanzahl:

    280

    ISBN
    Paperback 978-3-7315-1281-3

    DOI
    https://doi.org/10.5445/KSP/1000155927