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230811 ||| eng |
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|a KSP/1000155927
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|a Nagel, Claudia
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|a Multiscale Cohort Modeling of Atrial Electrophysiology
|h Elektronische Ressource
|b Risk Stratification for Atrial Fibrillation through Machine Learning on Electrocardiograms
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| 260 |
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|b KIT Scientific Publishing
|c 2023
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| 300 |
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|a 280 p.
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| 653 |
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|a Electrophysiologische Modellierung und Simulation; Elektrokardiogramm; Maschinelles Lernen; Vorhofflimmern; Statistisches Shape Modell; electrophysiological modeling and simulation; electrocardiogram; machine learning; atrial fibrillation; statistical shape model
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|a Electrical engineering
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OAPEN
|a OAPEN
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|a Karlsruhe transactions on biomedical engineering
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by-sa/4.0/
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|a 10.5445/KSP/1000155927
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|u https://library.oapen.org/bitstream/id/4850408b-3a51-4294-86c3-8fe69ec35e89/multiscale-cohort-modeling-of-atrial-electrophysiology-risk-stratification-for-atrial-fibrillation-through-machine-learning-on-electrocardiograms.pdf
|x Verlag
|3 Volltext
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|u https://library.oapen.org/handle/20.500.12657/62899
|z OAPEN Library: description of the publication
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|a 620
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|a 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.
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