Combined In-silico and Machine Learning Approaches Toward Predicting Arrhythmic Risk in Post-infarction Patients

Background: Remodeling due to myocardial infarction (MI) significantly increases patient arrhythmic risk. Simulations using patient-specific models have shown promise in predicting personalized risk for arrhythmia. However, these are computationally- and time- intensive, hindering translation to cli...

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Bibliographic Details

Main Authors:	Mary M. Maleckar, Lena Myklebust, Julie Uv, Per Magne Florvaag, Vilde Strøm, Charlotte Glinge, Reza Jabbari, Niels Vejlstrup, Thomas Engstrøm, Kiril Ahtarovski, Thomas Jespersen, Jacob Tfelt-Hansen, Valeriya Naumova, Hermenegild Arevalo
Format:	article
Language:	EN
Published:	Frontiers Media S.A. 2021
Subjects:	patient-specific modeling computational cardiology machine learning in cardiology modeling and simulation biophysical modeling data augmentation Physiology QP1-981
Online Access:	https://doaj.org/article/ba337637c73442f38f506a71d7c4dc66
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