Keywords: Machine Learning/Artificial Intelligence, Heart, Deep Learning; Heart FailureWe proposed a multi-source deep-learning model including traditional functional parameters and myocardial strain derived from cardiovascular magnetic resonance, as well as clinical features such as laboratory tests, electrocardiograms, and echocardiography. Meanwhile, we innovatively integrated cardiac motion characteristics through deep learning algorithms and fast neural convolution networks to construct deep learning heart failure prediction model. The results showed that compared with the traditional cox model, the deep learning model had higher efficacy for prognosis evaluation in patients with heart failure and could provide risk stratification in patients with heart failure, which may further guide clinical decision-making.
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