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Deep learning (DL) is a branch of machine learning (ML). It is showing increasing promise in medicine, to assist in data classification, novel disease phenotyping and complex decision making. Deep learning is a form of ML typically implemented via multi-layered neural networks. This work discuss the advantages and disadvantages of applying deep learning in clinical cardiology that also apply in medicine in general, while proposing certain directions as the more viable for clinical use. Deep learning models called deep neural networks (DNNs), convolutional neural networks(CNNs) and recurrent neural networks(RNNs) have been applied to arrhythmias, electrocardiogram, ultrasonic analysis, genomes and endomyocardial biopsy. Convincingly, the rusults of trained model are good, demonstrating the power of more expressive deep learning algorithms for clinical predictive modeling. In future, more novel deep learning methods are expected to make a difference in the field of clinical medicines.

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This page is a summary of: New Trends of Deep Learning in Clinical Cardiology, Current Bioinformatics, October 2021, Bentham Science Publishers, DOI: 10.2174/1574893615999200719234517.
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