New Trends of Deep Learning in Clinical Cardiology

(E-pub Ahead of Print)

Author(s): Zichao Chen, Qi Zhou, Aziz Khan Turlandi, Jordan Jill, Rixin Xiong, Xu Liu*

Journal Name: Current Bioinformatics


Become EABM
Become Reviewer
Call for Editor

Abstract:

Deep Learning (DL) is a novel type of Machine Learning (ML) model. It is showing increasing promise in medicine, study and treatment of diseases and injuries, to assist in data classification, novel disease symptoms and complicated decision making. Deep learning is the form of machine learning typically implemented via multi-level neural networks. This work discuss the pros and cons of using DL in clinical cardiology that also apply in medicine in general, while proposing certain directions as the more viable for clinical use. DL models called deep neural networks (DNNs), recurrent neural networks (RNNs) and convolutional neural networks (CNNs) 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 the future, more novel deep learning methods are expected to make a difference in the field of clinical medicines.

Keywords: Clinical, Cardiology, Artificial Intelligent, Machine Learning, Deep Leaning, Neural Networks

Rights & PermissionsPrintExport Cite as

Article Details

(E-pub Ahead of Print)
DOI: 10.2174/1574893615999200719234517
Price: $95

Article Metrics

PDF: 52