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Deep learning for health informatics

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Title: Deep learning for health informatics
Authors: Ravi, D
Wong, C
Deligianni, F
Berthelot, M
Andreu-Perez, J
Lo, B
Yang, G
Item Type: Journal Article
Abstract: With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reshape the future of artificial intelligence. Rapid improvements in computational power, fast data storage and parallelization have also contributed to the rapid uptake of the technology in addition to its predictive power and ability to generate automatically optimized high-level features and semantic interpretation from the input data. This article presents a comprehensive up-to-date review of research employing deep learning in health informatics, providing a critical analysis of the relative merit and potential pitfalls of the technique as well as its future outlook. The paper mainly focuses on key applications of deep learning in the fields of translational bioinformatics, medical imaging, pervasive sensing, medical informatics and public health.
Issue Date: 1-Jan-2017
Date of Acceptance: 2-Dec-2016
URI: http://hdl.handle.net/10044/1/42964
DOI: https://dx.doi.org/10.1109/JBHI.2016.2636665
ISSN: 2168-2208
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Start Page: 4
End Page: 21
Journal / Book Title: IEEE Journal of Biomedical and Health Informatics
Volume: 21
Issue: 1
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: EP/L014149/1
EP/M000257/1
EP/N027132/1
Keywords: Science & Technology
Technology
Life Sciences & Biomedicine
Computer Science, Information Systems
Computer Science, Interdisciplinary Applications
Mathematical & Computational Biology
Medical Informatics
Computer Science
Bioinformatics
deep learning
health informatics
machine learning
medical imaging
public health
wearable devices
CONVOLUTIONAL NEURAL-NETWORKS
BIG DATA
RECOGNITION
SEGMENTATION
ARCHITECTURE
MODEL
CLASSIFICATION
MEDICINE
SEQUENCE
MRI
Publication Status: Published
Appears in Collections:Department of Surgery and Cancer
Computing
Faculty of Engineering



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