Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations

JMIR MHealth and UHealth
Igbe ToboreLei Wang

Abstract

The use of deep learning (DL) for the analysis and diagnosis of biomedical and health care problems has received unprecedented attention in the last decade. The technique has recorded a number of achievements for unearthing meaningful features and accomplishing tasks that were hitherto difficult to solve by other methods and human experts. Currently, biological and medical devices, treatment, and applications are capable of generating large volumes of data in the form of images, sounds, text, graphs, and signals creating the concept of big data. The innovation of DL is a developing trend in the wake of big data for data representation and analysis. DL is a type of machine learning algorithm that has deeper (or more) hidden layers of similar function cascaded into the network and has the capability to make meaning from medical big data. Current transformation drivers to achieve personalized health care delivery will be possible with the use of mobile health (mHealth). DL can provide the analysis for the deluge of data generated from mHealth apps. This paper reviews the fundamentals of DL methods and presents a general view of the trends in DL by capturing literature from PubMed and the Institute of Electrical and Electronics Eng...Continue Reading

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Feb 15, 2020·Sensors·Beanbonyka RimMin Hong
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Methods Mentioned

BETA
feature extraction
RNA-seq
scRNA-seq
RNA-
seq

Software Mentioned

neon
mPower app
Apache Spark
SpiNNaker TrueNorth
NuPIC
Nervana Systems
MITOS
DBN
Caffe
mPower

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