A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues

In the last few years, the application of Machine Learning approaches like Deep Neural Network (DNN) models have become more attractive in the healthcare system given the rising complexity of the healthcare data. Machine Learning (ML) algorithms provide efficient and effective data analysis models to uncover hidden patterns and other meaningful information from the considerable amount of health data that conventional analytics are not able to discover in a reasonable time. In particular, Deep Learning (DL) techniques have been shown as promising methods in pattern recognition in the healthcare systems. Motivated by this consideration, the contribution of this paper is to investigate the deep learning approaches applied to healthcare systems by reviewing the cutting-edge network architectures, applications, and industrial trends. The goal is first to provide extensive insight into the application of deep learning models in healthcare solutions to bridge deep learning techniques and human healthcare interpretability. And then, to present the existing open challenges and future directions.

[1]  Li Li,et al.  Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records , 2016, Scientific Reports.

[2]  Xinbo Gao,et al.  A parasitic metric learning net for breast mass classification based on mammography , 2018, Pattern Recognit..

[3]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[4]  Byunghan Lee,et al.  Deep learning in bioinformatics , 2016, Briefings Bioinform..

[5]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[6]  Jimeng Sun,et al.  Using recurrent neural network models for early detection of heart failure onset , 2016, J. Am. Medical Informatics Assoc..

[7]  Farshad Firouzi,et al.  Internet-of-Things and big data for smarter healthcare: From device to architecture, applications and analytics , 2018, Future Gener. Comput. Syst..

[8]  Ah Chung Tsoi,et al.  Face recognition: a convolutional neural-network approach , 1997, IEEE Trans. Neural Networks.

[9]  Meng Wang,et al.  Disease Inference from Health-Related Questions via Sparse Deep Learning , 2015, IEEE Transactions on Knowledge and Data Engineering.

[10]  Lubomir M. Hadjiiski,et al.  Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography. , 2016, Medical physics.

[11]  Kasiprasad Mannepalli,et al.  A novel Adaptive Fractional Deep Belief Networks for speaker emotion recognition , 2017 .

[12]  Amy Loutfi,et al.  A review of unsupervised feature learning and deep learning for time-series modeling , 2014, Pattern Recognit. Lett..

[13]  Jiwen Lu,et al.  Single Sample Face Recognition via Learning Deep Supervised Autoencoders , 2015, IEEE Transactions on Information Forensics and Security.

[14]  Yoshua Bengio,et al.  Describing Multimedia Content Using Attention-Based Encoder-Decoder Networks , 2015, IEEE Transactions on Multimedia.

[15]  Michael S. Lew,et al.  Deep learning for visual understanding: A review , 2016, Neurocomputing.

[16]  Seunggyun Ha,et al.  Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imaging , 2017, NeuroImage: Clinical.

[17]  A. Mechelli,et al.  Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications , 2017, Neuroscience & Biobehavioral Reviews.

[18]  Zhong Yin,et al.  Cross-session classification of mental workload levels using EEG and an adaptive deep learning model , 2017, Biomed. Signal Process. Control..

[19]  Jun Wu,et al.  A deep learning-based multi-model ensemble method for cancer prediction , 2018, Comput. Methods Programs Biomed..

[20]  Asifullah Khan,et al.  Two-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detection , 2017, Comput. Biol. Medicine.

[21]  Eugene Laksana,et al.  The impact of extraneous features on the performance of recurrent neural network models in clinical tasks , 2020, J. Biomed. Informatics.

[22]  Nilanjan Dey,et al.  A Survey of Data Mining and Deep Learning in Bioinformatics , 2018, Journal of Medical Systems.

[23]  Christopher Joseph Pal,et al.  EmoNets: Multimodal deep learning approaches for emotion recognition in video , 2015, Journal on Multimodal User Interfaces.

[24]  Yong Fan,et al.  A deep learning model integrating FCNNs and CRFs for brain tumor segmentation , 2017, Medical Image Anal..

[25]  Guang-Zhong Yang,et al.  Deep Learning for Health Informatics , 2017, IEEE Journal of Biomedical and Health Informatics.

[26]  Honglak Lee,et al.  Deep learning for detecting robotic grasps , 2013, Int. J. Robotics Res..

[27]  Bo Hu,et al.  A Vision of IoT: Applications, Challenges, and Opportunities With China Perspective , 2014, IEEE Internet of Things Journal.

[28]  Parisa Rashidi,et al.  Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis , 2017, IEEE Journal of Biomedical and Health Informatics.

[29]  Wenqing Sun,et al.  Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis , 2017, Comput. Biol. Medicine.

[30]  Justin A. Blanco,et al.  Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement , 2011, Journal of neural engineering.

[31]  Usha Devi Gandhi,et al.  A novel three-tier Internet of Things architecture with machine learning algorithm for early detection of heart diseases , 2017, Comput. Electr. Eng..

[32]  Matthew B. Blaschko,et al.  An ensemble deep learning based approach for red lesion detection in fundus images , 2017, Comput. Methods Programs Biomed..

[33]  Ugur Halici,et al.  A novel deep learning approach for classification of EEG motor imagery signals , 2017, Journal of neural engineering.

[34]  Qi Zhang,et al.  Deep learning based classification of breast tumors with shear-wave elastography. , 2016, Ultrasonics.

[35]  Hua Xu,et al.  Predict effective drug combination by deep belief network and ontology fingerprints , 2018, J. Biomed. Informatics.

[36]  Gerald Penn,et al.  Convolutional Neural Networks for Speech Recognition , 2014, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[37]  Dinggang Shen,et al.  A Robust Deep Model for Improved Classification of AD/MCI Patients , 2015, IEEE Journal of Biomedical and Health Informatics.

[38]  Xiao Li,et al.  Machine Learning Paradigms for Speech Recognition: An Overview , 2013, IEEE Transactions on Audio, Speech, and Language Processing.

[39]  Youngjin Yoo,et al.  Deep learning of brain lesion patterns and user-defined clinical and MRI features for predicting conversion to multiple sclerosis from clinically isolated syndrome , 2019, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

[40]  Svetha Venkatesh,et al.  $\mathtt {Deepr}$: A Convolutional Net for Medical Records , 2016, IEEE Journal of Biomedical and Health Informatics.

[41]  Dong Yu,et al.  Automatic Speech Recognition: A Deep Learning Approach , 2014 .

[42]  Naif Alajlan,et al.  Deep learning approach for active classification of electrocardiogram signals , 2016, Inf. Sci..

[43]  Tie-Yan Liu,et al.  Knowledge-Powered Deep Learning for Word Embedding , 2014, ECML/PKDD.

[44]  Olaf Hellwich,et al.  Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology , 2017, Comput. Medical Imaging Graph..

[45]  J. Pluim,et al.  Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI , 2017, NeuroImage: Clinical.

[46]  Stojan Trajanovski,et al.  Deep Physiological Arousal Detection in a Driving Simulator Using Wearable Sensors , 2017, 2017 IEEE International Conference on Data Mining Workshops (ICDMW).

[47]  Ron Kimmel,et al.  Computational mammography using deep neural networks , 2018, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

[48]  Gisbert Schneider,et al.  Deep Learning in Drug Discovery , 2016, Molecular informatics.

[49]  U. Rajendra Acharya,et al.  Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals , 2017, Comput. Biol. Medicine.

[50]  Anthony T. Chronopoulos,et al.  Computational intelligence approaches for classification of medical data: State-of-the-art, future challenges and research directions , 2018, Neurocomputing.

[51]  Ping Zhang,et al.  Risk Prediction with Electronic Health Records: A Deep Learning Approach , 2016, SDM.

[52]  Gustavo Carneiro,et al.  A deep learning approach for the analysis of masses in mammograms with minimal user intervention , 2017, Medical Image Anal..

[53]  Shiming Xiang,et al.  Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks , 2014, IEEE Geoscience and Remote Sensing Letters.

[54]  José Cristóbal Riquelme Santos,et al.  A study of the suitability of autoencoders for preprocessing data in breast cancer experimentation , 2017, J. Biomed. Informatics.

[55]  Hamid Jafarkhani,et al.  A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI , 2015, Medical Image Anal..

[56]  Ying-Chang Liang,et al.  Federated Learning in Mobile Edge Networks: A Comprehensive Survey , 2020, IEEE Communications Surveys & Tutorials.

[57]  Roger C. Tam,et al.  Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls , 2017, NeuroImage: Clinical.

[58]  Hakan Gunduz,et al.  Deep Learning-Based Parkinson’s Disease Classification Using Vocal Feature Sets , 2019, IEEE Access.