Applying Deep Learning to Individual and Community Health Monitoring Data: A Survey

In the recent years, deep learning models have addressed many problems in various fields. Meanwhile, technology development has spawned the big data in healthcare rapidly. Nowadays, application of deep learning to solve the problems in healthcare is a hot research direction. This paper introduces the application of deep learning in healthcare extensively. We focus on 7 application areas of deep learning, which are electronic health records (EHR), electrocardiography (ECG), electroencephalogram (EEG), community healthcare, data from wearable devices, drug analysis and genomics analysis. The scope of this paper does not cover medical image processing since other researchers have already substantially reviewed it. In addition, we analyze the merits and drawbacks of the existing works, analyze the existing challenges, and discuss future trends.

[1]  Klaus-Robert Müller,et al.  Interpretable deep neural networks for single-trial EEG classification , 2016, Journal of Neuroscience Methods.

[2]  Yixin Chen,et al.  Compressing Neural Networks with the Hashing Trick , 2015, ICML.

[3]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[4]  Sepp Hochreiter,et al.  Toxicity Prediction using Deep Learning , 2015, ArXiv.

[5]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

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

[7]  Joshua E. Lewis,et al.  Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models , 2017, Scientific Reports.

[8]  Yichen Shen,et al.  Deep learning based atrial fibrillation detection using wearable photoplethysmography sensor , 2018, 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).

[9]  Xianxiang Chen,et al.  Respiration-based emotion recognition with deep learning , 2017, Comput. Ind..

[10]  Tara N. Sainath,et al.  Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups , 2012, IEEE Signal Processing Magazine.

[11]  Ronald M. Summers,et al.  Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique , 2016 .

[12]  Yan Liu,et al.  Distilling Knowledge from Deep Networks with Applications to Healthcare Domain , 2015, ArXiv.

[13]  Ingemar J. Cox,et al.  On Infectious Intestinal Disease Surveillance using Social Media Content , 2016, Digital Health.

[14]  Charles Elkan,et al.  Learning to Diagnose with LSTM Recurrent Neural Networks , 2015, ICLR.

[15]  Guang-Zhong Yang,et al.  Deep learning for human activity recognition: A resource efficient implementation on low-power devices , 2016, 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN).

[16]  Yixin Chen,et al.  Atrial fibrillation detection by multi-scale convolutional neural networks , 2017, 2017 20th International Conference on Information Fusion (Fusion).

[17]  Hongfang Liu,et al.  Temporal Pattern and Association Discovery of Diagnosis Codes Using Deep Learning , 2015, 2015 International Conference on Healthcare Informatics.

[18]  Yan Liu,et al.  Recurrent Neural Networks for Multivariate Time Series with Missing Values , 2016, Scientific Reports.

[19]  Nigam H. Shah,et al.  Improving palliative care with deep learning , 2017, 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

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

[21]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[22]  Lei Wang,et al.  A restricted Boltzmann machine based two-lead electrocardiography classification , 2015, 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN).

[23]  Dirk Hovy,et al.  Multitask Learning for Mental Health Conditions with Limited Social Media Data , 2017, EACL.

[24]  Brian Litt,et al.  Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets , 2010, 2010 Ninth International Conference on Machine Learning and Applications.

[25]  Geoffrey E. Hinton,et al.  Deep Boltzmann Machines , 2009, AISTATS.

[26]  Xiangji Huang,et al.  Deep learning for healthcare decision making with EMRs , 2014, 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[27]  Lovekesh Vig,et al.  Anomaly detection in ECG time signals via deep long short-term memory networks , 2015, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[28]  Kumardeep Chaudhary,et al.  Deep Learning–Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer , 2017, Clinical Cancer Research.

[29]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

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

[31]  Evgeny Putin,et al.  Deep biomarkers of human aging: Application of deep neural networks to biomarker development , 2016, Aging.

[32]  Junzhou Huang,et al.  Seq2seq Fingerprint: An Unsupervised Deep Molecular Embedding for Drug Discovery , 2017, BCB.

[33]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[34]  Liang Zhao,et al.  SimNest: Social Media Nested Epidemic Simulation via Online Semi-Supervised Deep Learning , 2015, 2015 IEEE International Conference on Data Mining.

[35]  Joseph Futoma,et al.  A comparison of models for predicting early hospital readmissions , 2015, J. Biomed. Informatics.

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

[37]  Aidong Zhang,et al.  A Novel Semi-Supervised Deep Learning Framework for Affective State Recognition on EEG Signals , 2014, 2014 IEEE International Conference on Bioinformatics and Bioengineering.

[38]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[39]  Ronald J. Williams,et al.  A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.

[40]  U. Rajendra Acharya,et al.  Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals , 2017, Inf. Sci..

[41]  Richard Walker,et al.  PD Disease State Assessment in Naturalistic Environments Using Deep Learning , 2015, AAAI.

[42]  Yixin Chen,et al.  Compressing Convolutional Neural Networks in the Frequency Domain , 2015, KDD.

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

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

[45]  Volker Tresp,et al.  Predicting Clinical Events by Combining Static and Dynamic Information Using Recurrent Neural Networks , 2016, 2016 IEEE International Conference on Healthcare Informatics (ICHI).

[46]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[47]  Reza Ghaeini,et al.  A Deep Learning Approach for Cancer Detection and Relevant Gene Identification , 2017, PSB.

[48]  Svetha Venkatesh,et al.  DeepCare: A Deep Dynamic Memory Model for Predictive Medicine , 2016, PAKDD.

[49]  Jeffrey Dean,et al.  Scalable and accurate deep learning with electronic health records , 2018, npj Digital Medicine.

[50]  Robert P. Sheridan,et al.  Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships , 2015, J. Chem. Inf. Model..

[51]  Ninghui Sun,et al.  DianNao family , 2016, Commun. ACM.

[52]  Geoffrey E. Hinton,et al.  Learning and relearning in Boltzmann machines , 1986 .

[53]  Andrew Y. Ng,et al.  Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks , 2017, ArXiv.

[54]  Hong Yu,et al.  Bidirectional RNN for Medical Event Detection in Electronic Health Records , 2016, NAACL.

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

[56]  Stefan M. Rüger,et al.  Adverse Drug Reaction Classification With Deep Neural Networks , 2016, COLING.

[57]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[58]  Wolfram Burgard,et al.  Deep learning with convolutional neural networks for EEG decoding and visualization , 2017, Human brain mapping.

[59]  Franck Dernoncourt,et al.  De-identification of patient notes with recurrent neural networks , 2016, J. Am. Medical Informatics Assoc..

[60]  J. T. Turner,et al.  Comparing Raw Data and Feature Extraction for Seizure Detection with Deep Learning Methods , 2014, FLAIRS Conference.