K. Heinzmann, L. Carter, J. S. Lewis, and E. O. Aboagye, Multiplexed imaging for diagnosis and therapy, vol.1, 2017.

C. Yu, W. Fei, Z. Ping, and H. Jianying, Risk Prediction with Electronic Health Records: A Deep Learning Approach, 2016.

T. A. Lasko, J. C. Denny, and M. A. Levy, Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data, 2013.

, Development of Inpatient Risk Stratification Models of Acute Kidney Injury for Use in Electronic Health Records, Medical Decision Making, vol.30, issue.6, p.20354229, 2010.

E. H. Kennedy, W. L. Wiitala, R. A. Hayward, and S. J. , Improved cardiovascular risk prediction using nonparametric regression and electronic health record data, Medical Care, 2013.

S. Yu, K. P. Liao, Y. Stanley, . Shaw, S. Vivian et al., Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources, Journal of the American Medical Informatics Association, vol.22, issue.5, pp.993-1000, 2015.

X. Wang, F. Wang, and J. Hu, A Multi-task Learning Framework for Joint Disease Risk Prediction and Comorbidity Discovery, Proceedings of the 2014 22Nd International Conference on Pattern Recognition, ICPR '14, pp.220-225, 2014.

J. C. Ho, J. Ghosh, S. R. Steinhubl, W. F. Stewart, J. C. Denny et al., Limestone: High-throughput candidate phenotype generation via tensor factorization, Special Section: Methods in Clinical Research Informatics, vol.52, pp.199-211, 2014.

I. Perros, E. E. Papalexakis, F. Wang, R. W. Vuduc, E. Searles et al., SPARTan: Scalable PARAFAC2 for Large & Sparse Data, Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.375-384, 2017.

I. Perros, E. E. Papalexakis, H. Park, R. W. Vuduc, X. Yan et al., SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping, 2018.

E. Choi, M. T. Bahadori, E. Searles, C. Coffey, and J. Sun, Multi-layer Representation Learning for Medical Concepts, 2016.

Y. Bengio, A. Courville, and P. Vincent, Representation Learning: A Review and New Perspectives, 2014.

R. Miotto, L. Li, B. A. Kidd, and J. T. Dudley, Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records, SCIENTIFIC REPORTS, 2016.

P. Nguyen, T. Tran, N. Wickramasinghe, and S. Venkatesh, mathttDeepr: A Convolutional Net for Medical Records, IEEE Journal of Biomedical and Health Informatics, vol.21, issue.1, pp.22-30, 2017.

E. Choi, M. T. Bahadori, L. Song, W. F. Stewart, and J. Sun, GRAM: Graph-based Attention Model for Healthcare Representation Learning, 2016.

J. Dean, G. S. Corrado, R. Monga, K. Chen, M. Devin et al., Large Scale Distributed Deep Networks, NIPS, 2012.

J. Keuper and F. Preundt, Distributed Training of Deep Neural Networks: Theoretical and Practical Limits of Parallel Scalability, Proceedings of the Workshop on Machine Learning in High Performance Computing Environments, MLHPC '16, pp.19-26, 2016.

W. Gupta, Model Accuracy and Runtime Tradeoff in Distributed Deep Learning: A Systematic Study, Proceedings of the TwentySixth International Joint Conference on Artificial Intelligence, IJCAI-17, pp.4854-4858, 2017.

L. Zhang and Y. Zhang, Yandong Wang. Nexus: Bringing Efficient and Scalable Training to Deep Learning Frameworks, 25th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, 2017.

C. Dünner and T. P. Parnell, Dimitrios Sarigiannis, Nikolas Ioannou, and Haralampos Pozidis. Snap Machine Learning. CoRR, 2018.

J. Peter, B. Jensen-lars, J. , and B. Søren, Mining electronic health records: towards better research applications and clinical care, Nature Reviews Genetics, vol.13, p.395, 2012.

G. Hripcsak and D. J. Albers, Next-generation phenotyping of electronic health records, JAMIA, vol.20, issue.1, pp.117-121, 2013.

Y. Bengio, Deep Learning of Representations for Unsupervised and Transfer Learning, Proceedings of the 2011 International Conference on Unsupervised and Transfer Learning Workshop, vol.27, pp.17-37, 2011.

T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, Distributed Representations of Words and Phrases and Their Compositionality, Proceedings of the 26th International Conference on Neural Information Processing Systems, vol.2, pp.3111-3119, 2013.

N. Srivastava, E. Mansimov, and R. Salakhudinov, Unsupervised Learning of Video Representations using LSTMs, Proceedings of the 32nd International Conference on Machine Learning, vol.37, pp.7-09, 2015.

P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. Manzagol, Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion, 2010.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen et al., Oriol Vinyals, 2016.