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Many computational models were proposed to extract temporal patterns from clinical time series for each patient and among patient group for predictive healthcare.
Deflation techniques for an implicitly restarted arnoldi iteration
Lehoucq, R. B. and Sorensen, D. C · 1996
Earlier work this paper cites.
On spectral clustering: Analysis and an algorithm
Ng, A. Y., Jordan, M. I., and Weiss, Y · 2002
Earlier work this paper cites.
On the use of cross-validation for time series predictor evaluation
Bergmeir, C. and Benítez, J. M · 2012
Earlier work this paper cites.
Disease progression modeling using hidden markov models
Sukkar, R., Katz, E., Zhang, Y., Raunig, D., and Wyman, B. T · 2012
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Mikolov, T., Chen, K., Corrado, G., and Dean, J · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Xgboost: A scalable tree boosting system
Chen, T. and Guestrin, C · 2016
Cited alongside, same era.
Patient subtyping via time-aware lstm networks
Baytas, I. M., Xiao, C., Zhang, X., Wang, F., Jain, A. K., and Zhou, J · 2017
Cited alongside, same era.
An rnn architecture with dynamic temporal matching for personalized predictions of parkinson’s disease
Che, C., Xiao, C., Liang, J., Jin, B., Zho, J., and Wang, F · 2017
Cited alongside, same era.
Gram: graph-based attention model for healthcare representation learning
Choi, E., Bahadori, M. T., Song, L., Stewart, W. F., and Sun, J · 2017
Cited alongside, same era.
Dipole: Diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks
Ma, F., Chitta, R., Zhou, J., You, Q., Sun, T., and Gao, J · 2017
Cited alongside, same era.
Clinical intervention prediction and understanding with deep neural networks
Interpretable representation learning for healthcare via capturing disease progression through time
Bai, T., Zhang, S., Egleston, B. L., and Vucetic, S · 2018
Later among the works it cites.
MiME: Multilevel medical embedding of electronic health records for predictive healthcare
Choi, E., Xiao, C., Stewart, W., and Sun, J · 2018
Later among the works it cites.
A general framework for diagnosis prediction via incorporating medical code descriptions
Ma, F., Wang, Y., Xiao, H., Yuan, Y., Chitta, R., Zhou, J., and Gao, J · 2018
Later among the works it cites.
Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review
Xiao, C., Choi, E., and Sun, J · 2018
Later among the works it cites.
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J · 2019
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Suresh, H., Hunt, N., Johnson, A., Celi, L. A., Szolovits, P., and Ghassemi, M · 2017
Cited alongside, same era.
Doctor ai: Predicting clinical events via recurrent neural networks
Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., and Sun, J
Cited in the paper.
Retain: An interpretable predictive model for healthcare using reverse time attention mechanism
Choi, E., Bahadori, M. T., Sun, J., Kulas, J., Schuetz, A., and Stewart, W
Cited in the paper.
Using recurrent neural network models for early detection of heart failure onset
Choi, E., Schuetz, A., Stewart, W. F., and Sun, J
Cited in the paper.
Kame: Knowledge-based attention model for diagnosis prediction in healthcare
Ma, F., You, Q., Xiao, H., Chitta, R., Zhou, J., and Gao, J
Cited in the paper.