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Modeling the time-series of high-dimensional, longitudinal data is important for predicting patient disease progression.
Estimating causal effects of treatments in randomized and nonrandomized studies
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Criteria for the classification of monoclonal gammopathies, multiple myeloma and related disorders: a report of the international myeloma working group
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Using disease progression models as a tool to detect drug effect
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LeCun, Y · 2012
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Parameter estimates for invasive breast cancer progression in the canadian national breast screening study
Taghipour, S., Banjevic, D., Miller, A., Montgomery, N., Jardine, A., and Harvey, B · 2013
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Neural machine translation by jointly learning to align and translate
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On the properties of neural machine translation: Encoder-decoder approaches
Cho, K., Van Merriënboer, B., Bahdanau, D., and Bengio, Y · 2014
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Kingma, D. P. and Ba, J · 2014
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A clockwork rnn
Koutnik, J., Greff, K., Gomez, F., and Schmidhuber, J · 2014
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Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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Unsupervised learning of disease progression models
Wang, X., Sontag, D., and Wang, F · 2014
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Jaderberg, M., Simonyan, K., Zisserman, A., and Kavukcuoglu, K · 2015
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Efficient learning of continuous-time hidden markov models for disease progression
Liu, Y.-Y., Li, S., Li, F., Song, L., and Rehg, J. M · 2015
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Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis
Perotte, A., Ranganath, R., Hirsch, J. S., Blei, D., and Elhadad, N · 2015
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Chemotherapeutic dose scheduling based on tumor growth rates provides a case for low-dose metronomic high-entropy therapies
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Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., and Satija, R · 2018
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Recurrent neural networks for multivariate time series with missing values
Che, Z., Purushotham, S., Cho, K., Sontag, D., and Liu, Y · 2018
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Characterizing heterogeneity in the progression of alzheimer’s disease using longitudinal clinical and neuroimaging biomarkers
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Doctor ai: Predicting clinical events via recurrent neural networks
Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., and Sun, J · 2016
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Cross-corpora unsupervised learning of trajectories in autism spectrum disorders
Elibol, H. M., Nguyen, V., Linderman, S., Johnson, M., Hashmi, A., and Doshi-Velez, F · 2016
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Predicting disease progression with a model for multivariate longitudinal clinical data
Futoma, J., Sendak, M., Cameron, B., and Heller, K · 2016
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Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
Ling, J., Kurzawski, A., and Templeton, J · 2016
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Deep patient: an unsupervised representation to predict the future of patients from the electronic health records
Miotto, R., Li, L., Kidd, B. A., and Dudley, J. T · 2016
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Integrative analysis using coupled latent variable models for individualizing prognoses
Schulam, P. and Saria, S · 2016
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Observational-interventional priors for dose-response learning
Silva, R · 2016
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Forecasting treatment responses over time using recurrent marginal structural networks
Lim, B., Alaa, A., and van der Schaar, M · 2018
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Rotation equivariant cnns for digital pathology
Veeling, B. S., Linmans, J., Winkens, J., Cohen, T., and Welling, M · 2018
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Attentive state-space modeling of disease progression
Alaa, A. M. and van der Schaar, M · 2019
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Cormorant: Covariant molecular neural networks
Anderson, B., Hy, T. S., and Kondor, R · 2019
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Analysis of temporal pattern, causal interaction and predictive modeling of financial markets using nonlinear dynamics, econometric models and machine learning algorithms
Ghosh, I., Jana, R. K., and Sanyal, M. K · 2019
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Models and machines: How deep learning will take clinical pharmacology to the next level
Hutchinson, L., Steiert, B., Soubret, A., Wagg, J., Phipps, A., Peck, R., Charoin, J.-E., and Ribba, B · 2019
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Challenges of integrative disease modeling in alzheimer’s disease
Khatami, S. G., Robinson, C., Birkenbihl, C., Domingo-Fernández, D., Hoyt, C. T., and Hofmann-Apitius, M · 2019
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Dive: A spatiotemporal progression model of brain pathology in neurodegenerative disorders
Marinescu, R. V., Eshaghi, A., Lorenzi, M., Young, A. L., Oxtoby, N. P., Garbarino, S., Crutch, S. J., Alexander, D. C., Initiative, A. D. N., et al · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
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Differentiable physics-informed graph networks
Seo, S. and Liu, Y · 2019
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Personalized input-output hidden markov models for disease progression modeling
Severson, K. A., Chahine, L. M., Smolensky, L. A., Ng, K., Hu, J., and Ghosh, S · 2020
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Incorporating symmetry into deep dynamics models for improved generalization
Wang, R., Walters, R., and Yu, R · 2020
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