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There has been increasing interest in modelling survival data using deep learning methods in medical research.
The accelerated failure time model: a useful alternative to the cox regression model in survival analysis
Lee-Jen Wei · 1992
Earlier work this paper cites.
Regression Models and Life-Tables
D. R. Cox, S. Kotz, and N. L. Johnson · 1992
Earlier work this paper cites.
Statistical models based on counting processes
P. K. Andersen, ø. Borgan, R. D. Gill, and N. Keiding · 1993
Earlier work this paper cites.
Recruitment of adults 65 years and older as participants in the cardiovascular health study
Grethe S.Tell DrPhilos, Linda P.Fried, BonnieHermanson, Teri A.Manolio, Anne B.Newman, and Nemat O.Borhani3 · 1993
Earlier work this paper cites.
Tutorial in biostatistics: multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors
Frank E. Harrell, Kerry L. Lee, and Daniel B. Mark · 1996
Earlier work this paper cites.
Greedy function approximation: a gradient boosting machine
Jerome H Friedman · 2001
Earlier work this paper cites.
Multi-ethnic study of atherosclerosis: objectives and design
D. E. Bild, D. A. Bluemke, G. L. Burke, R. Detrano, A. V. Diez Roux, A. R. Folsom, P. Greenland, D. R. Jacob, R. Kronmal, K. Liu, J. C. Nelson, D. O’Leary, M. F. Saad, S. Shea, M. Szklo, and R. P. Tracy · 2002
Earlier work this paper cites.
Generalised linear models for correlated pseudo-observations, with applications to multistate models
P. K. Andersen, J. P. Klein, and S. Rosthøj · 2003
Earlier work this paper cites.
Regression modeling of competing risks data based on pseudovalues of the cumulative incidence function
J. P. Klein and P. K. Andersen · 2005
Earlier work this paper cites.
Consistent estimation of the expected brier score in general survival models with right-censored event times
P. K Andersen and M. P. Perme · 2006
Earlier work this paper cites.
Regression analysis for multistate models based on a pseudo-value approach, with applications to bone marrow transplantation studies
P. K Andersen and J. P. Klein · 2007
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SAS and R functions to compute pseudo-values for censored data regression
John P Klein, Mette Gerster, P. K. Andersen, Sergey Tarima, and Maja Pohar Perme · 2008
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Pseudo-observations in survival analysis
P. K Andersen and M. P. Perme · 2010
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Restricted mean models for transplant benefit and urgency
Fang Xiang and Susan Murray · 2012
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Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
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Pseudo-observations for competing risks with covariate dependent censoring
Nadine Binder, Thomas A. Gerds, and Per Kragh Andersen · 2014
Deep learning for patient-specific kidney graft survival analysis
M. Luck, T. Sylvain, H. Cardinal, A. Lodi, and Y. Bengio · 2017
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Individualized treatment effects with censored data via fully nonparametric bayesian accelerated failure time models
Nicholas C Henderson, Thomas A Louis, Gary L Rosner, and Ravi Varadhan · 2017
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Deepsurv: personalized treatment recommender system using a cox proportional hazards deep neural network
J. L. Katzman, U. Shaham, A. Cloninger, J. Bates, T. Jiang, and Y. Kluger · 2018
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Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data
T. Ching, X. Zhu, and L. X. Garmire · 2018
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Deep neural networks for survival analysis based on a multi-task framework
Stephane Fotso · 2018
Later among the works it cites.
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Cited alongside, same era.
Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
Cited alongside, same era.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
Cited alongside, same era.
WTTE-RNN: Weibull time to event recurrent neural network
E. Martinsson · 2016
Cited alongside, same era.
Deep convolutional neural network for survival analysis with pathological images
Jiawen Yao Xinliang Zhu and Junzhou Huang · 2016
Cited alongside, same era.
Deephit: A deep learning approach to survival analysis with competing risks
C. Lee, W. R. Zame, J. Yoon, and M. van der Schaar · 2018
Later among the works it cites.
RNN-SURV: A deep recurrent model for survival analysis
Eleonora Giunchiglia, Anton Nemchenko, and Mihaela van der Schaar · 2018
Later among the works it cites.
A scalable discrete-time survival model for neural networks
Michael F. Gensheimer and Balasubramanian Narasimhan · 2019
Closest in time.
Evaluating center-specific long-term outcomes through differences in mean survival time: Analysis of national kidney transplant data
Kevin He, Valarie Ashby, and Douglas E. Schaubel · 2019
Closest in time.