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Application of discrete-time survival methods for continuous-time survival prediction is considered.
Regression models and life-tables
David R. Cox · 1972
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On the use of indicator variables for studying the time-dependence of parameters in a response-time model
Charles C. Brown · 1975
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Life tables with concomitant information
Theodore R. Holford · 1976
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Discrete-time methods for the analysis of event histories
Paul D. Allison · 1982
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Piecewise exponential models for survival data with covariates
Michael Friedman · 1982
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Evaluating the yield of medical tests
Frank E. Harrell Jr, Robert M. Califf, David B. Pryor, Kerry L. Lee, and Robert A. Rosati · 1982
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A neural network model for survival data
David Faraggi and Richard Simon · 1995
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Assessment and comparison of prognostic classification schemes for survival data
Erika Graf, Claudia Schmoor, Willi Sauerbrei, and Martin Schumacher · 1999
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Survival Analysis: Techniques for Censored and Truncated Data
John P. Klein and Melvin L. Moeschberger · 2003
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A time-dependent discrimination index for survival data
Laura Antolini, Patrizia Boracchi, and Elia Biganzoli · 2005
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Deep convolutional neural network for survival analysis with pathological images
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Wsisa: Making survival prediction from whole slide histopathological images
X. Zhu, J. Yao, F. Zhu, and J. Huang · 2017
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Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data
Travers Ching, Xun Zhu, and Lana X. Garmire · 2018
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Deep neural networks for survival analysis based on a multi-task framework
Stephane Fotso · 2018
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Deepsurv: personalized treatment recommender system using a Cox proportional hazards deep neural network
Jared L. Katzman, Uri Shaham, Alexander Cloninger, Jonathan Bates, Tingting Jiang, and Yuval Kluger · 2018
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Deephit: A deep learning approach to survival analysis with competing risks
Changhee Lee, William R Zame, Jinsung Yoon, and Mihaela van der Schaar · 2018
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