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We propose a versatile framework for survival analysis that combines advanced concepts from statistics with deep learning.
Continuous and Discrete-Time Survival Prediction with Neural Networks
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A general framework for neural network models on censored survival data
Biganzoli, E.; Boracchi, P.; and Marubini, E. 2002 · 2002
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Mixed Model-Based Hazard Estimation
Cai, T.; Hyndman, R. J.; and Wand, M. P. 2002 · 2002
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Rügamer, D.; Kolb, C.; and Klein, N. 2020 · 2002
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Penalized spline smoothing in multivariable survival models with varying coefficients
Kauermann, G. 2005 · 2005
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A General Machine Learning Framework for Survival Analysis
Bender, A.; Rügamer, D.; Scheipl, F.; and Bischl, B. 2020 · 2006
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Particle-based shape analysis of multi-object complexes
Cates, J.; Fletcher, P. T.; Styner, M.; Hazlett, H. C.; and Whitaker, R. 2008 · 2008
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Mapping local hippocampal changes in Alzheimer’s disease and normal ageing with MRI at 3 Tesla
Frisoni, G. B.; Ganzola, R.; Canu, E.; Rüb, U.; Pizzini, F. B.; Alessandrini, F.; Zoccatelli, G.; Beltramello, A.; Caltagirone, C.; and Thompson, P. M. 2008 · 2008
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The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
Jack Jr, C. R.; Bernstein, M. A.; Fox, N. C.; Thompson, P.; Alexander, G.; Harvey, D.; Borowski, B.; Britson, P. J.; L. Whitwell, J.; Ward, C.; et al. 2008 · 2008
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Multidimensional classification of hippocampal shape features discriminates Alzheimer’s disease and mild cognitive impairment from normal aging
Gerardin, E.; Chételat, G.; Chupin, M.; Cuingnet, R.; Desgranges, B.; Kim, H.-S.; Niethammer, M.; Dubois, B.; Lehéricy, S.; Garnero, L.; et al. 2009 · 2009
Deep Survival Analysis
Ranganath, R.; Perotte, A.; Elhadad, N.; and Blei, D. 2016 · 2016
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Whole-brain analysis reveals increased neuroanatomical asymmetries in dementia for hippocampus and amygdala
Wachinger, C.; Salat, D. H.; Weiner, M.; Reuter, M.; and Initiative, A. D. N. 2016 · 2016
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Deep multi-task gaussian processes for survival analysis with competing risks
Alaa, A. M.; and van der Schaar, M. 2017 · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Qi, C. R.; Su, H.; Mo, K.; and Guibas, L. J. 2017 · 2017
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Generalized Additive Models: An Introduction with R
Wood, S. N. 2017 · 2017
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A generalized additive model approach to time-to-event analysis
Bender, A.; Groll, A.; and Scheipl, F. 2018 · 2018
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Deep Conditional Transformation Models
Baumann, P.; Hothorn, T.; and Rügamer, D. 2020 · 2010
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Neural Mixture Distributional Regression
Rügamer, D.; Pfisterer, F.; and Bischl, B. 2020 · 2010
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Mild cognitive impairment
Petersen, R. C. 2011 · 2011
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FreeSurfer
Fischl, B. 2012 · 2012
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Piecewise Exponential Artificial Neural Networks (PEANN) for Modeling Hazard Function with Right Censored Data
Fornili, M.; Ambrogi, F.; Boracchi, P.; and Biganzoli, E. 2014 · 2014
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The diagnosis and management of mild cognitive impairment: a clinical review
Langa, K. M.; and Levine, D. A. 2014 · 2014
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Analysis of Time to Event Outcomes in Randomized Controlled Trials by Generalized Additive Models
Argyropoulos, C.; and Unruh, M. L. 2015 · 2015
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pammtools: Piece-wise exponential Additive Mixed Modeling tools
Bender, A.; and Scheipl, F. 2018 · 2018
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DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks
Lee, C.; Zame, W. R.; Yoon, J.; and van der Schaar, M. 2018 · 2018
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Penalized estimation of complex, non-linear exposure-lag-response associations
Bender, A.; Scheipl, F.; Hartl, W.; Day, A. G.; and Küchenhoff, H. 2019 · 2019
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A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data
Pölsterl, S.; Sarasua, I.; Gutiérrez-Becker, B.; and Wachinger, C. 2019 · 2019
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Comments on: Inference and computation with Generalized Additive Models and their extensions
Greven, S.; and Scheipl, F. 2020 · 2020
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Dynamic-DeepHit: A Deep Learning Approach for Dynamic Survival Analysis With Competing Risks Based on Longitudinal Data
Lee, C.; Yoon, J.; and van der Schaar, M. 2020 · 2020
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