Fetching the paper…
Reading the bibliography…
Medical practitioners use survival models to explore and understand the relationships between patients' covariates (e.g.
Uniformly distributed sequences with an additional uniform property
Ilya M Sobol · 1976
Earlier work this paper cites.
Regression modeling strategies for improved prognostic prediction
Frank E Harrell, Kerry L Lee, Robert M Califf, David B Pryor, and Robert A Rosati · 1984
Earlier work this paper cites.
Algorithm 647: Implementation and relative efficiency of quasirandom sequence generators
Bennett L. Fox · 1986
Earlier work this paper cites.
Regression models and life-tables
David R Cox · 1992
Earlier work this paper cites.
Survival analysis and neural nets
Knut Liestbl, Per Kragh Andersen, and Ulrich Andersen · 1994
Earlier work this paper cites.
An introduction to the bootstrap
Bradley Efron and Robert J Tibshirani · 1994
Earlier work this paper cites.
Randomized 2 x 2 trial evaluating hormonal treatment and the duration of chemotherapy in node-positive breast cancer patients. german breast cancer study group
M Schumacher, G Bastert, H Bojar, K Huebner, M Olschewski, W Sauerbrei, C Schmoor, C Beyerle, RL Neumann, and HF Rauschecker · 1994
Earlier work this paper cites.
A neural network model for survival data
David Faraggi and Richard Simon · 1995
Earlier work this paper cites.
The support prognostic model: objective estimates of survival for seriously ill hospitalized adults
William A Knaus, Frank E Harrell, Joanne Lynn, Lee Goldman, Russell S Phillips, Alfred F Connors, Neal V Dawson, William J Fulkerson, Robert M Califf, Norman Desbiens, et al · 1995
Earlier work this paper cites.
Prognostic factors for metachronous contralateral breast cancer: a comparison of the linear cox regression model and its artificial neural network extension
L Mariani, D Coradini, E Biganzoli, P Boracchi, E Marubini, S Pilotti, B Salvadori, R Silvestrini, U Veronesi, R Zucali, et al · 1997
Earlier work this paper cites.
A neural network model for prognostic prediction
W Nick Street · 1998
Earlier work this paper cites.
Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach
Elia Biganzoli, Patrizia Boracchi, Luigi Mariani, and Ettore Marubini · 1998
Earlier work this paper cites.
Comparison of the performance of neural network methods and cox regression for censored survival data
Anny Xiang, Pablo Lapuerta, Alex Ryutov, Jonathan Buckley, and Stanley Azen · 2000
Earlier work this paper cites.
The urokinase system of plasminogen activation and prognosis in 2780 breast cancer patients
John A Foekens, Harry A Peters, Maxime P Look, Henk Portengen, Manfred Schmitt, Michael D Kramer, Nils Brünner, Fritz Jänicke, Marion E Meijer-van Gelder, Sonja C Henzen-Logmans, et al · 2000
Cited alongside, same era.
What do we mean by validating a prognostic model?
Douglas G Altman and Patrick Royston · 2000
Cited alongside, same era.
Comparison of artificial neural networks with other statistical approaches
Daniel J Sargent · 2001
Cited alongside, same era.
Semi-supervised methods to predict patient survival from gene expression data
Eric Bair and Robert Tibshirani · 2004
Cited alongside, same era.
Artificial neural networks and prognosis in medicine. survival analysis in breast cancer patients
Leonardo Franco, José M Jerez, and Emilio Alba · 2005
Cited alongside, same era.
Random survival forests for r
The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups
Christina Curtis, Sohrab P Shah, Suet-Feung Chin, Gulisa Turashvili, Oscar M Rueda, Mark J Dunning, Doug Speed, Andy G Lynch, Shamith Samarajiwa, Yinyin Yuan, et al · 2012
Later among the works it cites.
External validation of a cox prognostic model: principles and methods
Patrick Royston and Douglas G Altman · 2013
Later among the works it cites.
Development of a prognostic model for breast cancer survival in an open challenge environment
Wei-Yi Cheng, Tai-Hsien Ou Yang, and Dimitris Anastassiou · 2013
Later among the works it cites.
Gradient methods for minimizing composite functions
Yu Nesterov · 2013
Later among the works it cites.
An empirical study of learning rates in deep neural networks for speech recognition
Alan Senior, Georg Heigold, Marc’Aurelio Ranzato, and Ke Yang · 2013
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
H. Ishwaran and U.B. Kogalur · 2007
Cited alongside, same era.
Random survival forests
H. Ishwaran, U.B. Kogalur, E.H. Blackstone, and M.S. Lauer · 2008
Cited alongside, same era.
Applied Survival Analysis: Regression Modeling of Time to Event Data
David W. Hosmer Jr., Stanley Lemeshow, and Susanne May · 2008
Cited alongside, same era.
Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E. Hinton · 2010
Cited alongside, same era.
Understanding the exploding gradient problem
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio · 2012
Cited alongside, same era.
Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
Cited alongside, same era.
Generating survival times to simulate cox proportional hazards models with time-varying covariates
Peter C Austin · 2012
Cited alongside, same era.
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Later among the works it cites.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
Later among the works it cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Later among the works it cites.
Development and validation of a prediction rule for benefit and harm of dual antiplatelet therapy beyond 1 year after percutaneous coronary intervention
Yeh RW, Secemsky EA, Kereiakes DJ, and et al · 2016
Closest in time.
Deep survival analysis
Rajesh Ranganath, Adler Perotte, Noémie Elhadad, and David Blei · 2016
Closest in time.
Ihc4 score plus clinical treatment score predicts locoregional recurrence in early breast cancer
Roopa Lakhanpal, Ivana Sestak, Bruce Shadbolt, Genevieve M Bennett, Michael Brown, Tessa Phillips, Yanping Zhang, Amanda Bullman, and Angela Rezo · 2016
Closest in time.
Self-normalizing neural networks
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter · 2017
Closest in time.