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Semi-parametric survival analysis methods like the Cox Proportional Hazards (CPH) regression (Cox, 1972) are a popular approach for survival analysis.
Regression models and life-tables
David R Cox · 1972
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Adaptive mixtures of local experts
Robert A Jacobs, Michael I Jordan, Steven J Nowlan, Geoffrey E Hinton, et al · 1991
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An introduction to the bootstrap
Bradley Efron and Robert J Tibshirani · 1994
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Hierarchical mixtures of experts and the em algorithm
Michael I Jordan and Robert A Jacobs · 1994
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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
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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
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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
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Mixtures of proportional hazards regression models
Ori Rosen and Martin Tanner · 1999
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What do we mean by validating a prognostic model?
Douglas G Altman and Patrick Royston · 2000
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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
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Anny Xiang, Pablo Lapuerta, Alex Ryutov, Jonathan Buckley, and Stanley Azen · 2000
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Comparison of artificial neural networks with other statistical approaches: results from medical data sets
Daniel J Sargent · 2001
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Random survival forests
Hemant Ishwaran, Udaya B Kogalur, Eugene H Blackstone, Michael S Lauer, et al · 2008
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Neural networks for machine learning lecture 6a overview of mini-batch gradient descent
Geoffrey Hinton, Nitish Srivastava, and Kevin Swersky · 2012
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Tip: Typifying the interpretability of procedures
Amit Dhurandhar, Vijay Iyengar, Ronny Luss, and Karthikeyan Shanmugam · 2017
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
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Self-normalizing neural networks
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter · 2017
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Vinod Nair and Geoffrey E Hinton · 2010
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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
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Machine learning for survival analysis: A survey
Ping Wang, Yan Li, and Chandan K. Reddy · 2017
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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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