A General Machine Learning Framework for Survival Analysis

Author:

Bender AndreasORCID,Rügamer DavidORCID,Scheipl FabianORCID,Bischl BerndORCID

Publisher

Springer International Publishing

Reference37 articles.

1. Alaa, A.M., van der Schaar, M.: Deep multi-task gaussian processes for survival analysis with competing risks. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 2326–2334 (2017)

2. Bender, A., Groll, A., Scheipl, F.: A generalized additive model approach to time-to-event analysis. Statistical Modelling p. 1471082X17748083 (2018)

3. Bender, A., Scheipl, F., Hartl, W., Day, A.G., Küchenhoff, H.: Penalized estimation of complex, non-linear exposure-lag-response associations. Biostatistics 20(2), 315–331 (2018)

4. Biganzoli, E., Boracchi, P., Marubini, E.: A general framework for neural network models on censored survival data. Neural Netw. 15(2), 209–218 (2002)

5. Binder, H., Allignol, A., Schumacher, M., Beyersmann, J.: Boosting for high-dimensional time-to-event data with competing risks. Bioinformatics 25(7), 890–896 (2009)

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