# A General Machine Learning Framework for Survival Analysis

@inproceedings{Bender2020AGM, title={A General Machine Learning Framework for Survival Analysis}, author={Andreas Bender and D. R{\"u}gamer and Fabian Scheipl and B. Bischl}, booktitle={ECML/PKDD}, year={2020} }

The modeling of time-to-event data, also known as survival analysis, requires specialized methods that can deal with censoring and truncation, time-varying features and effects, and that extend to settings with multiple competing events. However, many machine learning methods for survival analysis only consider the standard setting with right-censored data and proportional hazards assumption. The methods that do provide extensions usually address at most a subset of these challenges and often…

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