170 citations · 334 across the 9 of their papers we have counts for
3 papers · 1 filter
Semi-Structured Deep Piecewise Exponential Models
Philipp Kopper, Sebastian Pölsterl, Christian Wachinger +3
We propose a versatile framework for survival analysis that combines advanced concepts from statistics with deep learning. The presented framework is based on piecewise exponential…
mlr3proba: An R Package for Machine Learning in Survival Analysis
Raphael Sonabend, Franz J. Király, Andreas Bender +2
As machine learning has become increasingly popular over the last few decades, so too has the number of machine learning interfaces for implementing these models. Whilst many R lib…
A General Machine Learning Framework for Survival Analysis
Andreas Bender, David Rügamer, Fabian Scheipl +1
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, an…