5 papers
Functional Decomposition and Shapley Interactions for Interpreting Survival Models
Sophie Hanna Langbein, Hubert Baniecki, Fabian Fumagalli +3
Hazard and survival functions are natural, interpretable targets in time-to-event prediction, but their inherent non-additivity fundamentally limits standard additive explanation m…
Machine Learning in Epidemiology
Marvin N. Wright, Lukas Burk, Pegah Golchian +3
In the age of digital epidemiology, epidemiologists are faced by an increasing amount of data of growing complexity and dimensionality. Machine learning is a set of powerful tools…
Reduction Techniques for Survival Analysis
Johannes Piller, Léa Orsini, Simon Wiegrebe +6
In this work, we discuss what we refer to as reduction techniques for survival analysis, that is, techniques that "reduce" a survival task to a more common regression or classifica…
Gradient-based Explanations for Deep Learning Survival Models
Sophie Hanna Langbein, Niklas Koenen, Marvin N. Wright
Deep learning survival models often outperform classical methods in time-to-event predictions, particularly in personalized medicine, but their "black box" nature hinders broader a…
Interpretable Machine Learning for Survival Analysis
Sophie Hanna Langbein, Mateusz KrzyziÅski, MikoÅaj Spytek +3
With the spread and rapid advancement of black box machine learning models, the field of interpretable machine learning (IML) or explainable artificial intelligence (XAI) has becom…