ICML 2025

Gradient-based Explanations for Deep Learning Survival Models
Sophie Hanna Langbein * 1 2 Niklas Koenen * 1 2 Marvin N. Wright 1 2 3
Abstract
Deep learning survival models often outperform
classical methods in time-to-event predictions,
particularly in personalized medicine, but their
“black box” nature hinders broader adoption. We
propose a framework for gradient-based explana-
tion methods tailored to survival neural networks,
extending their use beyond regression and clas-
sification. We analyze the implications of their
theoretical assumptions for time-dependent ex-
planations in the survival setting and propose ef-
fective visualizations incorporating the temporal
dimension. Experiments on synthetic data show
that gradient-based methods capture the magni-
tude and direction of local and global feature
effects, including time dependencies. We intro-
duce GradSHAP(t), a gradient-based counterpart
to SurvSHAP(t), which outperforms SurvSHAP(t)
and SurvLIME in a computational speed vs. accu-
racy trade-off. Finally, we apply these methods to
medical data with multi-modal inputs, revealing
relevant tabular features and visual patterns, as
well as their temporal dynamics.
1. Introduction
As medical databases expand to include patients’ detailed
medical history and genetic information, healthcare is shift-
ing from population-based models and traditional statistical
approaches targeting the “average” patient to more complex
personalized medicine, which tailors diagnoses to individ-
ual patient characteristics. Survival analysis is fundamental
to medical data analysis, modeling time-to-event outcomes
while accounting for censoring, enabling personalized risk
predictions, and assessing treatment effects to advance clini-
*Equal contribution 1Leibniz Institute for Prevention Research
and Epidemiology – BIPS, Bremen, Germany 2Faculty of Math-
ematics and Computer Science, University of Bremen, Germany
3Department of Public Health, University of Copenhagen, Den-
mark. Corre...

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