DOI: 10.1093/bioinformatics/btad723 ISSN: 1367-4803

survex: an R package for explaining machine learning survival models

Mikołaj Spytek, Mateusz Krzyziński, Sophie Hanna Langbein, Hubert Baniecki, Marvin N Wright, Przemysław Biecek
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Molecular Biology
  • Biochemistry
  • Statistics and Probability



Due to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models. However, their widespread adoption is hindered by a lack of user-friendly tools to explain their internal operations and prediction rationales. To tackle this issue, we introduce the survex R package, which provides a cohesive framework for explaining any survival model by applying explainable artificial intelligence techniques. The capabilities of the proposed software encompass understanding and diagnosing survival models, which can lead to their improvement. By revealing insights into the decision-making process, such as variable effects and importances, survex enables the assessment of model reliability and the detection of biases. Thus, transparency and responsibility may be promoted in sensitive areas, such as biomedical research and healthcare applications.

Availability and Implementation

survex is available under the GPL3 public license at and on CRAN with documentation available at

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