Survival analysis is a task dealing with time-to-event prediction. Aside from the well-understood models like CPH, many more complex models have recently emerged, but most lack interpretability. Due to a functional type of prediction, either in the form of survival function or cumulative hazard function, standard model-agnostic explanations cannot be applied directly.

The `survex`

package provides model-agnostic explanations for machine learning survival models. It is based on the `DALEX`

package. If you’re unfamiliar with explainable machine learning, consider referring to the Explanatory Model Analysis book – most of the methods included in `survex`

extend these described in EMA and implemented in `DALEX`

but to models with functional output.

The main `explain()`

function uses a model and data to create a standardized `explainer`

object, which is further used as an interface for calculating predictions. We automate creating explainers from the following packages: `mlr3proba`

, `censored`

, `ranger`

, `randomForestSRC`

, and `survival`

. **Raise an Issue on GitHub if you find models from other packages that we can incorporate into the explain() interface.**

Note that an explainer can be created for **any** survival model, using the `explain_survival()`

function by passing `model`

, `data`

, `y`

, and `predict_survival_function`

arguments.

The package is available on CRAN:

`install.packages("survex")`

The latest development version can be installed from GitHub using `devtools::install_github()`

:

`devtools::install_github("https://github.com/ModelOriented/survex")`

```
library("survex")
library("survival")
library("ranger")
# create a model
model <- ranger(Surv(time, status) ~ ., data = veteran)
# create an explainer
explainer <- explain(model,
data = veteran[, -c(3, 4)],
y = Surv(veteran$time, veteran$status))
# evaluate the model
model_performance(explainer)
# visualize permutation-based feature importance
plot(model_parts(explainer))
# explain one prediction with SurvSHAP(t)
plot(predict_parts(explainer, veteran[1, -c(3, 4)]))
```

If you use `survex`

, please cite it as

M. Spytek, M. Krzyziński, H. Baniecki, P. Biecek.

survex: Explainable Machine Learning in Survival Analysis.R package version 1.0.0, 2023. https://github.com/ModelOriented/survex

```
@article{spytek2023survex,
title = {{survex: Explainable Machine Learning in Survival Analysis}},
author = {Mikołaj Spytek and Mateusz Krzyziński and
Hubert Baniecki and Przemysław Biecek},
journal = {R package version 1.0.0},
year = {2023},
url = {https://github.com/ModelOriented/survex}
}
```

`survex`

- H. Baniecki, B. Sobieski, P. Bombiński, P. Szatkowski, P. Biecek. Hospital Length of Stay Prediction Based on Multi-modal Data towards Trustworthy Human-AI Collaboration in Radiomics.
*International Conference on Artificial Intelligence in Medicine*, 2023. - W. Chen, B. Zhou, C. Y. Jeon, F. Xie, Y-C. Lin, R. K. Butler, Y. Zhou, T. Q. Luong, E. Lustigova, J. R. Pisegna, B. U. Wu. Machine learning versus regression for prediction of sporadic pancreatic cancer.
*Pancreatology*, 2023. - Share it with us!

- H. Ishwaran, U. B. Kogalur, E. H. Blackstone, M. S. Lauer. Random survival forests.
*Annals of Applied Statistics*, 2008. - A. Grudziąż, A. Gosiewska, P. Biecek. survxai: an R package for structure-agnostic explanations of survival models.
*Journal of Open Source Software*, 2018. - M. S. Kovalev, L. V. Utkin, E. M. Kasimov. SurvLIME: A method for explaining machine learning survival models.
*Knowledge-Based Systems*, 2020. - R. Sonabend, F. J. Király, A. Bender, B. Bischl, M. Lang. mlr3proba: an R package for machine learning in survival analysis.
*Bioinformatics*, 2021. - E. Hvitfeldt, H. Frick. censored: ‘parsnip’ Engines for Survival Models.
*CRAN v0.1.0*, 2022. - M. Krzyziński, M. Spytek, H. Baniecki, P. Biecek. SurvSHAP(t): Time-dependent explanations of machine learning survival models.
*Knowledge-Based Systems*, 2023.