TorchSurv is a Python package that serves as a companion tool to perform deep survival modeling within the PyTorch environment. Unlike existing libraries that impose specific parametric forms on users, TorchSurv enables the use of custom PyTorch-based deep survival models. With its lightweight design, minimal input requirements, full PyTorch backend, and freedom from restrictive survival model parameterizations, TorchSurv facilitates efficient survival model implementation, particularly beneficial for high-dimensional input data scenarios.
In this tutorial, we want to introduce how to easily use our package, from loss functions (Weibull and Cox model), evaluation metrics (concordance-index, AUC, Brier score) and statistical tools (Kaplan-Meier, estimator). This will enable Pytorch users to develop true survival model by changing few lines of code while using their favorite deep learning framework!
Existing tutorials on this topic
The tutorial will be adapted from our existing documentations:
svekars
commented
1 month ago
🚀 Describe the improvement or the new tutorial
TorchSurvis a Python package that serves as a companion tool to perform deep survival modeling within thePyTorchenvironment. Unlike existing libraries that impose specific parametric forms on users,TorchSurvenables the use of customPyTorch-based deep survival models. With its lightweight design, minimal input requirements, fullPyTorchbackend, and freedom from restrictive survival model parameterizations,TorchSurvfacilitates efficient survival model implementation, particularly beneficial for high-dimensional input data scenarios.In this tutorial, we want to introduce how to easily use our package, from
loss functions(Weibull and Cox model),evaluation metrics(concordance-index, AUC, Brier score) andstatistical tools(Kaplan-Meier, estimator). This will enablePytorchusers to develop true survival model by changing few lines of code while using their favorite deep learning framework!Existing tutorials on this topic
The tutorial will be adapted from our existing documentations:
Additional context
category:
survival analysisThis work was made as part of the collaboration research between the
FDAandNovartisFurther read:
RandPythonpackages can be found in this section