This repository allows to reproduce the results in Neural Survival Clustering: Non parametric mixture of neural networks for survival clustering This paper approaches survival analysis as a mixture of neural baselines whereby different baseline cumulative hazard functions are modelled using positive and monotone neural networks.

The model consists in a mixture of K neural networks that model different population-level survival distributions, and an assignment neural network that weights these different mixtures given the input data.

To use the model, one needs to execute:

from nsc import NeuralSurvivalClustering
model = NeuralSurvivalClustering()
model.fit(x, t, e)
model.predict_risk(x)

A full example with analysis is provided in examples/Neural Survival Clustering on SUPPORT Dataset.ipynb.

To reproduce the paper's results:

0. Clone the repository with dependencies: git clone [email protected]:Jeanselme/NeuralSurvivalClustering.git --recursive
1. Create a conda environment with all necessary libraries pycox, lifelines, pysurvival.
2. Add path export PYTHONPATH="$PWD:$PWD/DeepSurvivalMachines:$PWD/SuMo-net:$PYTHONPATH"
3. Run examples/experiments_paper.py METABRIC
4. Repeat with SUPPORT and SYNTHETIC
5. Analysis using examples/Analysis.ipynb

Adding a new method consists in adding a child to Experiment in experiment.py with functions to compute the nll and fit the model. Then, add the method in examples/experiments_paper.py and follow the previous point. TODOs have been added to make the addition of a new method easier.

• Competing risks.
• Post analysis: Replacing assignment neural network by more interpretable approach.

We followed the same structure than the DeepSurvivalMachines repository with the model in nsc/ - only the api should be used to test the model. Examples are provided in examples/.

git clone [email protected]:Jeanselme/NeuralSurvivalClustering.git --recursive

The model relies on DeepSurvivalMachines, SuMo-net, pytorch, numpy and tqdm.
To run the set of experiments pycox, lifelines, pysurvival are necessary.