DeepSurv

DeepSurv implements a deep learning generalization of the Cox proportional hazards model using Theano and Lasagne.

DeepSurv has an advantage over traditional Cox regression because it does not require an a priori selection of covariates, but learns them adaptively.

DeepSurv can be used in numerous survival analysis applications. One medical application is provided: recommend_treatment, which provides treatment recommendations for a set of patient observations.

For more details, see full paper DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network.

Installation:

Dependencies

Theano, Lasagne (bleeding edge version), lifelines, matplotlib (for visualization) and all of their respective dependencies.

Installing

You can install DeepSurv using

pip install deepsurv

from the command line.

Dependencies

DeepSurv has been succesfully installed and tested on macOS Sierra 10.12.1 with the follow versions of Python packages:

    'theano==0.8.2',
    'lasagne==0.2.dev1',
    'lifelines==0.9.2',

Running the tests

After installing, you can optionally run the test suite with

py.test

from the command line while in the module main directory.

Training a Network

Training DeepSurv can be done in a few lines. First, all you need to do is prepare the datasets to have the following keys:

{ 
	'x': (n,d) observations (dtype = float32), 
 	't': (n) event times (dtype = float32),
 	'e': (n) event indicators (dtype = int32)
}

Then prepare a dictionary of hyper-parameters. And it takes only two lines to train a network:

network = deepsurv.DeepSurv(**hyperparams)
log = network.train(train_data, valid_data, n_epochs=500)

You can then evaluate its success on testing data:

network.get_concordance_index(**test_data)
>> 0.62269622730138632

If you have matplotlib installed, you can visualize the training and validation curves after training the network:

deepsurv.plot_log(log)

Running Experiments

Experiments are run using Docker containers built off of the floydhub deep learning Docker images. DeepSurv can be run on either the CPU or the GPU with nvidia-docker.

To run an experiment, define the experiment name as an environmental variable EXPRIMENTand run the docker-compose file.