PROGRESSA

This repository contains the contain to reproduce the results of the paper "Deep Learning-Based Algorithm to Predict Aortic Stenosis Progression from the PROGRESSA cohort"

Install

Install the package and necessary dependencies by running the command:

pip3 install .

or

python3 setup.py build
python3 setup.py install

PREPROCESS

Before training the models, the first step is to run the script to extract the features using the command:

python3 progressa/preprocess/extract_features.py

The next step is to create a file than will contain the indices for the 10 splits that will be used for the repeated holdout method. To achieve this, run the script:

python3 progressa/preprocess/create_splits.py

Then, the features importance is calculated using the script :

python3 progressa/preprocess/feature_importance.py

The most important features returned from running this script were then entered in the select_features.py script. To get the file with selected features only, which will be found in data/features-22.pkl, run:

python3 progressa/preprocess/select_features.py

Train models

To train the RNN model (GRU):

python3 progressa/train_models/RNN.py

To train the machine learning models compared with RNN, use the following command lines:

python3 progressa/train_models/sklearn_models.py --model=naiveBayes python3 progressa/train_models/sklearn_models.py --model=Logistic_Regression python3 progressa/train_models/sklearn_models.py --model=lightgbm python3 progressa/train_models/sklearn_models.py --model=xgboost

To train on 2 visits at a time, modify the precedent commands by adding the command --n_visits=2. For example:

python3 progressa/train_models/sklearn_models.py --model=naiveBayes --n_visits=2

Analysis

To reproduce the analysis from the paper, run the following commands

python3 progressa/analysis/analyse_results.py

python3 progressa/analysis/analyse_results_per_sex.py

python3 progressa/analysis/severity_baseline.py

python3 progressa/analysis/stats.py

python3 progressa/analysis/calibration_plot.py

Create images

To reproduce the images from the paper, run the following commands

python3 progressa/create_images/plot_rocs.py

python3 progressa/create_images/plot_tsne.py