Shield:
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
CLOPS is a framework that allows neural networks to continually learn from clinical data streaming in over time.
This repository contains the PyTorch implementation of CLOPS. For details, see A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions. [Nature Communications Paper], [blogpost]
The CLOPS code requires the following:
- Python 3.6 or higher
- PyTorch 1.0 or higher
The datasets can be downloaded from the following links:
In order to pre-process the datasets appropriately for CLOPS, please refer to the following repository
To train the model(s) in the paper, run this command:
python run_experiments.py
To evaluate the model(s) in the paper, run this command:
python run_experiments.py
If you use our code in your research, please consider citing with the following BibTex.
@article{kiyasseh2021clinical,
title={A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions},
author={Kiyasseh, Dani and Zhu, Tingting and Clifton, David},
journal={Nature Communications},
volume={12},
number={1},
pages={1--11},
year={2021},
publisher={Nature Publishing Group}
}