Conditional Neural ODE Processes for Disease Progression Forecasting

Code for "Conditional Neural ODE Processes for Individual Disease Progression Forecasting: A Case Study on COVID-19" (Submission to KDD 2023).

Note: this will be continuously updated.

Getting started

For development, we used Python 3.9.13 and PyTorch 1.12.1. First, install PyTorch` using the official page and then run the following command to install the required packages:

pip install -r requirements.txt

Running the experiments

To run the experiments:

python CNDP_covid.py --lr 1e-4 --decay 0.95 --is_aug True --inputdim 385 --posweight 1.5  --varname CNDPtest --user_y0

Datasets

For more details on the COVID-19 dataset, please refer to:

@article{dang2022exploring,
  title={Exploring longitudinal cough, breath, and voice data for COVID-19 progression prediction via sequential deep learning: model development and validation},
  author={Dang, Ting and Han, Jing and Xia, Tong and Spathis, Dimitris and Bondareva, Erika and Siegele-Brown, Chlo{\"e} and Chauhan, Jagmohan and Grammenos, Andreas and Hasthanasombat, Apinan and Floto, R Andres and others},
  journal={Journal of medical Internet research},
  volume={24},
  number={6},
  pages={e37004},
  year={2022},
  publisher={JMIR Publications Toronto, Canada}
}

Credits

Our code relies to a great extent on the Neural ODE Processes by Cristian Bodnar.