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Python implementation for paper: "Causal Hidden Markov Model for Time Series Disease Forecasting"(CVPR 2021)

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Causal Hidden Markov Model for Time Series Disease Forecasting

This repository is the python implementation of Causal Hidden Markov Model for Time Series Disease Forecasting(CVPR2021).

The directed acylic graph (DAG) for our Causal-HMM.

The directed acylic graph (DAG) for our Causal-HMM

Re-implement the method on default setting by:

python main.py 

Due to data privacy, our data set is not public here.

Citation

If you found this work useful, please consider citing:

@inproceedings{li2021causal,
  title={Causal Hidden Markov Model for Time Series Disease Forecasting},
  author={Jing Li, Botong Wu, Xinwei Sun, Yizhou Wang},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2021}
}

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Python implementation for paper: "Causal Hidden Markov Model for Time Series Disease Forecasting"(CVPR 2021)

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