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StageNet: Stage-Aware Neural Networks for Health Risk Prediction

The source code for StageNet: Stage-Aware Neural Networks for Health Risk Prediction

Visualization

Welcome to test the prototype of our visualization tool:

http://47.93.42.104/598
http://47.93.42.104/664 (Alive)

Requirements

• Install python, pytorch. We use Python 3.7.3, Pytorch 1.1.
• If you plan to use GPU computation, install CUDA

Data preparation

We do not provide the MIMIC-III data itself. You must acquire the data yourself from https://mimic.physionet.org/. Specifically, download the CSVs. To run decompensation prediction task on MIMIC-III bechmark dataset, you should first build benchmark dataset according to https://github.com/YerevaNN/mimic3-benchmarks/.

After building the decompensation dataset, please save the files in decompensation directory to data/ directory.

• We provide some data SAMPLES in the folder, so that you can understand the data structure.

Fast way to test StageNet with MIMIC-III

1. We provide the trained weights in ./saved_weights/StageNet. You can obtain the reported performance in our paper by simply load the weights to the model.

2. You need to run train.py in test mode and input the data directory. For example,

   $ python train.py --test_mode=1 --data_path='./data/'

Training StageNet

1. The minimum input you need to train StageNet is the dataset directory and file name to save model. For example,

   $ python train.py --data_path='./data/' --file_name='trained_model'

2. You can also specify batch size --batch_size <integer> , learning rate --lr <float> and epochs --epochs <integer>

3. Additional hyper-parameters can be specified such as the dimension of RNN, dropout rate, etc. Detailed information can be accessed by

   $ python train.py --help

4. When training is complete, it will output the performance of StageNet on test dataset.

Citation

Junyi Gao, Cao Xiao, Yasha Wang, Wen Tang, Lucas M. Glass, Jimeng Sun. 2020. 
StageNet: Stage-Aware Neural Networks for Health Risk Prediction. 
In Proceedings of The Web Conference 2020 (WWW ’20), April 20–24, 2020, Taipei, Taiwan. ACM, New York, NY, USA, 11 pages. 
https://doi.org/10.1145/3366423.3380136