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MTGAN

Code for the paper: Multi-Label Clinical Time-Series Generation via Conditional GAN

Commands

Preprocess Data

  1. Go to https://mimic.physionet.org/ for access. Once you have the authority for the dataset, download the dataset and extract the csv files to {data_path}/mimic3/raw/ and {data_path}/mimic4/raw/.
  2. Check hyper-parameter settings:
python run_preprocess.py --help
  1. Run preprocess program:
  • For MIMIC-III
python run_preprocess.py --dataset mimic3 --train_num 6000
  • For MIMIC-IV
python run_preprocess.py --dataset mimic4 --train_num 6000 --sample_num 10000

Add --from_saved if you run it again and want to load saved encoded data.

Training MTGAN

  1. Check hyper-parameter setting:
python run_train.py --help
  1. Run training program
  • For MIMIC-III
python run_train.py --dataset mimic3
  • For MIMIC-IV
python run_train.py --dataset mimic4
  1. The parameters are saved in default at results/{dataset}/params/. The training plots are saved at results/{dataset}/records/.

Generating EHR data

  1. Check hyper-parameter setting:
python run_generate.py --help
  1. Run generating program
  • For MIMIC-III
python run_generate.py --dataset mimic3
  • For MIMIC-IV
python run_generate.py --dataset mimic4
  1. The generated data are saved at results/synthetic_{dataset}.npz.

Requirements:

Environment

  • Python >= 3.7
  • Virtualenv (optional, recommended)
  • CUDA (optional, recommended)
  • RAM > 16GB

Installing packages:

  • matplotlib
  • numpy
  • openpyxl
  • pandas
  • scipy
  • pytorch