donalee / DualAAE-EHR

Dual Adversarial Autoencoder for Generating Set-valued Sequences

Dual Adversarial AutoEncoder (DAAE) for EHR Generation

• This is a PyTorch implementation of "Generating sequential electronic health records using dual adversarial autoencoder", published in Journal of the American Medical Informatics Association (JAMIA) 2020.

Overview

The DAAE architecture that is composed of 1) the seq2seq autoencoder, 2) the inner GAN, and 3) the outer GAN.

The detailed architecture of the seq2seq autoencoder. The embedding layer encodes the semantics of all the medical entities, and the GRU layer learns the temporal contexts within patients' sequential records.

Running the Code

Step 1. Installing Python and PyTorch

• Install Python 3 and PyTorch.

Step 2. Preparing Data

• You need .npy (numpy file format) files of EHR.
• It should be in the form of a dictionary, whose key is the patient-id and value is the set-valued sequence of the patient.
• For example,

  {   17: [['7455', '45829', 'V1259', '2724'], ['4239', '5119', '78551', '4589', '311', '7220', '71946', '2724']],
      21: [['41071', '78551', '42731', '1122', '2720', '2749', 'V1046', '43889'], ['0388', '78552', '40391', '42731',  '25000', '2859', '43889', '2749', '41401', '185', '4439', '2449']],
      ...,
   99982: [['42823', '4254', '2875', '5303', '4280', 'V5861', '45829'], ['4280', '42823', '5849', '4254', '2763', '42731', '78729', '2768'], ['5849', '42731', '4280', '2875','V422', '7994']]}

• Each entity (i.e., diagnosis code) must be provided as string format.
• In the input data directory, two .npy files should be required respecitvely for training and validation: ehr.train.npy and ehr.valid.npy.

Step 3. Running DAAE on your Data

• Now, you are ready to learn the sequences about real patients and synthesize fake ones.

  python train_daae.py --data_dir <input_directory> --create_data --gendata_size <#_fake_sequences>

• Finally, you will obtain the two files about fake patients: daae_generated_codes.npy and daae_generated_patients.npy.
• These are basic arguments you can specify:

Argument	Description
--data_dir	The path to the input nummpy files
--max_sequence_length	The maximum length of sequential records (default: 10)
--max_visit_length	The maximum number of entities in a single record (default: 40)
--gendata_size	The number of fake sequences to be generated (default: 100,000)
--batch_size	The size of a single mini-batch for set-valued sequences (default: 32)
--learning_rate	The initial learning rate for the ADMM optimizer (default: 0.001)
--epochs	The total number of epochs for training (default: 500)
--gpu_devidx	The index of GPU to be used (default: 0)

• The detailed architecture of each componenet (i.e., encoder, decoder, generator, and inner/outer critics) can be specified by using the following arguments:

Argument	Description
--rnn_type	The type of RNN encoder and decoder (default: 'gru')
--embedding_size	The dimensionality of the entity embedding layer (default: 128)
--hidden_size	The dimensionality of hidden vectors in RNN encoder and decoder (default: 128)
--noise_size	The dimensionality of noise vectors (default: 128)
--latent_size	The dimensionality of code vectors (default: 128)
--filter_size	The number of conv filters in the outer critic (default: 16)
--window_sizes	The widths of conv filters in the outer critic (default: [2, 3])
--bidirectional	The flag indicating the RNN encoder is bidirectional (default: False)
--entity_dropout	The entity dropout rate for decoder input sequences (default: 0.05)
--embedding_dropout	The dropout rate for the entity embedding layer (default: 0.5)
--feature_dropout	The dropout rate for the fc layer in the outer critic (default: 0.5)
--gmlp_archs	The MLP architecture of the generator (default: [128, 128])
--dmlp_archs	The MLP architecture of the inner critic (default: [256, 128])

Step 4. Using a differentially-private (DP) optimizer

• If you need to guarantee the synthetic samples do not leak private information about the training data, you can use a DP optimizer when training the model.
• Install the PyVacy package and run the code with --dp_sgd set to True.
• You can control the privacy budget by the following arguments: --noise_multiplier, --l2_norm_clip, and --delta.
• Note that there is a trade-off between the privacy guarantee and the quality of generated sequences.

Citation

@article{lee2020generating,
  author = {Lee, Dongha and Yu, Hwanjo and Jiang, Xiaoqian and Rogith, Deevakar and Gudala, Meghana and Tejani, Mubeen and Zhang, Qiuchen and Xiong, Li},
  title = {Generating sequential electronic health records using dual adversarial autoencoder},
  journal = {Journal of the American Medical Informatics Association},
  volume = {27},
  number = {9},
  pages = {1411-1419},
  year = {2020},
  month = {09}
}