HALO

This is the source code for reproducing the inpatient dataset experiments found in the paper "Synthesizing Extremely High Dimensional Electronic Health Records."

Generating the Dataset

This code interfaces with the pubilc MIMIC-III ICU stay database. Before using the code, you will need to apply, complete training, and download the ADMISSIONS and DIAGNOSES_ICD tables from https://physionet.org. From there, generate an empty directory data/, edit the mimic_dir variable in the file build_dataset.py, and run that file. It will generate all of the relevant data files.

Training a Model

Next, a model can be training by creating an empt save/ directory and running the train_model.py script.

Training Baseline Models

Next, any desired baseline models may be trained by changing your working directory to baselines/{baseline_model} and running the corresponding train_{baseline_model}.py script

Evaluating the Model(s)

Finally, the trained model and its synthetic data may be evaluated. Before beginning, create the following directory paths:

• results/datasets
• results/dataset_stats/plots
• results/testing_stats
• results/synthetic_training_stats
• results/privacy_evaluations

After these directories are created, first run the test_model.py script (along with any corresponding test_{baseline_model}.py in the directories from the previous section). This will generate perplexity, prediction, and synthetic dataset results. From there, you may run any other evaluation scripts (prefixed with evaluate_), making sure any references to unrun baseline models are commented out. All corresponding results will be printed and saved to pickle files.