This is the implementation of MLGAN: a Meta-Learning based Generative Adversarial Network adapter for rare disease differentiation tasks.
dataset folder contains a toy data named as toy_dataset. The toy_dataset is a tuple, we have
diagnosis_codes, time_step, pid, demographic, label = data
python train_eval.py