Code for https://arxiv.org/abs/2304.13017
MIMIC-IV patient ID train and test splits are provided in mimic-iv-patient-split.json and were produced using a random shuffle of patient IDs from hosp/patients.csv. For our validation split, we use the 15% of patient IDs that appear first in the train split. For PhysioNet, we use the torchtime library to load splits.
To run both pretraining and fine-tuning on PhysioNet-2012, run python train.py. Our results were generated with PyTorch 1.13.1, PyTorch Lightning 1.6.1, CUDA 11.7.1, x-transformers 1.5.3, and torchtime 0.5.1.
The data format used by the model has instances of the form (x,y), where x is a tuple (x_ts, x_static, times). x_ts is a matrix of size n_timesteps x d_time_series*2 already in the binned format specified in the paper, with x_ts[:,:d_time_series] containing the zero-imputed binned time series values and x_ts[:,d_time_series:] containing the corresponding numbers of observations. x_static is a vector of all static variables and times is a vector of bin end times in (fractional) days.
Note that the forward function of the model doesn't take this representation directly, since further preprocessing is required depending on whether the model is being pretrained or fine-tuned. See the model's training_step method for details.