Paper Link: https://arxiv.org/abs/2302.08091
PhysioNet Link: https://physionet.org/content/clinical-t5/1.0.0/
Citation:
@article{Lehman2023DoWS,
title={Do We Still Need Clinical Language Models?},
author={Eric P. Lehman and Evan Hernandez and Diwakar Mahajan and Jonas Wulff and Micah J. Smith and Zachary M. Ziegler and Daniel Nadler and Peter Szolovits and Alistair E. W. Johnson and Emily Alsentzer},
journal={ArXiv},
year={2023},
volume={abs/2302.08091}
}
For this paper, I used Python 3.9. Install the requirements.txt
. You may need to install jax and jaxlib separately.
Most of the code for this is from Huggingface. To run the pre-processing, please download the proper files, and then run preprocessing_notes_tag_insertion.sh
.
- You will need to get the
radqa.csv
+discharge.csv
from MIMIC-IV. noteevents.csv
is from MIMIC-III.- You will need to download thr carevue dataset from here: https://physionet.org/content/mimic3-carevue/1.4/
For actually training the models, please see the scripts in scripts/pretraining
.
The hyperparameters are described fully in the paper. However, for the T5 models, we used a learning rate of 1e-4. For BioClinRoBERTa, we also explore the hyperparameters suggested in their paper. We did a similar thing for PubMedGPT. All code for running the finetune can be seen in src/finetuning/
.
Seeds used were all between 1-10, but mostly were 1-3. There might have been some cases in which 4-9 were used, but I would need to go back and check. If trying to recreate the experiments exactly, please email [email protected]
, and I can send you all of my Wandb files, which contain the seeds used. I have them all in my folder, but just don't have the time right now to go through and record.
You will not be able to re-create any of the OpenAI experiments... Even with a temperature of 0.0, OpenAI is injecting an internal temperature! The unfortunate reality of 2023!
Feel free to email [email protected]
with any questions / concerns.