# audit-icu-gpt2-131_6M 

This repo contains the model weights for audit-icu-gpt2-131_6M, a tabular language model built on the gpt2 architecture
for evaluating the cross-entropy of Epic EHR audit log event sequences. This model was originally designed to 
calculate cross-entropies but can also be used for generation.

The code to train and perform inference this model is available [here](https://github.com/bcwarner/audit-log-lm).
More details about how to use this model can be found there.

# Model Details

More details can be found in the model card of our paper in Appendix B [here](https://arxiv.org/abs/2311.06401).

Please cite our paper if you use this model in your work:
```

@misc{warner2023autoregressive,
      title={Autoregressive Language Models For Estimating the Entropy of Epic EHR Audit Logs},
      author={Benjamin C. Warner and Thomas Kannampallil and Seunghwan Kim},
      year={2023},
      eprint={2311.06401},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

```

{"license": "apache-2.0", "tags": ["tabular-regression", "ehr", "transformer", "medical"], "model_name": "audit-icu-gpt2-25_3M"}
transformers pytorch gpt2 tabular-regression ehr transformer medical arxiv:2311.06401 license:apache-2.0 endpoints_compatible text-generation-inference region:us