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--- |
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license: mit |
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language: |
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- en |
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library_name: transformers |
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tags: |
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- medical |
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- healthcare |
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- clinical |
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- perioperative care |
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base_model: emilyalsentzer/Bio_ClinicalBERT |
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inference: false |
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--- |
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# BJH-perioperative-notes-bioClinicalBERT |
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This clinical foundational model is intended to predict post-operative surgical outcomes from clinical notes taken during perioperative care. |
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It was finetuned from the `emilyalsentzer/Bio_ClinicalBERT` model through a multi-task learning approach, spanning the following 6 outcomes: |
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- Death in 30 days |
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- Deep vein thrombosis (DVT) |
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- pulmonary embolism (PE) |
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- Pneumonia |
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- Acute Knee Injury |
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- delirium |
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## Dataset |
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We used 84,875 perioperative clinical notes from patients spanning the Barnes Jewish Hospital (BJH) system in St Louis, MO. |
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The following are the characteristics for the data: |
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- vocabulary size: 3203 |
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- averaging words per clinical note: 8.9 words |
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- all single sentenced clinical notes |
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## How to use model |
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``` |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained("cja5553/BJH-perioperative-notes-bioClinicalBERT") |
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model = AutoModel.from_pretrained("cja5553/BJH-perioperative-notes-bioClinicalBERT") |
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``` |
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## Codes |
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Codes used to train the model are publicly available at: https://github.com/cja5553/LLMs_in_perioperative_care |
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## Citation |
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If you find this model useful, please cite the following paper: |
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``` |
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@article{ |
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author={Bing Xue, Charles Alba, Joanna Abraham, Thomas Kannampallil, Christopher King, Michael Avidan, Chenyang Lu} |
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title={"Prescribing Large Language Models for Perioperative Care: What’s The Right Dose for Pretrained Models?"}, |
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year={2024} |
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} |
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``` |
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## Questions? |
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contact me at alba@wustl.edu |
