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README.md
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pipeline_tag: text-classification
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tags:
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- medical
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---
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-
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pipeline_tag: text-classification
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tags:
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- medical
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base_model: "emilyalsentzer/Bio_ClinicalBERT"
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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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