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--- |
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'[object Object]': null |
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license: apache-2.0 |
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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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widget: |
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- text: "Patient is a a formerly incarcerated individual having arrived in the ED with stomach pain." |
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- example_title: "Former Incarceration" |
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- text: "Patient arrived in the ED for chest pain." |
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- example_title: "No Incarceration" |
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--- |
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# Model Card for incar-status-any |
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A Clinical Longformer-based model trained by the HAIL lab to predict incarceration status (past and present) in ED Notes. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** Vimig Socrates |
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- **Model type:** Longformer |
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- **Language(s) (NLP):** English |
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- **License:** Apache License 2.0 |
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- **Finetuned from model:** [Clinical Lonformer](https://huggingface.co/yikuan8/Clinical-Longformer |
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) |
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## Uses |
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This model can be used to predict the incarceration status that a patient might have given most types of clinical ED notes. |
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## Bias, Risks, and Limitations |
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This should not be used directly without supervision from a physician as predicting incarceration status incorrectly can have significant negative social and clinical impacts. |
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## Training Details |
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### Training Data |
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This model was trained on custom annotated data labeled for incarceration status from Yale-New Haven Health Hospital System ED Notes. |
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### Training Procedure |
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## Evaluation |
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TODO |
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### Testing Data, Factors & Metrics |
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### Results |
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TODO |
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] |
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## Citation [optional] |
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Coming soon! |
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**BibTeX:** |
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{{ citation_bibtex | default("[More Information Needed]", true)}} |
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**APA:** |
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{{ citation_apa | default("[More Information Needed]", true)}} |
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## Model Card Authors [optional] |
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Vimig Socrates |
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## Model Card Contact |
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Vimig Socrates: [vimig.socrates@yale.edu](mailto:vimig.socrates@yale.edu) |
