---
'[object Object]': null
license: apache-2.0
language:
- en
library_name: transformers
tags:
- medical
widget: 
- text: "Patient is a a formerly incarcerated individual having arrived in the ED with stomach pain."
- example_title: "Former Incarceration"
- text: "Patient arrived in the ED for chest pain."
- example_title: "No Incarceration"

---

# Model Card for incar-status-any

A Clinical Longformer-based model trained by the HAIL lab to predict incarceration status (past and present) in ED Notes. 

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->


- **Developed by:** Vimig Socrates
- **Model type:** Longformer
- **Language(s) (NLP):** English
- **License:** Apache License 2.0
- **Finetuned from model:** [Clinical Lonformer](https://huggingface.co/yikuan8/Clinical-Longformer
)

## Uses

This model can be used to predict the incarceration status that a patient might have given most types of clinical ED notes. 

## Bias, Risks, and Limitations

This should not be used directly without supervision from a physician as predicting incarceration status incorrectly can have significant negative social and clinical impacts. 

## Training Details

### Training Data

This model was trained on custom annotated data labeled for incarceration status from Yale-New Haven Health Hospital System ED Notes. 

### Training Procedure

## Evaluation

TODO

### Testing Data, Factors & Metrics


### Results

TODO

]
## Citation [optional]

Coming soon! 

**BibTeX:**

{{ citation_bibtex | default("[More Information Needed]", true)}}

**APA:**

{{ citation_apa | default("[More Information Needed]", true)}}


## Model Card Authors [optional]

Vimig Socrates

## Model Card Contact

Vimig Socrates: [vimig.socrates@yale.edu](mailto:vimig.socrates@yale.edu)