
# CORe Model - Clinical Mortality Risk Prediction

## Model description

The CORe (_Clinical Outcome Representations_) model is introduced in the paper [Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration](https://www.aclweb.org/anthology/2021.eacl-main.75.pdf).
It is based on BioBERT and further pre-trained on clinical notes, disease descriptions and medical articles with a specialised _Clinical Outcome Pre-Training_ objective.

This model checkpoint is **fine-tuned on the task of mortality risk prediction**.
The model expects patient admission notes as input and outputs the predicted risk of in-hospital mortality.

#### How to use CORe Mortality Risk Prediction

You can load the model via the transformers library:
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("bvanaken/CORe-clinical-mortality-prediction")
model = AutoModelForSequenceClassification.from_pretrained("bvanaken/CORe-clinical-mortality-prediction")
```

The following code shows an inference example:

```
input = "CHIEF COMPLAINT: Headaches\n\nPRESENT ILLNESS: 58yo man w/ hx of hypertension, AFib on coumadin presented to ED with the worst headache of his life."

tokenized_input = tokenizer(input, return_tensors="pt")
output = model(**tokenized_input)

import torch
predictions = torch.softmax(output.logits.detach(), dim=1)
mortality_risk_prediction = predictions[0][1].item()
```


### More Information

For all the details about CORe and contact info, please visit [CORe.app.datexis.com](http://core.app.datexis.com/).

### Cite

```bibtex
@inproceedings{vanaken21,
  author    = {Betty van Aken and
               Jens-Michalis Papaioannou and
               Manuel Mayrdorfer and
               Klemens Budde and
               Felix A. Gers and
               Alexander Löser},
  title     = {Clinical Outcome Prediction from Admission Notes using Self-Supervised
               Knowledge Integration},
  booktitle = {Proceedings of the 16th Conference of the European Chapter of the
               Association for Computational Linguistics: Main Volume, {EACL} 2021,
               Online, April 19 - 23, 2021},
  publisher = {Association for Computational Linguistics},
  year      = {2021},
}
```
{"language": "en", "tags": ["bert", "medical", "clinical", "mortality"], "thumbnail": "https://core.app.datexis.com/static/paper.png"}
transformers pytorch bert text-classification medical clinical mortality en autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #medical #clinical #mortality #en #autotrain_compatible #endpoints_compatible #region-us 


# CORe Model - Clinical Mortality Risk Prediction

## Model description

The CORe (_Clinical Outcome Representations_) model is introduced in the paper Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration.
It is based on BioBERT and further pre-trained on clinical notes, disease descriptions and medical articles with a specialised _Clinical Outcome Pre-Training_ objective.

This model checkpoint is fine-tuned on the task of mortality risk prediction.
The model expects patient admission notes as input and outputs the predicted risk of in-hospital mortality.

#### How to use CORe Mortality Risk Prediction

You can load the model via the transformers library:


The following code shows an inference example:




### More Information

For all the details about CORe and contact info, please visit URL.

### Cite


# CORe Model - Clinical Mortality Risk Prediction ## Model description

The CORe (_Clinical Outcome Representations_) model is introduced in the paper Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration.
It is based on BioBERT and further pre-trained on clinical notes, disease descriptions and medical articles with a specialised _Clinical Outcome Pre-Training_ objective.

This model checkpoint is fine-tuned on the task of mortality risk prediction.
The model expects patient admission notes as input and outputs the predicted risk of in-hospital mortality. #### How to use CORe Mortality Risk Prediction

You can load the model via the transformers library:


The following code shows an inference example: ### More Information

For all the details about CORe and contact info, please visit URL. ### Cite
TAGS
#transformers #pytorch #bert #text-classification #medical #clinical #mortality #en #autotrain_compatible #endpoints_compatible #region-us 
 # CORe Model - Clinical Mortality Risk Prediction ## Model description

The CORe (_Clinical Outcome Representations_) model is introduced in the paper Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration.
It is based on BioBERT and further pre-trained on clinical notes, disease descriptions and medical articles with a specialised _Clinical Outcome Pre-Training_ objective.

This model checkpoint is fine-tuned on the task of mortality risk prediction.
The model expects patient admission notes as input and outputs the predicted risk of in-hospital mortality. #### How to use CORe Mortality Risk Prediction

You can load the model via the transformers library:


The following code shows an inference example: ### More Information

For all the details about CORe and contact info, please visit URL. ### Cite
passage: TAGS
#transformers #pytorch #bert #text-classification #medical #clinical #mortality #en #autotrain_compatible #endpoints_compatible #region-us 
# CORe Model - Clinical Mortality Risk Prediction## Model description

The CORe (_Clinical Outcome Representations_) model is introduced in the paper Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration.
It is based on BioBERT and further pre-trained on clinical notes, disease descriptions and medical articles with a specialised _Clinical Outcome Pre-Training_ objective.

This model checkpoint is fine-tuned on the task of mortality risk prediction.
The model expects patient admission notes as input and outputs the predicted risk of in-hospital mortality.#### How to use CORe Mortality Risk Prediction

You can load the model via the transformers library:


The following code shows an inference example:### More Information

For all the details about CORe and contact info, please visit URL.### Cite