readme: ---
  language: "en"
  tags:
  - bert
  - medical
  - clinical
  - mortality
  thumbnail: "https://core.app.datexis.com/static/paper.png"
  ---
  
  # 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},
  }
  ```