readme: ---
  language: "en"
  tags:
  - bert
  - medical
  - clinical
  thumbnail: "https://core.app.datexis.com/static/paper.png"
  ---
  
  # CORe Model - BioBERT + Clinical Outcome Pre-Training
  
  ## 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.
  
  #### How to use CORe
  
  You can load the model via the transformers library:
  ```
  from transformers import AutoTokenizer, AutoModel
  tokenizer = AutoTokenizer.from_pretrained("bvanaken/CORe-clinical-outcome-biobert-v1")
  model = AutoModel.from_pretrained("bvanaken/CORe-clinical-outcome-biobert-v1")
  ```
  From there, you can fine-tune it on clinical tasks that benefit from patient outcome knowledge.
  
  ### Pre-Training Data
  
  The model is based on [BioBERT](https://huggingface.co/dmis-lab/biobert-v1.1) pre-trained on PubMed data.
  The _Clinical Outcome Pre-Training_ included discharge summaries from the MIMIC III training set (specified [here](https://github.com/bvanaken/clinical-outcome-prediction/blob/master/tasks/mimic_train.csv)), medical transcriptions from [MTSamples](https://mtsamples.com/) and clinical notes from the i2b2 challenges 2006-2012. It  further includes ~10k case reports from PubMed Central (PMC), disease articles from Wikipedia and article sections from the [MedQuAd](https://github.com/abachaa/MedQuAD) dataset extracted from NIH websites.
  
  ### 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},
  }
  ```