readme: ---
  language: "en"
  tags:
  - bert
  - medical
  - clinical
  - assertion
  - negation
  - text-classification
  widget:
  - text: "Patient denies [entity] SOB [entity]."
  
  ---
  
  # Clinical Assertion / Negation Classification BERT
  
  ## Model description
  
  The Clinical Assertion and Negation Classification BERT is introduced in the paper [Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?
  ](https://aclanthology.org/2021.nlpmc-1.5/). The model helps structure information in clinical patient letters by classifying medical conditions mentioned in the letter into PRESENT, ABSENT and POSSIBLE.
  
  The model is based on the [ClinicalBERT - Bio + Discharge Summary BERT Model](https://huggingface.co/emilyalsentzer/Bio_Discharge_Summary_BERT) by Alsentzer et al. and fine-tuned on assertion data from the [2010 i2b2 challenge](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3168320/).
  
  
  #### How to use the model
  
  You can load the model via the transformers library:
  ```
  from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
  tokenizer = AutoTokenizer.from_pretrained("bvanaken/clinical-assertion-negation-bert")
  model = AutoModelForSequenceClassification.from_pretrained("bvanaken/clinical-assertion-negation-bert")
  
  ```
  
  The model expects input in the form of spans/sentences with one marked entity to classify as `PRESENT(0)`, `ABSENT(1)` or `POSSIBLE(2)`. The entity in question is identified with the special token `[entity]` surrounding it.
  
  Example input and inference:
  ```
  input = "The patient recovered during the night and now denies any [entity] shortness of breath [entity]."
  
  classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
  
  classification = classifier(input)
  # [{'label': 'ABSENT', 'score': 0.9842607378959656}]
  ``` 
  
  ### Cite
  
  When working with the model, please cite our paper as follows:
  
  ```bibtex
  @inproceedings{van-aken-2021-assertion,
      title = "Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?",
      author = "van Aken, Betty  and
        Trajanovska, Ivana  and
        Siu, Amy  and
        Mayrdorfer, Manuel  and
        Budde, Klemens  and
        Loeser, Alexander",
      booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations",
      year = "2021",
      address = "Online",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/2021.nlpmc-1.5",
      doi = "10.18653/v1/2021.nlpmc-1.5"
  }
  ```