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--- |
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language: "en" |
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tags: |
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- bert |
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- medical |
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- clinical |
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- assertion |
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- negation |
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- text-classification |
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--- |
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# Clinical Assertion / Negation Classification BERT |
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## Model description |
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The model is introduced in the paper [Assertion Detection in Clinical Notes: Medical Language Models to the Rescue? |
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](https://aclanthology.org/2021.nlpmc-1.5/). |
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It 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/). |
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#### How to use the model |
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You can load the model via the transformers library: |
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``` |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained("bvanaken/clinical-assertion-negation-bert") |
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model = AutoModel.from_pretrained("bvanaken/clinical-assertion-negation-bert") |
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``` |
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The model expects input in the form of spans/sentences with one marked entity to classify as `PRESENT`, `ABSENT` or `POSSIBLE`. The entity in question is identified with the special token `[entity]` surrounding it. |
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Example input: |
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``` |
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The patient recovered during the night and now denies any [entity] shortness of breath [entity]. |
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``` |
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### Cite |
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```bibtex |
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@inproceedings{van-aken-2021-assertion, |
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title = "Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?", |
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author = "van Aken, Betty and |
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Trajanovska, Ivana and |
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Siu, Amy and |
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Mayrdorfer, Manuel and |
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Budde, Klemens and |
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Loeser, Alexander", |
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booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations", |
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year = "2021", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.nlpmc-1.5", |
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doi = "10.18653/v1/2021.nlpmc-1.5" |
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} |
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``` |
