--- language: "en" tags: - bert - medical - clinical - assertion - negation - text-classification --- # Clinical Assertion / Negation Classification BERT ## Model description The model is introduced in the paper [Assertion Detection in Clinical Notes: Medical Language Models to the Rescue? ](https://aclanthology.org/2021.nlpmc-1.5/). 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/). #### How to use the model You can load the model via the transformers library: ``` from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("bvanaken/clinical-assertion-negation-bert") model = AutoModel.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`, `ABSENT` or `POSSIBLE`. The entity in question is identified with the special token `[entity]` surrounding it. Example input: ``` The patient recovered during the night and now denies any [entity] shortness of breath [entity]. ``` ### Cite ```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" } ```