|
--- |
|
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" |
|
} |
|
``` |
