
# 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"
}
```
{"language": "en", "tags": ["bert", "medical", "clinical", "assertion", "negation", "text-classification"], "widget": [{"text": "Patient denies [entity] SOB [entity]."}]}
transformers pytorch bert text-classification medical clinical assertion negation en autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #text-classification #medical #clinical #assertion #negation #en #autotrain_compatible #endpoints_compatible #has_space #region-us 


# 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?
. 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 by Alsentzer et al. and fine-tuned on assertion data from the 2010 i2b2 challenge.


#### How to use the model

You can load the model via the transformers library:


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:
 

### Cite

When working with the model, please cite our paper as follows:


# 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?
. 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 by Alsentzer et al. and fine-tuned on assertion data from the 2010 i2b2 challenge. #### How to use the model

You can load the model via the transformers library:


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: ### Cite

When working with the model, please cite our paper as follows:
TAGS
#transformers #pytorch #bert #text-classification #medical #clinical #assertion #negation #en #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # 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?
. 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 by Alsentzer et al. and fine-tuned on assertion data from the 2010 i2b2 challenge. #### How to use the model

You can load the model via the transformers library:


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: ### Cite

When working with the model, please cite our paper as follows:
passage: TAGS
#transformers #pytorch #bert #text-classification #medical #clinical #assertion #negation #en #autotrain_compatible #endpoints_compatible #has_space #region-us 
# 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?
. 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 by Alsentzer et al. and fine-tuned on assertion data from the 2010 i2b2 challenge.#### How to use the model

You can load the model via the transformers library:


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:### Cite

When working with the model, please cite our paper as follows: