bvanaken / clinical-assertion-negation-bert

huggingface.co
Total runs: 11.7K
24-hour runs: 257
7-day runs: 530
30-day runs: 10.1K
Model's Last Updated: Janeiro 17 2025
text-classification

Introduction of clinical-assertion-negation-bert

Model Details of clinical-assertion-negation-bert

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:

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:

@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"
}

Runs of bvanaken clinical-assertion-negation-bert on huggingface.co

11.7K
Total runs
257
24-hour runs
292
3-day runs
530
7-day runs
10.1K
30-day runs

More Information About clinical-assertion-negation-bert huggingface.co Model

clinical-assertion-negation-bert huggingface.co

clinical-assertion-negation-bert huggingface.co is an AI model on huggingface.co that provides clinical-assertion-negation-bert's model effect (), which can be used instantly with this bvanaken clinical-assertion-negation-bert model. huggingface.co supports a free trial of the clinical-assertion-negation-bert model, and also provides paid use of the clinical-assertion-negation-bert. Support call clinical-assertion-negation-bert model through api, including Node.js, Python, http.

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clinical-assertion-negation-bert install

clinical-assertion-negation-bert is an open source model from GitHub that offers a free installation service, and any user can find clinical-assertion-negation-bert on GitHub to install. At the same time, huggingface.co provides the effect of clinical-assertion-negation-bert install, users can directly use clinical-assertion-negation-bert installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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