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--- |
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language: de |
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tags: |
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- negation |
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- speculation |
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- cross-lingual |
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- bert |
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- clinical/medical |
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- text-classification |
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extra_gated_prompt: >- |
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You agree to not use the model to conduct experiments that cause harm to human |
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subjects, i.e. attempting to misuse clinical data or re-identify any sensible |
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data. |
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extra_gated_fields: |
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Company: text |
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Country: text |
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Name: text |
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Email: text |
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I agree to use this model for non-commercial use ONLY: checkbox |
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pipeline_tag: text-classification |
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--- |
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# FactualMedBERT-DE: Clinical Factuality Detection BERT model for German language |
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## Model description |
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FactualMedBERT-DE is the first pre-trained language model to address factuality/assertion detection problem in German clinical texts (primarily discharge summaries). |
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It is introduced in the paper [Factuality Detection using Machine Translation - a Use Case for German Clinical Text](https://arxiv.org/abs/2308.08827). The model classifies tagged medical conditions based |
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on their factuality value. It can support label classification of `Affirmed`, `Negated` and `Possible`. |
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It was intialized from [smanjil/German-MedBERT](https://huggingface.co/smanjil/German-MedBERT) German language model and |
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was trained on a translated subset data of [the 2010 i2b2/VA assertion challenege](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3168320/). |
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## How to use the model |
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- You might need to authenticate and login before being able to download the model (see more [here](https://huggingface.co/docs/huggingface_hub/quick-start)) |
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- Get the model using the transformers library |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("binsumait/factual-med-bert-de") |
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model = AutoModelForSequenceClassification.from_pretrained("binsumait/factual-med-bert-de") |
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``` |
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- Predict an instance by pre-tagging the factuality target (ideally a medical condition) with `[unused1]` special token: |
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```python |
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from transformers import TextClassificationPipeline |
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instance = "Der Patient hat vielleicht [unused1] Fieber [unused1]" |
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factuality_pipeline = TextClassificationPipeline(model=model, tokenizer=tokenizer) |
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print(factuality_pipeline(instance)) |
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``` |
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which should output: |
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`[{'label': 'possible', 'score': 0.9744388461112976}]` |
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## Cite |
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If you use our model, please cite your paper as follows: |
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```bibtex |
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@inproceedings{bin_sumait_2023, |
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title={Factuality Detection using Machine Translation - a Use Case for German Clinical Text}, |
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author={Bin Sumait, Mohammed and Gabryszak, Aleksandra and Hennig, Leonhard and Roller, Roland}, |
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booktitle={Proceedings of the 19th Conference on Natural Language Processing (KONVENS 2023)}, |
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year={2023} |
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} |
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``` |
