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--- |
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language: nl |
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license: mit |
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--- |
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# MedRoBERTa.nl finetuned for negation |
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## Description |
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This model is a finetuned RoBERTa-based model pre-trained from scratch on Dutch hospital notes sourced from Electronic Health Records. All code used for the creation of MedRoBERTa.nl can be found at https://github.com/cltl-students/verkijk_stella_rma_thesis_dutch_medical_language_model. The publication associated with the negation detection task can be found at https://arxiv.org/abs/2209.00470. The code for finetuning the model can be found at https://github.com/umcu/negation-detection. |
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## Minimal example |
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```python |
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tokenizer = AutoTokenizer.from_pretrained("UMCU/MedRoBERTa.nl_NegationDetection") |
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model = AutoModelForTokenClassification.from_pretrained("UMCU/MedRoBERTa.nl_NegationDetection") |
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some_text = "De patient was niet aanspreekbaar en hij zag er grauw uit. Hij heeft de inspanningstest echter goed doorstaan." |
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inputs = tokenizer(some_text, return_tensors='pt') |
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output = model.forward(inputs) |
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probas = torch.nn.functional.softmax(output.logits[0]).detach().numpy() |
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# koppel aan tokens |
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input_tokens = tokenizer.convert_ids_to_tokens(inputs['input_ids'][0]) |
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target_map = {0: 'B-Negated', 1:'B-NotNegated',2:'I-Negated',3:'I-NotNegated'} |
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results = [{'token': input_tokens[idx], |
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'proba_negated': proba_arr[0]+proba_arr[2], |
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'proba_not_negated': proba_arr[1]+proba_arr[3] |
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} |
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for idx,proba_arr in enumerate(probas)] |
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``` |
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It perhaps good to note that we assume the [Inside-Outside-Beginning](https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)) format. |
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## Intended use |
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The model is finetuned for negation detection on Dutch clinical text. Since it is a domain-specific model trained on medical data, it is meant to be used on medical NLP tasks for Dutch. This particular model is trained on a 512-max token windows surrounding the concept-to-be negated. |
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## Data |
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The pre-trained model was trained on nearly 10 million hospital notes from the Amsterdam University Medical Centres. The training data was anonymized before starting the pre-training procedure. |
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The finetuning was performed on the Erasmus Dutch Clinical Corpus (EDCC), and can be obtained through Jan Kors ([email protected]). The EDCC is described here: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-014-0373-3 |
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## Authors |
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MedRoBERTa.nl: Stella Verkijk, Piek Vossen, |
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Finetuning: Bram van Es, Sebastiaan Arends. |
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## Contact |
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If you are having problems with this model please add an issue on our git: https://github.com/umcu/negation-detection/issues |
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## Usage |
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If you use the model in your work please refer either to |
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https://doi.org/10.5281/zenodo.6980076 or https://doi.org/10.48550/arXiv.2209.00470 |
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## References |
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Paper: Verkijk, S. & Vossen, P. (2022) MedRoBERTa.nl: A Language Model for Dutch Electronic Health Records. Computational Linguistics in the Netherlands Journal, 11. |
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Paper: Bram van Es, Leon C. Reteig, Sander C. Tan, Marijn Schraagen, Myrthe M. Hemker, Sebastiaan R.S. Arends, Miguel A.R. Rios, Saskia Haitjema (2022): Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods, Arxiv |
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