transformers safetensors roberta token-classification medical nl arxiv:2209.00470 license:gpl-3.0 autotrain_compatible endpoints_compatible region:us
---
language:
- nl
license: gpl-3.0
pipeline_tag: token-classification
tags:
- medical
---


# MedRoBERTa.nl finetuned for experiencer

## Description
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.


## Minimal example

```python
tokenizer = AutoTokenizer\
             .from_pretrained("UMCU/MedRoBERTa.nl_Experiencer")
model = AutoModelForTokenClassification\
            .from_pretrained("UMCU/MedRoBERTa.nl_Experiencer")

some_text = "De patient was niet aanspreekbaar en hij zag er grauw uit. \
Hij heeft de inspanningstest echter goed doorstaan. \
De broer heeft onlangs een operatie ondergaan."

inputs = tokenizer(some_text, return_tensors='pt')
output = model.forward(inputs)
probas = torch.nn.functional.softmax(output.logits[0]).detach().numpy()

#  associate with tokens
input_tokens = tokenizer.convert_ids_to_tokens(inputs['input_ids'][0])
target_map = {0: 'B-Patient', 1:'B-Other',2:'I-Patient',3:'I-Other'}
results = [{'token': input_tokens[idx],
                 'proba_patient': proba_arr[0]+proba_arr[2],
                 'proba_other': proba_arr[1]+proba_arr[3]
                 }  
                 for idx,proba_arr in enumerate(probas)]

```

The medical entity classifiers are (being) integrated in the opensource library [clinlp](https://github.com/umcu/clinlp), feel free to contact
us for access, either through Huggingface or through git.

It is 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.

## Intended use
The model is finetuned for experiencer 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 64-max token windows surrounding the concept-to-be labeled. 


## Data
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. 

The finetuning was performed on the Erasmus Dutch Clinical Corpus (EDCC), which was synthetically upsampled for the minority classses.
The EDCC can be obtained through Jan Kors (j.kors@erasmusmc.nl). 
The EDCC is described here: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-014-0373-3

## Authors

MedRoBERTa.nl: Stella Verkijk, Piek Vossen,
Finetuning: Bram van Es

## Contact

If you are having problems with this model please add an issue on our git: https://github.com/umcu/negation-detection/issues

## Usage

If you use the model in your work please use the following referral; https://doi.org/10.1186/s12859-022-05130-x

## References
Paper: Verkijk, S. & Vossen, P. (2022) MedRoBERTa.nl: A Language Model for Dutch Electronic Health Records. Computational Linguistics in the Netherlands Journal, 11.

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