README.md

---
language: nl
license: mit
---

# MedRoBERTa.nl finetuned for negation

## 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.

## Intended use
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.  

## 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), and 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, Sebastiaan Arends.

## Usage

If you use the model in your work please refer either to 
https://doi.org/10.5281/zenodo.6980076 or https://doi.org/10.48550/arXiv.2209.00470

## References
Paper: Verkijk, S. & Vossen, P. (2022) MedRoBERTa.nl: A Language Model for Dutch Electroniz 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