Notebooks for medical named entity recognition with BERT and Flair, used in the article "A clinical trials corpus annotated with UMLS entities to enhance the access to Evidence-Based Medicine".
Update (2022): The annotated data and the BERT trained model is now available in the Huggingface hub.
- Python 3.6+
- Pytorch 1.3+
- Huggingface Transformers (tested with version 2.9)
- Flair (tested with version 0.6)
- Matplotlib (tested with version 3.2)
- Numpy (tested with version 1.17.4)
- Pandas (tested with version 0.20)
- Seaborn (tested with version 0.9)
- Seqeval (tested with version 0.12)
- A Graphical Processing Unit (GPU)
These code is inspired by the following resources:
https://github.com/Spain-AI/transformers (by Álvaro Barbero)
https://www.depends-on-the-definition.com/named-entity-recognition-with-bert/ (by Tobias Sterbak)
Check also the Flair tutorials at:
https://github.com/flairNLP/flair
If you use this code, you can make reference to the article where the script was made available, as follows:
A clinical trials corpus annotated with UMLS entities to enhance the access to Evidence-Based Medicine
Leonardo Campillos-Llanos, Ana Valverde-Mateos, Adrián Capllonch-Carrión, Antonio Moreno-Sandoval
BMC Medical Informatics and Decision Making 21, 69 (2021)
@article{campillosetal-midm2021,
title = {A clinical trials corpus annotated with UMLS entities to enhance the access to Evidence-Based Medicine},
author = {Campillos-Llanos, Leonardo and Valverde-Mateos, Ana and Capllonch-Carri{\'o}n, Adri{\'a}n and Moreno-Sandoval, Antonio},
journal = {BMC Medical Informatics and Decision Making},
volume = {21},
number = {69},
year = {2021}
}