Medical Named Entity Recognition

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".

Workspace requirements

• 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)

Acknowledgements

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

How to cite

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}
  }