BSC-NLP4BIA/drug-ner-cat-v1

DRUG-NER-CAT

Table of contents

Click to expand

• Model description
• How to use
• Limitations and bias
• Training
• Evaluation
• Additional information
  • Authors
  • Contact information
  • Licensing information
  • Funding
  • Citing information
  • Disclaimer

Model description

A fine-tuned version of the bsc-bio-ehr-es model on the DrugTEMIST corpus (Catalan Gold Standard Corpus).

For further information, check the official website.

How to use

⚠ We recommend pre-tokenizing the input text into words instead of providing it directly to the model, as this is how the model was trained. Otherwise, the results and performance might get affected.

A usage example can be found here.

Limitations and bias

At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.

Training

The model was trained using the Barcelona Supercomputing Center infrastructure.

Evaluation

F1 Score: 0.879 on DrugTEMIST (Catalan Gold Standard)

F1 Score: 0.913 on CataCCC-drugs (Catalan Gold Standard)

Additional information

Authors

NLP4BIA team at the Barcelona Supercomputing Center ([email protected]).

Contact information

jan.rodriguez [at] bsc.es

Licensing information

Apache License, Version 2.0

Funding

This research was funded by the Ministerio de Ciencia e Innovación (MICINN) under project AI4ProfHealth (PID2020-119266RA-I00 MICIU/AEI/10.13039/501100011033) and BARITONE (TED2021-129974B-C22). This work is also supported by the European Union’s Horizon Europe Co-ordination & Support Action under Grant Agreement No 101080430 (AI4HF) as well as Grant Agreement No 101057849 (DataTool4Heartproject).

Citing information

Please cite the following works:

@inproceedings{multicardioner2024overview,
  title = {{Overview of MultiCardioNER task at BioASQ 2024 on Medical Speciality and Language Adaptation of Clinical NER Systems for Spanish, English and Italian}},
  author = {Salvador Lima-López and Eulàlia Farré-Maduell and Jan Rodríguez-Miret and Miguel Rodríguez-Ortega and Livia Lilli and Jacopo Lenkowicz and Giovanna Ceroni and Jonathan Kossoff and Anoop Shah and Anastasios Nentidis and Anastasia Krithara and Georgios Katsimpras and Georgios Paliouras and Martin Krallinger},
  booktitle = {CLEF Working Notes},
  year = {2024},
  editor = {Faggioli, Guglielmo and Ferro, Nicola and Galuščáková, Petra and García Seco de Herrera, Alba}
}

@article{rodriguez2024exploring,
  title={Exploring the Potential of Neural Machine Translation for Cross-Language Clinical Natural Language Processing (NLP) Resource Generation through Annotation Projection},
  author={Rodr{\'\i}guez-Miret, Jan and Farr{\'e}-Maduell, Eul{\`a}lia and Lima-L{\'o}pez, Salvador and Vigil, Laura and Briva-Iglesias, Vicent and Krallinger, Martin},
  journal={Information},
  volume={15},
  number={10},
  pages={585},
  year={2024},
  publisher={MDPI}
}

Disclaimer

The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.

When third parties deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of artificial intelligence.

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Los modelos publicados en este repositorio tienen una finalidad generalista y están a disposición de terceros. Estos modelos pueden tener sesgos y/u otro tipo de distorsiones indeseables.

Cuando terceros desplieguen o proporcionen sistemas y/o servicios a otras partes usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) o se conviertan en usuarios de los modelos, deben tener en cuenta que es su responsabilidad mitigar los riesgos derivados de su uso y, en todo caso, cumplir con la normativa aplicable, incluyendo la normativa en materia de uso de inteligencia artificial.