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Biomedical-clinical language model for Spanish

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Model description

Biomedical pretrained language model for Spanish. This model is a RoBERTa-based model trained on a biomedical-clinical corpus in Spanish collected from several sources.

Intended uses and limitations

The model is ready-to-use only for masked language modelling to perform the Fill Mask task (try the inference API or read the next section). However, it is intended to be fine-tuned on downstream tasks such as Named Entity Recognition or Text Classification.

How to use

from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("BSC-TeMU/roberta-base-biomedical-es")
model = AutoModelForMaskedLM.from_pretrained("BSC-TeMU/roberta-base-biomedical-es")
from transformers import pipeline
unmasker = pipeline('fill-mask', model="BSC-TeMU/roberta-base-biomedical-es")
unmasker("El único antecedente personal a reseñar era la <mask> arterial.")
# Output
[
  {
    "sequence": " El único antecedente personal a reseñar era la hipertensión arterial.",
    "score": 0.9855039715766907,
    "token": 3529,
    "token_str": " hipertensión"
  },
  {
    "sequence": " El único antecedente personal a reseñar era la diabetes arterial.",
    "score": 0.0039140828885138035,
    "token": 1945,
    "token_str": " diabetes"
  },
  {
    "sequence": " El único antecedente personal a reseñar era la hipotensión arterial.",
    "score": 0.002484665485098958,
    "token": 11483,
    "token_str": " hipotensión"
  },
  {
    "sequence": " El único antecedente personal a reseñar era la Hipertensión arterial.",
    "score": 0.0023484621196985245,
    "token": 12238,
    "token_str": " Hipertensión"
  },
  {
    "sequence": " El único antecedente personal a reseñar era la presión arterial.",
    "score": 0.0008009297889657319,
    "token": 2267,
    "token_str": " presión"
  }
]

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 training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original RoBERTA model with a vocabulary size of 52,000 tokens. The pretraining consists of a masked language model training at the subword level following the approach employed for the RoBERTa base model with the same hyperparameters as in the original work. The training lasted a total of 48 hours with 16 NVIDIA V100 GPUs of 16GB DDRAM, using Adam optimizer with a peak learning rate of 0.0005 and an effective batch size of 2,048 sentences.

The training corpus is composed of several biomedical corpora in Spanish, collected from publicly available corpora and crawlers, and a real-world clinical corpus collected from more than 278K clinical documents and notes. To obtain a high-quality training corpus while retaining the idiosyncrasies of the clinical language, a cleaning pipeline has been applied only to the biomedical corpora, keeping the clinical corpus uncleaned. Essentially, the cleaning operations used are:

  • data parsing in different formats
  • sentence splitting
  • language detection
  • filtering of ill-formed sentences
  • deduplication of repetitive contents
  • keep the original document boundaries

Then, the biomedical corpora are concatenated and further global deduplication among the biomedical corpora have been applied. Eventually, the clinical corpus is concatenated to the cleaned biomedical corpus resulting in a medium-size biomedical-clinical corpus for Spanish composed of more than 1B tokens. The table below shows some basic statistics of the individual cleaned corpora:

Name No. tokens Description
Medical crawler 745,705,946 Crawler of more than 3,000 URLs belonging to Spanish biomedical and health domains.
Clinical cases misc. 102,855,267 A miscellany of medical content, essentially clinical cases. Note that a clinical case report is a scientific publication where medical practitioners share patient cases and it is different from a clinical note or document.
Clinical notes/documents 91,250,080 Collection of more than 278K clinical documents, including discharge reports, clinical course notes and X-ray reports, for a total of 91M tokens.
Scielo 60,007,289 Publications written in Spanish crawled from the Spanish SciELO server in 2017.
BARR2_background 24,516,442 Biomedical Abbreviation Recognition and Resolution (BARR2) containing Spanish clinical case study sections from a variety of clinical disciplines.
Wikipedia_life_sciences 13,890,501 Wikipedia articles crawled 04/01/2021 with the Wikipedia API python library starting from the "Ciencias_de_la_vida" category up to a maximum of 5 subcategories. Multiple links to the same articles are then discarded to avoid repeating content.
Patents 13,463,387 Google Patent in Medical Domain for Spain (Spanish). The accepted codes (Medical Domain) for Json files of patents are: "A61B", "A61C","A61F", "A61H", "A61K", "A61L","A61M", "A61B", "A61P".
EMEA 5,377,448 Spanish-side documents extracted from parallel corpora made out of PDF documents from the European Medicines Agency.
mespen_Medline 4,166,077 Spanish-side articles extracted from a collection of Spanish-English parallel corpus consisting of biomedical scientific literature. The collection of parallel resources are aggregated from the MedlinePlus source.
PubMed 1,858,966 Open-access articles from the PubMed repository crawled in 2017.

Evaluation

The model has been evaluated on the Named Entity Recognition (NER) using the following datasets:

  • PharmaCoNER: is a track on chemical and drug mention recognition from Spanish medical texts (for more info see: https://temu.bsc.es/pharmaconer/).

  • CANTEMIST: is a shared task specifically focusing on named entity recognition of tumor morphology, in Spanish (for more info see: https://zenodo.org/record/3978041#.YTt5qH2xXbQ).

  • ICTUSnet: consists of 1,006 hospital discharge reports of patients admitted for stroke from 18 different Spanish hospitals. It contains more than 79,000 annotations for 51 different kinds of variables.

The evaluation results are compared against the mBERT and BETO models:

F1 - Precision - Recall roberta-base-biomedical-clinical-es mBERT BETO
PharmaCoNER 90.04 - 88.92 - 91.18 87.46 - 86.50 - 88.46 88.18 - 87.12 - 89.28
CANTEMIST 83.34 - 81.48 - 85.30 82.61 - 81.12 - 84.15 82.42 - 80.91 - 84.00
ICTUSnet 88.08 - 84.92 - 91.50 86.75 - 83.53 - 90.23 85.95 - 83.10 - 89.02

Additional information

Author

Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)

Contact information

For further information, send an email to plantl-gob-es@bsc.es

Copyright

Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)

Licensing information

Apache License, Version 2.0

Funding

This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.

Citation information

If you use our models, please cite our latest preprint:


@misc{carrino2021biomedical,
      title={Biomedical and Clinical Language Models for Spanish: On the Benefits of Domain-Specific Pretraining in a Mid-Resource Scenario}, 
      author={Casimiro Pio Carrino and Jordi Armengol-Estapé and Asier Gutiérrez-Fandiño and Joan Llop-Palao and Marc Pàmies and Aitor Gonzalez-Agirre and Marta Villegas},
      year={2021},
      eprint={2109.03570},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

If you use our Medical Crawler corpus, please cite the preprint:


@misc{carrino2021spanish,
      title={Spanish Biomedical Crawled Corpus: A Large, Diverse Dataset for Spanish Biomedical Language Models}, 
      author={Casimiro Pio Carrino and Jordi Armengol-Estapé and Ona de Gibert Bonet and Asier Gutiérrez-Fandiño and Aitor Gonzalez-Agirre and Martin Krallinger and Marta Villegas},
      year={2021},
      eprint={2109.07765},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Disclaimer

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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.

In no event shall the owner of the models (SEDIA – State Secretariat for Digitalization and Artificial Intelligence) nor the creator (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.

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.

En ningún caso el propietario de los modelos (SEDIA – Secretaría de Estado de Digitalización e Inteligencia Artificial) ni el creador (BSC – Barcelona Supercomputing Center) serán responsables de los resultados derivados del uso que hagan terceros de estos modelos.

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