metadata

language:
  - ca
tags:
  - biomedical
  - clinical
  - EHR
  - catalan
  - professions
  - employment status
license: apache-2.0
metrics:
  - precision
  - recall
  - f1
base_model:
  - PlanTL-GOB-ES/bsc-bio-ehr-es
model-index:
  - name: BSC-NLP4BIA/prof-ner-cat-v1
    results:
      - task:
          type: token-classification
        dataset:
          name: MEDDOPROF-cat
          type: MEDDOPROF-cat
        metrics:
          - name: precision (micro)
            type: precision
            value: 0.743
          - name: recall (micro)
            type: recall
            value: 0.717
          - name: f1 (micro)
            type: f1
            value: 0.729
      - task:
          type: token-classification
        dataset:
          name: CataCCC-professions
          type: CataCCC-professions
        metrics:
          - name: precision (micro)
            type: precision
            value: 0.838
          - name: recall (micro)
            type: recall
            value: 0.794
          - name: f1 (micro)
            type: f1
            value: 0.815
widget:
  - text: >-
      El diagnóstico definitivo de nuestro paciente fue de un Adenocarcinoma de
      pulmón cT2a cN3 cM1a Estadio IV (por una única lesión pulmonar
      contralateral) PD-L1 90%, EGFR negativo, ALK negativo y ROS-1 negativo.
  - text: >-
      Durante el ingreso se realiza una TC, observándose un nódulo pulmonar en
      el LII y una masa renal derecha indeterminada. Se realiza punción biopsia
      del nódulo pulmonar, con hallazgos altamente sospechosos de carcinoma.
  - text: >-
      Trombosis paraneoplásica con sospecha de hepatocarcinoma por imagen, sobre
      hígado cirrótico, en paciente con índice Child-Pugh B.

PROF-NER-CAT

Click to expand

Model description
How to use
Limitations and bias
Training
Evaluation
Additional information

Model description

A fine-tuned version of the bsc-bio-ehr-es model on the MEDDOPROF corpus (Catalan Gold Standard Corpus) for professions/jobs (PROFESION) and employment status (SITUACION_LABORAL).

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

Micro F1 Score: 0.729 on MEDDOPROF-cat (Catalan Gold Standard)

PROFESION F1 Score: 0.775

SITUACION_LABORAL F1 Score: 0.639

Micro F1 Score: 0.815 on CataCCC-professions (Catalan Gold Standard)

PROFESION F1 Score: 0.863

SITUACION_LABORAL F1 Score: 0.651

Additional information

Authors

NLP4BIA team at the Barcelona Supercomputing Center (nlp4bia@bsc.es).

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:

@article{meddoprof,
title={{NLP applied to occupational health: MEDDOPROF shared task at IberLEF 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts}},
author={Lima-López, Salvador and Farré-Maduell, Eulàlia and Miranda-Escalada, Antonio and Brivá-Iglesias, Vicent and Krallinger, Martin},
journal = {Procesamiento del Lenguaje Natural}, 
volume = {67}, 
year={2021}, 
issn = {1989-7553},
url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6393}, 
pages = {243--256}}

@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.

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.

BSC-NLP4BIA
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prof-ner-cat-v1