metadata

language:
  - es
tags:
  - NER
  - named entity recognition
  - biomedical
  - clinical
  - EHR
  - spanish
  - location
  - birth place
  - residence
  - movement
  - medical care
license: apache-2.0
metrics:
  - precision
  - recall
  - f1
base_model:
  - PlanTL-GOB-ES/bsc-bio-ehr-es
model-index:
  - name: BSC-NLP4BIA/location-sub-tagger
    results:
      - task:
          type: token-classification
        dataset:
          name: MEDDOPLACE (subtrack 3)
          type: MEDDOPLACE
        metrics:
          - name: precision (micro)
            type: precision
            value: 0.742
          - name: recall (micro)
            type: recall
            value: 0.733
          - name: f1 (micro)
            type: f1
            value: 0.738
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.
pipeline_tag: token-classification

LocationSubTagger

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 MEDDOPLACE corpus (subtrack 3) for subcategorization of location entities. The labels detected are: brith place (LUGAR_NATAL), residence (RESIDENCIA), movement (MOVIMIENTO), medical care (ATENCION), and other (OTHER).

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.

Evaluation

Strict (same class, exact boundary) and overlapping (same class, some overlap) metrics for the MEDDOPLACE Subtrack 1 test set.

	precision	recall	f_score	ov_precision	ov_recall	ov_f_score
ATENCION	0.728	0.738	0.733	0.797	0.809	0.803
LUGAR_NATAL	0.672	0.743	0.706	0.728	0.805	0.765
MOVIMIENTO	0.775	0.713	0.743	0.804	0.740	0.771
RESIDENCIA	0.761	0.816	0.787	0.791	0.848	0.819
OTHER	0.751	0.694	0.721	0.808	0.747	0.776
Micro avg.	0.742	0.733	0.738	0.795	0.785	0.790

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

TBD

Citing information

Please cite the following works:

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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location-sub-tagger