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---
license: mit
inference:
parameters:
aggregation_strategy: "average"
language:
- pt
pipeline_tag: fill-mask
tags:
- medialbertina-ptpt
- deberta
- portuguese
- european portuguese
- medical
- clinical
- healthcare
- NER
- Named Entity Recognition
- IE
- Information Extraction
widget:
- text: Durante a cirurgia ortopédica para corrigir a fratura no tornozelo, os sinais vitais do utente, incluindo a pressão arterial, com leitura de 120/87 mmHg, a frequência cardíaca, de 80 batimentos por minuto, e SpO2 a 98%, foram monitorizados. Após a cirurgia o utente apresentava dor intensa no local e inchaço no tornozelo, mas os resultados dos exames de radiografia revelaram uma recuperação satisfatória.
example_title: Example 1
- text: Durante o procedimento endoscópico, foram encontrados pólipos no cólon do paciente.
example_title: Example 2
- text: Foi recomendada aspirina de 500mg a cada 4 horas, durante 3 dias.
example_title: Example 3
- text: Após as sessões de fisioterapia o paciente apresenta recuperação de mobilidade.
example_title: Example 4
- text: O paciente está em Quimioterapia com uma dosagem específica de Cisplatina para o tratamento do cancro do pulmão.
example_title: Example 5
- text: Monitorização da Freq. cardíaca com 90 bpm. P Arterial de 120-80 mmHg
example_title: Example 6
- text: A ressonância magnética da utente revelou uma ruptura no menisco lateral do joelho.
example_title: Example 7
- text: A paciente foi diagnosticada com esclerose múltipla e iniciou terapia com imunomoduladores.
---
# MediAlbertina
The first publicly available medical language models trained with real European Portuguese data.
MediAlbertina is a family of encoders from the Bert family, DeBERTaV2-based, resulting from the continuation of the pre-training of [PORTULAN's Albertina](https://huggingface.co/PORTULAN) models with Electronic Medical Records shared by Portugal's largest public hospital.
Like its antecessors, MediAlbertina models are distributed under the [MIT license](https://huggingface.co/portugueseNLP/medialbertina_pt-pt_900m/blob/main/LICENSE).
# Model Description
MediAlbertina PT-PT 900M NER was created through domain adaptation of [MediAlbertina PT-PT 900M](https://huggingface.co/portugueseNLP/medialbertina_pt-pt_900m) on real European Portuguese EMRs that have been hand-annotated for the following entities:
- Diagnostico
- Sintoma
- Medicamento
- Dosagem
- ProcedimentoMedico
- SinalVital
- Resultado
- Progresso
-
MediAlbertina PT-PT 900M NER achieved superior results to the same adaptation made on a non-medical Portuguese language model, demonstrating the effectiveness of this domain adaptation, and its potential for medical AI in Portugal.
| Model | NER single-model | NER multi-models | Assertion Status |
|-------------------------|:----------------:|:----------------:|:----------------:|
| | F1-score | F1-score | F1-score |
|albertina-900m-portuguese-ptpt-encoder | 0.813 | 0.811 | 0.687 |
| **medialbertina_pt-pt_900m** | **0.832** | **0.848** | **0.755** |
## Data
MediAlbertina PT-PT 900M NER was fine-tuned on more than 10k hand-annotated entities from more than a thousand fully anonymized medical sentences from Portugal's largest public hospital. This data was acquired under the framework of the [FCT project DSAIPA/AI/0122/2020 AIMHealth-Mobile Applications Based on Artificial Intelligence](https://ciencia.iscte-iul.pt/projects/aplicacoes-moveis-baseadas-em-inteligencia-artificial-para-resposta-de-saude-publica/1567).
## How to use
```Python
from transformers import pipeline
ner_pipeline = pipeline('ner', model='portugueseNLP/medialbertina_pt-pt_900m_NER', aggregation_strategy='average')
sentence = 'Durante o procedimento endoscópico, foram encontrados pólipos no cólon do paciente.'
entities = ner_pipeline(sentence)
for entity in entities:
print(f"{entity['entity_group']} - {sentence[entity['start']:entity['end']]}")
```
## Citation
MediAlbertina is developed by a joint team from [ISCTE-IUL](https://www.iscte-iul.pt/), Portugal, and [Select Data](https://selectdata.com/), CA USA. For a fully detailed description, check the respective publication:
```latex
In publishing process. Reference will be added soon.
```
Please use the above cannonical reference when using or citing this model.
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