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--- |
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license: mit |
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inference: |
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parameters: |
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aggregation_strategy: "average" |
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language: |
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- pt |
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pipeline_tag: fill-mask |
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tags: |
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- medialbertina-ptpt |
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- deberta |
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- portuguese |
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- european portuguese |
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- medical |
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- clinical |
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- healthcare |
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- NER |
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- Named Entity Recognition |
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- IE |
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- Information Extraction |
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widget: |
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- 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. |
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example_title: Example 1 |
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- text: Durante o procedimento endoscópico, foram encontrados pólipos no cólon do paciente. |
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example_title: Example 2 |
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- text: Foi recomendada aspirina de 500mg a cada 4 horas, durante 3 dias. |
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example_title: Example 3 |
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- text: Após as sessões de fisioterapia o paciente apresenta recuperação de mobilidade. |
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example_title: Example 4 |
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- text: O paciente está em Quimioterapia com uma dosagem específica de Cisplatina para o tratamento do cancro do pulmão. |
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example_title: Example 5 |
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- text: Monitorização da Freq. cardíaca com 90 bpm. P Arterial de 120-80 mmHg |
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example_title: Example 6 |
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- text: A ressonância magnética da utente revelou uma ruptura no menisco lateral do joelho. |
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example_title: Example 7 |
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- text: A paciente foi diagnosticada com esclerose múltipla e iniciou terapia com imunomoduladores. |
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--- |
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# MediAlbertina |
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The first publicly available medical language models trained with real European Portuguese data. |
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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. |
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Like its antecessors, MediAlbertina models are distributed under the [MIT license](https://huggingface.co/portugueseNLP/medialbertina_pt-pt_900m/blob/main/LICENSE). |
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# Model Description |
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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: |
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- Diagnostico |
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- Sintoma |
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- Medicamento |
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- Dosagem |
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- ProcedimentoMedico |
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- SinalVital |
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- Resultado |
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- Progresso |
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- |
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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. |
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| Model | NER single-model | NER multi-models | Assertion Status | |
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|-------------------------|:----------------:|:----------------:|:----------------:| |
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| | F1-score | F1-score | F1-score | |
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|albertina-900m-portuguese-ptpt-encoder | 0.813 | 0.811 | 0.687 | |
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| **medialbertina_pt-pt_900m** | **0.832** | **0.848** | **0.755** | |
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## Data |
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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). |
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## How to use |
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```Python |
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from transformers import pipeline |
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ner_pipeline = pipeline('ner', model='portugueseNLP/medialbertina_pt-pt_900m_NER', aggregation_strategy='average') |
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sentence = 'Durante o procedimento endoscópico, foram encontrados pólipos no cólon do paciente.' |
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entities = ner_pipeline(sentence) |
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for entity in entities: |
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print(f"{entity['entity_group']} - {sentence[entity['start']:entity['end']]}") |
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``` |
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## Citation |
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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: |
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```latex |
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In publishing process. Reference will be added soon. |
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``` |
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Please use the above cannonical reference when using or citing this model. |
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