medialbertina_pt-pt_900m_NER / README.md

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	---
	license: mit

	inference:
	parameters:
	aggregation_strategy: "average"

	language:
	- pt
	pipeline_tag: token-classification
	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 e a frequência cardíaca, de 80 batimentos por minuto, foram monitorizados. Após a cirurgia o utente apresentava dor intensa no local e inchaço no tornozelo, mas os resultados da radiografia revelaram uma recuperação satisfatória. Foi prescrito ibuprofeno 600mg de 8 em 8 horas durante 3 dias.
	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 rotura no menisco lateral do joelho.
	example_title: Example 7
	- text: A paciente foi diagnosticada com esclerose múltipla e iniciou terapia com imunomoduladores.
	example_title: Example 8
	---

	# MediAlbertina
	The first publicly available medical language model 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_NER/blob/main/LICENSE).



	# Model Description

	MediAlbertina PT-PT 900M NER was created through fine-tuning 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 (D): All types of diseases and conditions following the ICD-10-CM guidelines.
	- Sintoma (S): Any complaints or evidence from healthcare professionals indicating that a patient is experiencing a medical condition.
	- Medicamento (M): Something that is administrated to the patient (through any route), including drugs, specific food/drink, vitamins, or blood for transfusion.
	- Dosagem (D): Dosage and frequency of medication administration.
	- ProcedimentoMedico (PM): Anything healthcare professionals do related to patients, including exams, moving patients, administering something, or even surgeries.
	- SinalVital (SV): Quantifiable indicators in a patient that can be measured, always associated with a specific result. Examples include cholesterol levels, diuresis, weight, or glycaemia.
	- Resultado (R): Results can be associated with Medical Procedures and Vital Signs. It can be a numerical value if something was measured (e.g., the value associated with blood pressure) or a descriptor to indicate the result (e.g., positive/negative, functional).
	- Progresso (P): Describes the progress of patient’s condition. Typically, it includes verbs like improving, evolving, or regressing and mentions to patient’s stability.

	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 \| B-D \| I-D \| B-S \| I-S \| B-PM \| I-PM \| B-SV \| I-SV \| B-R \| I-R \| B-M \| I-M \| B-DO \| I-DO \| B-P \| I-P \|
	\|-------------------------\|:----:\|:----:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|
	\| \| F1 \| F1 \| F1 \| F1 \| F1 \| F1 \| F1 \| F1 \| F1 \| F1 \| F1 \| F1 \| F1 \| F1 \| F1 \| F1 \|
	\| albertina-900m-portuguese-ptpt-encoder\|0.721\|0.786\|0.734\|0.775\|0.737\|0.805\|0.859\|0.811\|0.803\|0.816\|0.913\|0.871\|0.853\|0.895\|0.769\|0.785\|
	\| medialbertina_pt-pt_900m \| 0.799\| 0.832\| 0.754\| 0.782\| 0.786\| 0.813\| 0.916\| 0.788\| 0.821\| 0.83\| 0.926\| 0.895\|0.85\|0.885\| 0.779\| 0.807\|





	## Data

	MediAlbertina PT-PT 900M NER was fine-tuned on about 10k hand-annotated medical entities from about 4k 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
	@article{MediAlbertina PT-PT,
	title={MediAlbertina: An European Portuguese medical language model},
	author={Miguel Nunes and João Boné and João Ferreira
	and Pedro Chaves and Luís Elvas},
	year={2024},
	journal={CBM},
	volume={182}
	url={https://doi.org/10.1016/j.compbiomed.2024.109233}
	}
	```
	Please use the above cannonical reference when using or citing this [model](https://www.sciencedirect.com/science/article/pii/S0010482524013180?via%3Dihub).

	## Acknowledgements

	This work was financially supported by Project Blockchain.PT – Decentralize Portugal with Blockchain Agenda, (Project no 51), WP2, Call no 02/C05-i01.01/2022, funded by the Portuguese Recovery and Resillience Program (PRR), The Portuguese Republic and The European Union (EU) under the framework of Next Generation EU Program.