portugueseNLP/medialbertina_pt-pt_900m_NER

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 models with Electronic Medical Records shared by Portugal's largest public hospital.

Like its antecessors, MediAlbertina models are distributed under the MIT license.

Model Description

MediAlbertina PT-PT 900M NER was created through fine-tuning of 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.

How to use

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, Portugal, and Select Data, CA USA. For a fully detailed description, check the respective publication:

In publishing process. Reference will be added soon.

Please use the above cannonical reference when using or citing this model.