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UMCU/Echocardiogram_Diastolic_dysfunction_reduced

sourceHugging Facegpl-3.0updated 2y agoView on Hugging Face
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Model Card

Description

This model is a MedRoBERTa.nl model finetuned on Dutch echocardiogram reports sourced from Electronic Health Records. The publication associated with the span classification task can be found at https://arxiv.org/abs/2408.06930. The config file for training the model can be found at https://github.com/umcu/echolabeler.

Minimum working example

python
from transformer import pipeline
python
le_pipe = pipeline(model="UMCU/Echocardiogram_Diastolic_dysfunction_reduced")
document = "Lorem ipsum"
results = le_pipe(document)

Label Scheme

<details>

<summary>View label scheme</summary>

ComponentLabels
`reduced`No label, Normal, Not Normal

</details>

Here, for the reduced labels Present means that for any one or multiple of the pathologies we have a positive result.

Here, for the pathologies we have

<details>

<summary>View pathologies</summary>

AnnotationPathology
pePericardial Effusion
wmaWall Motion Abnormality
lv_dilLeft Ventricle Dilation
rv_dilRight Ventricle Dilation
lvsystfuncLeft Ventricle Systolic Dysfunction
rvsystfuncRight Ventricle Systolic Dysfunction
lvdiasfuncDiastolic Dysfunction
aorticvalvenative_stenosisAortic Stenosis
mitralvalvenative_regurgitationMitral valve regurgitation
tricuspidvalvenative_regurgitationTricuspid regurgitation
aorticvalvenative_regurgitationAortic Regurgitation

</details>

Note: lv_dias_func should have been dias_func..

Intended use

The model is developed for document classification of Dutch clinical echocardiogram reports. Since it is a domain-specific model trained on medical data, it is only meant to be used on medical NLP tasks for Dutch echocardiogram reports.

Data

The model was trained on approximately 4,000 manually annotated echocardiogram reports from the University Medical Centre Utrecht. The training data was anonymized before starting the training procedure.

FeatureDescription
NameEchocardiogram_SpanCategorizer_aortic_stenosis
Version1.0.0
transformers>=4.40.0
Default Pipelinepipeline, text-classification
ComponentsRobertaForSequenceClassification
Licensecc-by-sa-4.0
Author[Bram van Es]()

Contact

If you are having problems with this model please add an issue on our git: https://github.com/umcu/echolabeler/issues

Usage

If you use the model in your work please use the following referral; https://doi.org/10.48550/arXiv.2408.06930

References

Paper: Bauke Arends, Melle Vessies, Dirk van Osch, Arco Teske, Pim van der Harst, René van Es, Bram van Es (2024): Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification, Arxiv https://arxiv.org/abs/2408.06930