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

from transformer import pipeline

le_pipe = pipeline(model="UMCU/Echocardiogram_MitralRegurgitation_reduced")
document = "Lorem ipsum"
results = le_pipe(document)

Label Scheme

View label scheme

Component	Labels
`reduced`	`No label`, `Normal`, `Not Normal`

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

View pathologies

Annotation	Pathology
pe	Pericardial Effusion
wma	Wall Motion Abnormality
lv_dil	Left Ventricle Dilation
rv_dil	Right Ventricle Dilation
lv_syst_func	Left Ventricle Systolic Dysfunction
rv_syst_func	Right Ventricle Systolic Dysfunction
lv_dias_func	Diastolic Dysfunction
aortic_valve_native_stenosis	Aortic Stenosis
mitral_valve_native_regurgitation	Mitral valve regurgitation
tricuspid_valve_native_regurgitation	Tricuspid regurgitation
aortic_valve_native_regurgitation	Aortic Regurgitation

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.

Feature	Description
Name	`Echocardiogram_MitralRegurgitation_reduced`
Version	`1.0.0`
transformers	`>=4.40.0`
Default Pipeline	`pipeline`, `text-classification`
Components	`RobertaForSequenceClassification`
License	`cc-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

UMCU
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Echocardiogram_MitralRegurgitation_reduced