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--- |
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tags: |
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- spacy |
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- arxiv:2408.06930 |
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- medical |
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language: |
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- nl |
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license: cc-by-sa-4.0 |
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model-index: |
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- name: Echocardiogram_SpanCategorizer_wall_motion_abnormalities |
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results: |
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- task: |
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type: token-classification |
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dataset: |
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type: test |
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name: "internal test set" |
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metrics: |
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- name: "Weighted f1" |
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type: f1 |
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value: 0.596 |
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verified: false |
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- name: "Weighted precision" |
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type: precision |
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value: 0.587 |
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verified: false |
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- name: "Weighted recall" |
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type: recall |
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value: 0.606 |
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verified: false |
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pipeline_tag: token-classification |
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metrics: |
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- f1 |
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- precision |
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- recall |
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--- |
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# Description |
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This model is a spaCy SpanCategorizer model trained from scratch 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. |
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# Minimum working example |
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```python |
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!pip install https://huggingface.co/baukearends/Echocardiogram-SpanCategorizer-wall-motion-abnormalities/resolve/main/nl_Echocardiogram_SpanCategorizer_wall_motion_abnormalities-any-py3-none-any.whl |
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``` |
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```python |
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import spacy |
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nlp = spacy.load("nl_Echocardiogram_SpanCategorizer_wall_motion_abnormalities") |
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``` |
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```python |
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prediction = nlp("Op dit echo geen duidelijke WMA te zien, goede systolische L.V. functie, wel L.V.H., diastolische dysfunctie graad 1A tot 2. Geringe aortastenose en - matige -insufficientie. Geringe M.I.") |
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for span, score in zip(prediction.spans['sc'], prediction.spans['sc'].attrs['scores']): |
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print(f"Span: {span}, label: {span.label_}, score: {score[0]:.3f}") |
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``` |
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# Label Scheme |
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<details> |
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<summary>View label scheme (2 labels for 1 components)</summary> |
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| Component | Labels | |
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| --- | --- | |
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| **`spancat`** | `wma_present`, `wma_not_present` | |
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</details> |
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# Intended use |
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The model is developed for span classification on Dutch clinical text. Since it is a domain-specific model trained on medical data, it is meant to be used on medical NLP tasks for Dutch. |
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# Data |
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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. |
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| Feature | Description | |
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| --- | --- | |
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| **Name** | `Echocardiogram_SpanCategorizer_wall_motion_abnormalities` | |
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| **Version** | `1.0.0` | |
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| **spaCy** | `>=3.7.4,<3.8.0` | |
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| **Default Pipeline** | `tok2vec`, `spancat` | |
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| **Components** | `tok2vec`, `spancat` | |
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| **License** | `cc-by-sa-4.0` | |
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| **Author** | [Bauke Arends]() | |
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# Contact |
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If you are having problems with this model please add an issue on our git: https://github.com/umcu/echolabeler/issues |
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# Usage |
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If you use the model in your work please use the following referral; https://doi.org/10.48550/arXiv.2408.06930 |
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# References |
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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 |
