Create README.md

README.md

@@ -0,0 +1,113 @@
+---
+tags:
+- echocardiogram
+- arxiv:2408.06930
+- medical
+language:
+- nl
+license: gpl-3.0
+model-index:
+- name: Echocardiogram_MitralRegurgitation_reduced
+  results:
+  - task:
+      type: text-classification
+    dataset:
+      type: test
+      name: internal test set
+    metrics:
+    - name: Macro f1
+      type: f1
+      value: 0.949
+      verified: false
+    - name: Macro precision
+      type: precision
+      value: 0.958
+      verified: false
+    - name: Macro recall
+      type: recall
+      value: 0.939
+      verified: false
+pipeline_tag: text-classification
+metrics:
+- f1
+- precision
+- recall
+---
+
+# Description
+This model is a [MedRoBERTa.nl](https://huggingface.co/CLTL/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_MitralRegurgitation_reduced")
+document = "Lorem ipsum"
+results = le_pipe(document)
+```
+
+# Label Scheme
+
+<details>
+
+<summary>View label scheme</summary>
+
+| Component | Labels |
+| --- | --- |
+| **`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>
+
+| 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 |
+</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.
+
+| 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