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--- |
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language: nl |
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license: mit |
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pipeline_tag: text-classification |
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inference: false |
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--- |
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# Regression Model for Work and Employment Functioning Levels (ICF d840-d859) |
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## Description |
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A fine-tuned regression model that assigns a functioning level to Dutch sentences describing work and employment functions. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about work and employment functions in clinical text in Dutch, use the [icf-domains](https://huggingface.co/CLTL/icf-domains) classification model. |
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## Functioning levels |
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Level | Meaning |
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---|--- |
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4 | Can work/study fully (like when healthy). |
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3 | Can work/study almost fully. |
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2 | Can work/study only for about 50\%, or can only work at home and cannot go to school / office. |
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1 | Work/study is severely limited. |
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0 | Cannot work/study. |
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The predictions generated by the model might sometimes be outside of the scale (e.g. 4.2); this is normal in a regression model. |
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## Intended uses and limitations |
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- The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records). |
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- The model was fine-tuned with the [Simple Transformers](https://simpletransformers.ai/) library. This library is based on Transformers but the model cannot be used directly with Transformers `pipeline` and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled. |
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## How to use |
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To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library: |
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``` |
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from simpletransformers.classification import ClassificationModel |
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model = ClassificationModel( |
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'roberta', |
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'CLTL/icf-levels-ber', |
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use_cuda=False, |
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) |
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example = 'Fysiek zwaar werk is niet mogelijk, maar administrative taken zou zij wel aan moeten kunnen.' |
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_, raw_outputs = model.predict([example]) |
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predictions = np.squeeze(raw_outputs) |
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``` |
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The prediction on the example is: |
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``` |
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2.41 |
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``` |
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The raw outputs look like this: |
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``` |
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[[2.40793037]] |
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``` |
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## Training data |
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- The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released. |
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- The annotation guidelines used for the project can be found [here](https://github.com/cltl/a-proof-zonmw/tree/main/resources/annotation_guidelines). |
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## Training procedure |
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The default training parameters of Simple Transformers were used, including: |
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- Optimizer: AdamW |
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- Learning rate: 4e-5 |
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- Num train epochs: 1 |
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- Train batch size: 8 |
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## Evaluation results |
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The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals). |
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| | Sentence-level | Note-level |
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|---|---|--- |
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mean absolute error | 1.56 | 1.49 |
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mean squared error | 3.06 | 2.85 |
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root mean squared error | 1.75 | 1.69 |
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## Authors and references |
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### Authors |
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Jenia Kim, Piek Vossen |
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### References |
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TBD |
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