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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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+
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+ # Regression Model for Attention Functioning Levels (ICF b140)
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+
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+ ## Description
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+ A fine-tuned regression model that assigns a functioning level to Dutch sentences describing attention 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 attention functions in clinical text in Dutch, use the [icf-domains](https://huggingface.co/CLTL/icf-domains) classification model.
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+
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+ ## Functioning levels
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+ Level | Meaning
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+ ---|---
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+ 4 | No problem with concentrating / directing / holding / dividing attention.
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+ 3 | Slight problem with concentrating / directing / holding / dividing attention for a longer period of time or for complex tasks.
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+ 2 | Can concentrate / direct / hold / divide attention only for a short time.
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+ 1 | Can barely concentrate / direct / hold / divide attention.
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+ 0 | Unable to concentrate / direct / hold / divide attention.
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+
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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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+
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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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+
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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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+
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+ model = ClassificationModel(
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+ 'roberta',
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+ 'CLTL/icf-levels-att',
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+ use_cuda=False,
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+ )
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+
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+ example = 'Snel afgeleid, moeite aandacht te behouden.'
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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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+ 2.89
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+ ```
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+ The raw outputs look like this:
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+ ```
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+ [[2.89226103]]
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+ ```
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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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+
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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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+
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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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+
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+ | | Sentence-level | Note-level
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+ |---|---|---
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+ mean absolute error | 0.99 | 1.03
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+ mean squared error | 1.35 | 1.47
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+ root mean squared error | 1.16 | 1.21
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+
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+ ## Authors and references
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+ ### Authors
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+ Jenia Kim, Piek Vossen
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+
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+ ### References
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+ TBD