README.md

---
language: nl
license: mit
pipeline_tag: text-classification
inference: false
---

# Regression Model for Exercise Tolerance Functioning Levels (ICF b455)

## Description
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing exercise tolerance 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 exercise tolerance functions in clinical text in Dutch, use the [icf-domains](https://huggingface.co/CLTL/icf-domains) classification model.

## Functioning levels
Level | Meaning
---|---
5 | MET>6. Can tolerate jogging, hard exercises, running, climbing stairs fast, sports.
4 | 4≤MET≤6. Can tolerate walking / cycling at a brisk pace, considerable effort (e.g. cycling from 16 km/h), heavy housework.
3 | 3≤MET<4. Can tolerate walking / cycling at a normal pace, gardening, exercises without equipment.
2 | 2≤MET<3. Can tolerate walking at a slow to moderate pace, grocery shopping, light housework.
1 | 1≤MET<2. Can tolerate sitting activities.
0 | 0≤MET<1. Can physically tolerate only recumbent activities.

The predictions generated by the model might sometimes be outside of the scale (e.g. 5.2); this is normal in a regression model.

## Intended uses and limitations
- 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).
- 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.

## How to use
To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library:
```
from simpletransformers.classification import ClassificationModel

model = ClassificationModel(
    'roberta',
    'CLTL/icf-levels-ins',
    use_cuda=False,
)

example = 'kan nog goed traplopen, maar flink ingeleverd aan conditie na Corona'
_, raw_outputs = model.predict([example])
predictions = np.squeeze(raw_outputs)
```
The prediction on the example is:
```
3.13
```
The raw outputs look like this:
```
[[3.1300993]]
```

## Training data
- 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.
- The annotation guidelines used for the project can be found [here](https://github.com/cltl/a-proof-zonmw/tree/main/resources/annotation_guidelines).

## Training procedure
The default training parameters of Simple Transformers were used, including:
- Optimizer: AdamW
- Learning rate: 4e-5
- Num train epochs: 1
- Train batch size: 8

## Evaluation results
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).

| | Sentence-level | Note-level
|---|---|---
mean absolute error | 0.69 | 0.61
mean squared error | 0.80 | 0.64
root mean squared error | 0.89 | 0.80

## Authors and references
### Authors
Jenia Kim, Piek Vossen

### References
TBD