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Regression Model for Walking Functioning Levels (ICF d550)


A fine-tuned regression model that assigns a functioning level to Dutch sentences describing walking 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 walking functions in clinical text in Dutch, use the icf-domains classification model.

Functioning levels

Level Meaning
5 Patient can walk independently anywhere: level surface, uneven surface, slopes, stairs.
4 Patient can walk independently on level surface but requires help on stairs, inclines, uneven surface; or, patient can walk independently, but the walking is not fully normal.
3 Patient requires verbal supervision for walking, without physical contact.
2 Patient needs continuous or intermittent support of one person to help with balance and coordination.
1 Patient needs firm continuous support from one person who helps carrying weight and with balance.
0 Patient cannot walk or needs help from two or more people; or, patient walks on a treadmill.

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 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 library:

from simpletransformers.classification import ClassificationModel

model = ClassificationModel(

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:


The raw outputs look like this:


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.

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 0.70 0.66
mean squared error 0.91 0.93
root mean squared error 0.95 0.96

Authors and references


Jenia Kim, Piek Vossen



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