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Regression Model for Eating Functioning Levels (ICF d550)

Description

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

Functioning levels

Level Meaning
4 Can eat independently (in culturally acceptable ways), good intake, eats according to her/his needs.
3 Can eat independently but with adjustments, and/or somewhat reduced intake (>75% of her/his needs), and/or good intake can be achieved with proper advice.
2 Reduced intake, and/or stimulus / feeding modules / nutrition drinks are needed (but not tube feeding / TPN).
1 Intake is severely reduced (<50% of her/his needs), and/or tube feeding / TPN is needed.
0 Cannot eat, and/or fully dependent on tube feeding / TPN.

The predictions generated by the model might sometimes be outside of the scale (e.g. 4.2); 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(
    'roberta',
    'CLTL/icf-levels-etn',
    use_cuda=False,
)

example = 'Sondevoeding is geïndiceerd'
_, raw_outputs = model.predict([example])
predictions = np.squeeze(raw_outputs)

The prediction on the example is:

0.89

The raw outputs look like this:

[[0.8872931]]

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 0.59 0.50
mean squared error 0.65 0.47
root mean squared error 0.81 0.68

Authors and references

Authors

Jenia Kim, Piek Vossen

References

TBD

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