
# Regression Model for Attention Functioning Levels (ICF b140)

## Description
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.

## Functioning levels
Level | Meaning
---|---
4 | No problem with concentrating / directing / holding / dividing attention.
3 | Slight problem with concentrating / directing / holding / dividing attention for a longer period of time or for complex tasks.
2 | Can concentrate / direct / hold / divide attention only for a short time.
1 | Can barely concentrate / direct / hold / divide attention.
0 | Unable to concentrate / direct / hold / divide attention.

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](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-att',
    use_cuda=False,
)

example = 'Snel afgeleid, moeite aandacht te behouden.'
_, raw_outputs = model.predict([example])
predictions = np.squeeze(raw_outputs)
```
The prediction on the example is:
```
2.89
```
The raw outputs look like this:
```
[[2.89226103]]
```

## 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 | 0.99 | 1.03
mean squared error | 1.35 | 1.47
root mean squared error | 1.16 | 1.21

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

### References
TBD

{"language": "nl", "license": "mit", "pipeline_tag": "text-classification", "inference": false}
transformers pytorch roberta text-classification nl license:mit autotrain_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #nl #license-mit #autotrain_compatible #region-us 

Regression Model for Attention Functioning Levels (ICF b140)
============================================================


Description
-----------


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 classification model.


Functioning levels
------------------



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:


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: mean absolute error, Note-level: 0.99
Sentence-level: mean squared error, Note-level: 1.35
Sentence-level: root mean squared error, Note-level: 1.16


Authors and references
----------------------


### Authors


Jenia Kim, Piek Vossen


### References


TBD



### Authors


Jenia Kim, Piek Vossen ### References


TBD
TAGS
#transformers #pytorch #roberta #text-classification #nl #license-mit #autotrain_compatible #region-us 
 ### Authors


Jenia Kim, Piek Vossen ### References


TBD
TAGS
#transformers #pytorch #roberta #text-classification #nl #license-mit #autotrain_compatible #region-us 
### Authors


Jenia Kim, Piek Vossen### References


TBD