Update README.md

README.md

@@ -1,8 +1,108 @@
 ---
+language: nl
 license: mit
-language:
-- nl
-base_model:
-- FacebookAI/roberta-base
 pipeline_tag: text-classification
----
+inference: false
+---
+
+# Regression Model for Respiration Functioning Levels
+
+## Description
+A fine-tuned regression model that assigns a functioning level to Dutch sentences describing respiration 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 respiration functions in clinical text in Dutch, use the [icf17-domains](https://huggingface.co/CLTL/icf17-domains) classification model. We use a single classifier for 17 different ICF categories to determine the level of functioning.
+
+The following ICF categories are covered:
+
+
+ICF code | Domain | name in repo
+---|---|---
+b1300 | Energy level | ENR
+b140 | Attention functions | ATT
+b152 | Emotional functions | STM
+b440 | Respiration functions | ADM
+b455 | Exercise tolerance functions | INS
+b530 | Weight maintenance functions | MBW
+d450 | Walking | FAC
+d550 | Eating | ETN
+d840-d859 | Work and employment | BER
+B280 | Sensations of pain | SOP
+B134 | Sleep functions | SLP
+D760 | Family relationships | FML
+B164 | Higher-level cognitive functions | HLC
+D465 | Moving around using equipment | MAE
+D410 | Changing basic body position | CBP
+B230 | Hearing functions | HRN
+D240 | Handling stress and other psychological demands | HSP
+
+## Functioning levels
+Level | Meaning
+---|---
+5 | No problem functioning
+4 | No problem functioning or almost complete functioning
+3 | Shortness of breath in exercise (saturation ≥90), and/or respiratory rate is slightly increased (EWS: 21-30).
+2 | Shortness of breath in rest (saturation ≥90), and/or respiratory rate is fairly increased (EWS: 31-35).
+1 | Needs oxygen at rest or during exercise (saturation <90), and/or respiratory rate >35.
+0 | Mechanical ventilation is needed.
+
+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-adm',
+    use_cuda=False,
+)
+
+example = 'Nu sinds 5-6 dagen progressieve benauwdheidsklachten (bij korte stukken lopen al kortademig), terwijl dit eerder niet zo was.'
+_, raw_outputs = model.predict([example])
+predictions = np.squeeze(raw_outputs)
+```
+The prediction on the example is:
+```
+2.26
+```
+The raw outputs look like this:
+```
+[[2.26074648]]
+```
+
+## 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 | 0.48 | 0.37
+mean squared error | 0.55 | 0.34
+root mean squared error | 0.74 | 0.58
+
+## Authors and references
+### Authors
+Jenia Kim, Piek Vossen
+
+### References
+When using this repository please cite:
+
+J. Kim, S. Verkijk, E. Geleijn, M. van der Leeden, C. Meskers, C. Meskers, S. van der Veen, P. Vossen, and G. Widdershoven, Modeling dutch medical texts for detecting functional categories and levels of covid-19 patients, 2022. In: Proceedings of the 13th Language Resources and Evaluation Conference, Marseille, June, 2022.
+
+Bibtext:
+
+@proceedings{kim-etal-lrec2022, author={Jenia Kim and Stella Verkijk and Edwin Geleijn and Marieke van der Leeden and Carel Meskers and Caroline Meskers and Sabina van der Veen and Piek Vossen and Guy Widdershoven}, title={Modeling Dutch Medical Texts for Detecting Functional Categories and Levels of COVID-19 Patients}, booktitle={Proceedings of the 13th Language Resources and Evaluation Conference, Marseille, June, 2022}, year={2022} }