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HCAHPS survey comments multilabel classification
This model is a fine-tuned version of Bio_ClinicalBERT on a dataset of HCAHPS survey comments.
It achieves the following results on the evaluation set:
precision recall f1-score support
medical 0.87 0.81 0.84 83
environmental 0.77 0.91 0.84 93
administration 0.58 0.32 0.41 22
communication 0.85 0.82 0.84 50
condition 0.42 0.52 0.46 29
treatment 0.90 0.78 0.83 68
food 0.92 0.94 0.93 36
clean 0.65 0.83 0.73 18
bathroom 0.64 0.64 0.64 14
discharge 0.83 0.83 0.83 24
wait 0.96 1.00 0.98 24
financial 0.44 1.00 0.62 4
extra_nice 0.20 0.13 0.16 23
rude 1.00 0.64 0.78 11
nurse 0.92 0.98 0.95 110
doctor 0.96 0.84 0.90 57
micro avg 0.81 0.81 0.81 666
macro avg 0.75 0.75 0.73 666
weighted avg 0.82 0.81 0.81 666
samples avg 0.64 0.64 0.62 666
Model description
The model classifies free-text comments into the following labels
- Medical
- Environmental
- Administration
- Communication
- Condition
- Treatment
- Food
- Clean
- Bathroom
- Discharge
- Wait
- Financial
- Extra_nice
- Rude
- Nurse
- Doctor
How to use
You can now use the models directly through the transformers library. Check out the model's page for instructions on how to use the models within the Transformers library.
Load the model via the transformers library:
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("joniponi/multilabel_inpatient_comments_16labels")
model = AutoModel.from_pretrained("joniponi/multilabel_inpatient_comments_16labels")
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