|
--- |
|
license: apache-2.0 |
|
tags: |
|
- text-classification |
|
--- |
|
|
|
# Clinical BERT for ICD-10 Prediction |
|
|
|
The Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base (cased_L-12_H-768_A-12) or BioBERT (BioBERT-Base v1.0 + PubMed 200K + PMC 270K) & trained on either all MIMIC notes or only discharge summaries. |
|
|
|
--- |
|
|
|
## How to use the model |
|
|
|
Load the model via the transformers library: |
|
|
|
from transformers import AutoTokenizer, BertForSequenceClassification |
|
tokenizer = AutoTokenizer.from_pretrained("AkshatSurolia/ICD-10-Code-Prediction") |
|
model = BertForSequenceClassification.from_pretrained("AkshatSurolia/ICD-10-Code-Prediction") |
|
config = model.config |
|
|
|
Run the model with clinical diagonosis text: |
|
|
|
text = "subarachnoid hemorrhage scalp laceration service: surgery major surgical or invasive" |
|
encoded_input = tokenizer(text, return_tensors='pt') |
|
output = model(**encoded_input) |
|
|
|
Return the Top-5 predicted ICD-10 codes: |
|
|
|
results = output.logits.detach().cpu().numpy()[0].argsort()[::-1][:5] |
|
return [ config.id2label[ids] for ids in results] |
