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]
Downloads last month
470
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Spaces using AkshatSurolia/ICD-10-Code-Prediction 7