readme: ---
  license: apache-2.0
  tags:
  - text-classification
  datasets:
  - Mimic III
  ---
  
  # 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]