ICD 10 Code Prediction

🚀 Clinical BERT for ICD-10 Prediction

This project provides four unique clinicalBERT models for ICD - 10 prediction, offering high - value solutions for text classification in the clinical field.

🚀 Quick Start

The Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models. These models are initialized with either BERT - Base (cased_L - 12_H - 768_A - 12) or BioBERT (BioBERT - Base v1.0 + PubMed 200K + PMC 270K) and trained on either all MIMIC notes or only discharge summaries.

💻 Usage Examples

Basic Usage

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

Advanced Usage

Run the model with clinical diagonosis text and return the Top - 5 predicted ICD - 10 codes:

# 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]

📄 License

This project is licensed under the Apache - 2.0 license.