AkshatSurolia/ICD-10-Code-Prediction

Clinical BERT for ICD-10 Prediction

The Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base (casedL-12H-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.frompretrained("AkshatSurolia/ICD-10-Code-Prediction") model = BertForSequenceClassification.frompretrained("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" encodedinput = tokenizer(text, returntensors='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]