
# 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]
{"license": "apache-2.0", "tags": ["text-classification"]}
transformers pytorch bert text-classification license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #text-classification #license-apache-2.0 #endpoints_compatible #has_space #region-us 


# 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 = URL

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 = URL().cpu().numpy()[0].argsort()[::-1][:5]
    return [ config.id2label[ids] for ids in results]
# 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 = URL

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 = URL().cpu().numpy()[0].argsort()[::-1][:5]
    return [ config.id2label[ids] for ids in results]
TAGS
#transformers #pytorch #bert #text-classification #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 # 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 = URL

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 = URL().cpu().numpy()[0].argsort()[::-1][:5]
    return [ config.id2label[ids] for ids in results]
passage: TAGS
#transformers #pytorch #bert #text-classification #license-apache-2.0 #endpoints_compatible #has_space #region-us 
# 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 = URL

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 = URL().cpu().numpy()[0].argsort()[::-1][:5]
    return [ config.id2label[ids] for ids in results]