ashishkgpian/biobert_icd9_classifier_ehr

Model Card for BioBERT Fine-tuned on MIMIC-3 for ICD-9 Code Classification

Model Details

Model Description

This is a BioBERT model fine-tuned on the MIMIC-3 (Medical Information Mart for Intensive Care) corpus specifically for ICD-9 code classification. The model is designed to predict medical diagnostic codes based on Electronic Health Record (EHR) and symptom text inputs.

• Developed by: [Researcher/Institution Name - to be added]
• Model type: Transformer-based medical language model (BioBERT)
• Language(s): English (Medical Domain)
• License: [License to be specified]
• Finetuned from model: BioBERT base model

Model Sources

• Repository: [GitHub/Model Repository Link - to be added]
• Paper: [Research Paper Link - to be added]

Uses

Direct Use

The primary use of this model is to automatically classify medical conditions by predicting relevant ICD-9 diagnostic codes from clinical text, such as electronic health records, medical notes, or symptom descriptions.

Downstream Use

This model can be integrated into:

• Clinical decision support systems
• Medical coding automation
• Electronic health record (EHR) analysis tools
• Healthcare informatics research

Out-of-Scope Use

• The model should not be used for direct medical diagnosis without professional medical oversight
• It is not intended to replace clinical judgment
• Performance may vary with text outside the medical domain or significantly different from the training corpus

Bias, Risks, and Limitations

• The model's performance is limited to the medical conditions and coding patterns in the MIMIC-3 dataset
• Potential biases from the original training data may be present
• Accuracy can be affected by variations in medical terminology, writing styles, and complex medical cases

Recommendations

• Validate model predictions with medical professionals
• Use as a supportive tool, not a replacement for expert medical assessment
• Regularly evaluate performance on new datasets
• Be aware of potential demographic or contextual biases in the predictions

How to Get Started with the Model

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

# Load the model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained('model_path')
tokenizer = AutoTokenizer.from_pretrained('model_path')

# Example prediction function (similar to the provided get_predictions function)
def predict_icd9_codes(input_text, threshold=0.8):
    # Tokenize input
    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=512, padding='max_length')
    
    # Get model predictions
    with torch.no_grad():
        outputs = model(**inputs)
        predictions = torch.sigmoid(outputs.logits)
        
    # Filter predictions above threshold
    predicted_codes = [model.config.id2label[i] for i in (predictions > threshold).nonzero()[:, 1]]
    
    return predicted_codes

Training Details

Training Data

• Dataset: MIMIC-3 Corpus
• Domain: Medical/Clinical text
• Content: Electronic Health Records (EHR)

Training Procedure

Preprocessing

• Text tokenization
• Maximum sequence length: 512 tokens
• Padding to uniform length
• Potential text normalization techniques

Training Hyperparameters

• Base Model: BioBERT
• Training Regime: Fine-tuning
• Precision: [Specify training precision, e.g., mixed precision]

Evaluation

Testing Data, Factors & Metrics

Testing Data

• Held-out subset of MIMIC-3 corpus
• Diverse medical cases and documentation styles

Metrics

• Precision
• Recall
• F1-Score
• Multi-label classification metrics

Environmental Impact

• Estimated carbon emissions to be calculated
• Compute details to be specified

Technical Specifications

Model Architecture

• Base Model: BioBERT
• Task: Multi-label ICD-9 Code Classification

Citation

[Citation information to be added when research is published]

More Information

For more details about the model's development, performance, and usage, please contact the model developers.