metadata

license: mit
base_model: emilyalsentzer/Bio_ClinicalBERT
tags:
  - medical
  - healthcare
  - clinical-notes
  - medical-coding
  - few-shot-learning
  - prototypical-networks
language:
  - en
metrics:
  - accuracy
library_name: transformers
pipeline_tag: text-classification

MediCoder AI v4 🏥

Model Description

MediCoder AI v4 is a state-of-the-art medical coding system that predicts ICD/medical codes from clinical notes with 46.3% Top-1 accuracy. Built on Bio_ClinicalBERT with few-shot prototypical learning, it can handle ~57,000 medical codes.

🎯 Performance

Top-1 Accuracy: 46.3%
Top-3 Accuracy: ~52%
Top-5 Accuracy: ~54%
Improvement: +6.8 percentage points over baseline
Medical Codes: ~57,000 supported codes

🏗️ Architecture

Base Model: Bio_ClinicalBERT (specialized for medical text)
Approach: Few-shot Prototypical Networks
Embedding Dimension: 768
Optimization: Conservative incremental improvements (Phase 2)

🚀 Usage

import torch
from transformers import AutoTokenizer

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("your-username/medicoder-ai-v4-model")
model = torch.load("pytorch_model.bin", map_location="cpu")

# Example usage
clinical_note = "Patient presents with chest pain and shortness of breath..."

# Tokenize
inputs = tokenizer(clinical_note, return_tensors="pt", 
                  truncation=True, max_length=512)

# Get predictions (top-5 medical codes)
with torch.no_grad():
    embeddings = model.encode_text(inputs['input_ids'], inputs['attention_mask'])
    similarities = torch.mm(embeddings, model.prototypes.t())
    top_codes = similarities.topk(5).indices
    
print("Top 5 predicted medical codes:", top_codes)

📊 Training Details

Training Data: Medical clinical notes with associated codes
Training Approach: Few-shot learning with prototypical networks
Optimization Strategy: Conservative incremental improvements
Phases:
- Phase 1: Enhanced embeddings and pooling (+5.7pp)
- Phase 2: Ensemble prototypes with attention (+1.1pp)

🎯 Use Cases

Medical Coding Assistance: Help medical coders find relevant codes
Clinical Decision Support: Suggest appropriate diagnostic codes
Healthcare Analytics: Automated coding for large datasets
Research: Medical text analysis and categorization

⚠️ Limitations

Designed for English clinical text
Performance varies by medical specialty
Requires domain expertise for validation
Not a replacement for professional medical coding

📋 Model Details

Model Size: ~670 MB
Inference Speed: 3-8 seconds (CPU), <1 second (GPU)
Memory Requirements: ~2-3 GB during inference
Self-contained: No external dataset dependencies

🔬 Technical Details

Few-shot Learning: Learns from limited examples per medical code
Prototypical Networks: Creates representative embeddings for each code
Ensemble Prototypes: Multiple prototypes per code for better coverage
Attention Aggregation: Smart combination of multiple examples

📈 Evaluation

Evaluated on held-out medical coding dataset with standard metrics:

Precision, Recall, F1-score
Top-K accuracy (K=1,3,5,10,20)
Comparison with baseline methods

🏥 Real-world Impact

This model helps medical professionals by:

Reducing coding time from hours to minutes
Improving coding accuracy and consistency
Narrowing 57,000+ codes to top suggestions
Supporting healthcare workflow automation

📜 Citation

If you use this model, please cite:

@misc{medicoder-ai-v4,
  title={MediCoder AI v4: Few-shot Medical Coding with Prototypical Networks},
  author={Your Name},
  year={2025},
  url={https://huggingface.co/your-username/medicoder-ai-v4-model}
}

📞 Contact

For questions or collaborations, please reach out via the model repository issues.

Built with ❤️ for the medical community