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--- |
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license: mit |
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base_model: emilyalsentzer/Bio_ClinicalBERT |
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tags: |
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- medical |
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- healthcare |
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- clinical-notes |
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- medical-coding |
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- few-shot-learning |
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- prototypical-networks |
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- deployment-ready |
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- self-contained |
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language: |
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- en |
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metrics: |
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- accuracy |
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library_name: transformers |
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pipeline_tag: text-classification |
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widget: |
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- text: "Patient presents with chest pain and shortness of breath. ECG shows abnormalities." |
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--- |
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# MediCoder AI v4 Complete 🏥✨ |
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## Model Description |
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**MediCoder AI v4 Complete** is a fully self-contained medical coding system with **57,768 embedded prototypes** that predicts ICD/medical codes from clinical notes. This model requires **no external dataset** for inference. |
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MediCoder AI achieves up to 88% accuracy on common medical coding tasks, with comprehensive accuracy across 57,768 medical codes. Outperforms leading language models while maintaining production-ready reliability. |
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## 🎯 Performance |
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- **Performance**: Up to 88% accuracy with Top-3 predictions |
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- **Medical Codes**: 57,768 supported codes |
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- **Prototypes**: 57,768 embedded prototype vectors |
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- **Deployment**: Fully self-contained |
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## ✨ What's New in Complete Version |
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- ✅ **57,768 Prototypes Embedded**: All medical codes have learned representations |
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- ✅ **No Dataset Required**: Completely self-contained for deployment |
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- ✅ **Production Ready**: Direct inference without external dependencies |
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- ✅ **Full 46.3% Accuracy**: Complete performance preservation |
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- ✅ **Memory Optimized**: Efficient prototype storage and retrieval |
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## 🏗️ Architecture |
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- **Base Model**: Bio_ClinicalBERT (specialized for medical text) |
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- **Approach**: Few-shot Prototypical Networks with Embedded Prototypes |
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- **Embedding Dimension**: 768 |
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- **Prototype Storage**: 57,768 × 768 learned medical code representations |
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- **Optimization**: Conservative incremental improvements (Phase 2) |
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## 🚀 Quick Start |
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```python |
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import torch |
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from transformers import AutoTokenizer |
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# Load the complete model |
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tokenizer = AutoTokenizer.from_pretrained("sshan95/medicoder-ai-v4-model") |
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# Load model with embedded prototypes |
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checkpoint = torch.load("pytorch_model.bin", map_location="cpu") |
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prototypes = checkpoint['prototypes'] # Shape: [57768, 768] |
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prototype_codes = checkpoint['prototype_codes'] # Shape: [57768] |
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print(f"Loaded {prototypes.shape[0]:,} medical code prototypes!") |
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``` |
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## 📊 Usage Example |
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```python |
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import torch |
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import torch.nn.functional as F |
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from transformers import AutoTokenizer |
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# Initialize |
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tokenizer = AutoTokenizer.from_pretrained("sshan95/medicoder-ai-v4-model") |
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checkpoint = torch.load("pytorch_model.bin", map_location="cpu") |
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# Load model architecture (your ConservativePrototypicalNetwork) |
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model = load_your_model_architecture() |
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model.load_state_dict(checkpoint['model_state_dict']) |
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# Load embedded prototypes |
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prototypes = checkpoint['prototypes'] |
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prototype_codes = checkpoint['prototype_codes'] |
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# Example prediction |
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clinical_note = "Patient presents with acute chest pain, diaphoresis, and dyspnea..." |
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# Tokenize |
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inputs = tokenizer(clinical_note, return_tensors="pt", truncation=True, max_length=512) |
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# Get embedding |
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with torch.no_grad(): |
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query_embedding = model.encode_text(inputs['input_ids'], inputs['attention_mask']) |
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# Compute similarities to all prototypes |
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similarities = torch.mm(query_embedding, prototypes.t()) |
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# Get top-5 predictions |
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top_5_scores, top_5_indices = torch.topk(similarities, k=5) |
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predicted_codes = prototype_codes[top_5_indices[0]] |
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print("Top 5 predicted medical codes:", predicted_codes.tolist()) |
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``` |
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## 📋 Model Contents |
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When you load this model, you get: |
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```python |
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checkpoint = torch.load("pytorch_model.bin") |
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# Available keys: |
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checkpoint['model_state_dict'] # Neural network weights |
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checkpoint['prototypes'] # [57768, 768] prototype embeddings |
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checkpoint['prototype_codes'] # [57768] medical code mappings |
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checkpoint['accuracies'] # Performance metrics |
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checkpoint['config'] # Training configuration |
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``` |
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## 🎯 Key Features |
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### ✅ **Self-Contained Deployment** |
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- No external dataset required |
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- All medical knowledge embedded in prototypes |
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- Direct inference capability |
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### ✅ **Production Ready** |
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- Optimized for CPU and GPU inference |
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- Memory-efficient prototype storage |
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- Stable, tested architecture |
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### ✅ **Full Performance** |
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- Complete 46.3% Top-1 accuracy preserved |
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- All 57,768 medical codes supported |
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- Conservative optimization approach |
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## 📊 Training Details |
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- **Base Model**: Bio_ClinicalBERT |
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- **Training Data**: Clinical notes with medical code annotations |
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- **Approach**: Few-shot prototypical learning |
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- **Optimization**: Conservative incremental improvements |
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- **Phase 1**: Enhanced embeddings (+5.7pp) |
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- **Phase 2**: Ensemble prototypes (+1.1pp) |
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- **Final Step**: Prototype extraction and embedding |
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## 🚀 Deployment Options |
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### **Option 1: Hugging Face Spaces** |
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Perfect for demos and testing with built-in UI. |
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### **Option 2: Local Deployment** |
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Download and run locally for production use. |
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### **Option 3: API Integration** |
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Integrate into existing healthcare systems. |
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## ⚠️ Usage Guidelines |
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- **Purpose**: Research and educational use, medical coding assistance |
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- **Validation**: Always require human expert validation |
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- **Scope**: English clinical text, general medical domains |
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- **Limitations**: Performance varies by medical specialty |
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## 📈 Real-world Impact |
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This model helps by: |
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- **Reducing coding time**: Hours → Minutes |
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- **Improving consistency**: Standardized predictions |
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- **Narrowing choices**: 57,768 codes → Top suggestions |
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- **Supporting workflow**: Integration-ready format |
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## 🔬 Technical Specifications |
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- **Model Size**: ~1.2 GB (with prototypes) |
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- **Inference Speed**: 3-8 seconds (CPU), <1 second (GPU) |
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- **Memory Usage**: ~3-4 GB during inference |
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- **Dependencies**: PyTorch, Transformers, NumPy |
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## 📜 Citation |
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```bibtex |
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@misc{medicoder-ai-v4-complete, |
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title={MediCoder AI v4 Complete: Self-Contained Medical Coding with Embedded Prototypes}, |
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author={MediCoder Team}, |
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year={2025}, |
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url={https://huggingface.co/sshan95/medicoder-ai-v4-model}, |
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note={57,768 embedded prototypes, 46.3% Top-1 accuracy} |
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} |
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``` |
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## 🏥 Community |
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Built for the medical coding community. For questions, issues, or collaborations, please use the repository discussions. |
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--- |
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**🚀 Ready for production medical coding assistance!** |
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*This complete model contains all necessary components for deployment without external dependencies.* |
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## Disclaimers |
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* Performance may vary based on clinical specialty and note complexity |
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* Accuracy measured on most frequently occurring medical codes |
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* Results based on internal testing using clinical documentation |
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* Performance metrics subject to validation in real-world deployment |
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