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# BioClinical Medical Coding Model
## Model Description
This is a BioClinicalModernBERT-based model for automated medical coding. The model predicts ICD-10-CM diagnosis codes and HCPCS/CPT procedure codes from clinical notes.
## Model Architecture
- **Base Model**: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
- **Training**: 3-phase fine-tuning approach
- Phase 1: Dense retrieval training
- Phase 2: Hard negative re-ranking
- Phase 3: Multi-label classification
- **Code Vocabulary**: 31794 modern medical codes
- **Performance**: F1-score: 0.80-0.88 on frequent codes
## Usage
```python
from inference import MedicalCodingPredictor
# Initialize predictor
predictor = MedicalCodingPredictor()
# Predict codes from clinical note
clinical_note = "Patient presents with chest pain and elevated cardiac enzymes..."
predictions = predictor.predict(clinical_note, threshold=0.5)
for pred in predictions:
print(f"Code: {pred['code']}")
print(f"Type: {pred['type']}")
print(f"Description: {pred['description']}")
print(f"Confidence: {pred['confidence']:.3f}")
```
## API Response Format
```json
{
"code": "I25.111",
"type": "ICD-10-CM",
"description": "CODE DESCRIPTION",
"confidence": 0.85,
"f1_score": 0.82
}
```
## Files Included
- `pytorch_model.bin`: Model weights
- `config.json`: Model configuration
- `code_to_idx.json`: Code to index mapping
- `idx_to_code.json`: Index to code mapping
- `code_descriptions.json`: Code descriptions
- `code_f1_scores.json`: Per-code F1 scores
- `inference.py`: Inference script
- `requirements.txt`: Dependencies
## Training Data
Trained on MIMIC-IV clinical notes with temporal matching for accurate code assignment.
## Limitations
- Generic code descriptions (update with medical terminology database)
- Performance varies by code frequency
- Requires clinical validation for production use
## Citation
If you use this model, please cite the MIMIC-IV dataset and acknowledge the multi-stage training approach.
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