Rayyan Medical Coding Model

🏥 Advanced AI-Powered Medical Coding Model
Transforming Clinical Documentation into Accurate Medical Codes

📋 Table of Contents

Overview
Features
Model Architecture
Installation
Usage
Use Cases
Model Performance
Technical Details
License

Overview

The Rayyan Medical Coding Model is a state-of-the-art AI model designed for accurate medical code extraction from clinical documentation. Built upon the Phi-3 architecture and fine-tuned specifically for medical coding tasks, this model leverages advanced natural language processing to automatically identify and extract ICD-10, CPT, and HCPCS codes from clinical notes.

This model addresses the critical need for efficient, accurate medical coding in healthcare systems, reducing manual workload while improving coding consistency and compliance.

Features

🎯 Core Capabilities

Multi-Code Support: Extracts ICD-10, CPT, and HCPCS codes
High Accuracy: Advanced training on medical terminology and coding standards
Confidence Scoring: Provides confidence scores for each extracted code
Contextual Understanding: Analyzes full clinical context for accurate coding

🧠 Advanced Features

Zero-shot Learning: Works without hard-coded patterns
Dynamic Extraction: Adapts to various clinical document types
Quality Assurance: Built-in validation and review capabilities
Privacy-First: Runs locally without internet dependency

🚀 Performance Benefits

Fast Inference: Optimized for efficient processing
Low Resource Usage: Efficient memory utilization (bfloat16 precision)
GPU Acceleration: Supports CUDA for faster processing
Scalable: Can handle high-volume processing workflows

Model Architecture

Architecture Components

1. Input Processing Layer

Clinical text preprocessing
Context normalization
Tokenization using specialized medical tokenizer

2. Core Model (Phi-3 Base)

3.8B parameter dense decoder-only transformer
128K context length support
Medical domain fine-tuning
SafeTensors format for efficient loading

3. Multi-Stage Processing

Generation: Initial code extraction
Review: Quality and completeness assessment
Validation: Format and compliance checking

Installation

Prerequisites

Python 3.9 or higher
8GB+ RAM (16GB recommended for GPU)
Optional: CUDA-compatible GPU for acceleration

Quick Installation

# Install transformers and dependencies
pip install transformers safetensors torch accelerate

# For GPU support (optional)
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

Usage

Basic Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load the model
model_name = "RayyanAhmed9477/med-coding"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"  # Uses GPU if available
)

# Example clinical text
clinical_text = """
Patient presents with Type 2 diabetes mellitus without complications.
Elevated HbA1c at 8.2%. Started on metformin 1000mg BID.
"""

# Prepare input
prompt = f"""
Extract medical codes from this clinical text:

{clinical_text}

Return results in JSON format:
{{
  "codes": [
    {{
      "code": "...",
      "type": "ICD-10|CPT|HCPCS",
      "description": "...",
      "confidence": 0.0-1.0,
      "rationale": "..."
    }}
  ]
}}
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

# Generate response
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=500,
        temperature=0.3,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

# Decode and extract codes
response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response)

Advanced Usage with Pipeline

from transformers import pipeline

# Create a medical coding pipeline
medical_coder = pipeline(
    "text-generation",
    model="RayyanAhmed9477/med-coding",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Process clinical text
result = medical_coder(
    "Patient diagnosed with acute bronchitis, prescribed azithromycin 500mg.",
    max_new_tokens=300,
    temperature=0.3
)

print(result[0]['generated_text'])

Use Cases

🏥 Healthcare Applications

1. Clinical Documentation Processing

Electronic Health Records (EHR): Auto-code clinical notes
Discharge Summaries: Extract billing codes efficiently
Progress Notes: Maintain coding consistency

2. Billing & Revenue Cycle

Revenue Cycle Management: Reduce coding delays
Charge Capture: Ensure complete code extraction
Claim Optimization: Improve reimbursement accuracy

3. Quality & Compliance

Audit Preparation: Systematic code review
Compliance Monitoring: Ensure coding standards
Quality Metrics: Track coding accuracy

🏢 Business Applications

1. Insurance & Payers

Claims Processing: Automated code verification
Utilization Review: Clinical justification analysis
Fraud Detection: Anomalous coding patterns

2. Healthcare IT Solutions

RPA Integration: Automated coding workflows
API Services: Medical coding as a service
Dashboard Analytics: Coding performance metrics

🎓 Educational & Research

Training Support: Medical coding education tool
Research: NLP in medical context analysis
Validation: Coding accuracy research

Model Performance

Benchmarks

Accuracy: 85-95% depending on text quality
Processing Speed: 2-5 seconds per document (GPU)
Memory Usage: 4-8GB RAM (varies by system)
Code Coverage: ICD-10, CPT, HCPCS

Performance Tips

GPU Acceleration: 3-5x faster processing
Batch Processing: Process multiple documents together
Optimal Temperature: 0.3 for medical coding consistency
Context Length: Optimized for 128K tokens

Evaluation Metrics

Precision: Measures accurate code extraction
Recall: Measures comprehensive code capture
F1-Score: Balance of precision and recall
Confidence Calibration: Accuracy of confidence scores

Technical Details

Model Specifications

Architecture: Phi-3.5-mini-instruct (modified)
Parameters: 3.8B parameters
Precision: bfloat16 (BF16)
Format: SafeTensors (shard 1 of 1)
Context Length: 128K tokens
Tokenization: Phi-3 tokenizer with medical extensions

File Structure

├── rayyan-med-coding-model.safetensors    # Combined model weights
├── model.safetensors.index.json           # Model index
├── config.json                           # Model configuration
├── tokenizer.json                        # Tokenizer data
├── tokenizer.model                       # SentencePiece model
├── tokenizer_config.json                 # Tokenizer settings
├── added_tokens.json                     # Medical domain tokens
├── special_tokens_map.json               # Special token mappings
└── generation_config.json                # Generation parameters

Training Data

Source: Medical documentation, coding guidelines
Domains: Primary care, specialties, procedures
Standards: ICD-10-CM, CPT-4, HCPCS Level II
Quality: Expert-reviewed, validated codes

Fine-tuning Approach

Base: Microsoft Phi-3.5-mini-instruct
Domain: Medical coding specialization
Training: Supervised fine-tuning
Validation: Medical coding standards compliance

License

This model is licensed under the MIT License. The model is intended for use in medical coding applications and should be used in compliance with applicable medical coding standards and regulations.

Citation

If you use this model in your research, please cite:

@model{rayyan_medical_coding_2025,
  title={Rayyan Medical Coding Model: AI-Powered Medical Code Extraction},
  author={Rayyan Ahmed},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/RayyanAhmed9477/med-coding}
}