🏥 AI Medical Prescription Verification System

A comprehensive AI-powered system for medical prescription verification, drug interaction detection, age-specific dosage recommendations, and NLP-based prescription parsing.

🚀 Features

• Drug Interaction Detection: Check for dangerous drug-drug interactions using multiple medical databases
• Age-Specific Dosage: Calculate appropriate dosages based on patient age, weight, and medical conditions
• NLP Prescription Parser: Extract structured information from prescription text using Ollama (granite3.2-vision model) and Google Gemini AI
• Alternative Medication Finder: Suggest safer alternatives when contraindications exist
• Interactive Dashboard: User-friendly Streamlit interface for healthcare professionals

🛠️ Technology Stack

Backend (FastAPI)

• FastAPI: High-performance API framework
• SQLite/PostgreSQL: Database for caching and storage
• Ollama: Primary NLP processing (using granite3.3:2b model)
• Google Gemini AI: Fallback AI processing
• RxNorm API: Drug terminology and interactions
• OpenFDA: Adverse event data

Frontend (Streamlit)

• Streamlit: Interactive web interface
• Plotly: Data visualizations
• Pandas: Data manipulation
• Requests: API communication

📋 Prerequisites

• Python 3.8+
• Docker (for containerized deployment)
• Ollama (for local AI processing) with granite3.3:2b model

🔧 Installation

Quick Setup with Docker (Recommended)

# Clone the repository
git clone <repository-url>
cd ibm-project

# Build and run with Docker Compose
docker-compose up --build

Manual Setup

1. Backend Setup

cd backend
python -m venv venv
# On Windows: venv\Scripts\activate
# On Unix/MacOS: source venv/bin/activate
pip install -r requirements.txt

2. Frontend Setup

cd frontend
python -m venv venv
# On Windows: venv\Scripts\activate
# On Unix/MacOS: source venv/bin/activate
pip install -r requirements.txt

3. Environment Configuration

cp .env.example .env
# Edit .env with your API keys

4. Ollama Setup

# Install Ollama from https://ollama.com/
# Pull the granite3.3:2b model
ollama pull granite3.3:2b

🚀 Running the Application

With Docker (Recommended)

# Build and run with Docker Compose
docker-compose up --build

# Access the application:
# Frontend: http://localhost:8501
# Backend API: http://localhost:8000
# API Documentation: http://localhost:8000/docs

Development Mode

1. Start Backend

cd backend
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

2. Start Frontend

cd frontend  
streamlit run streamlit-frontend.py --server.port 8501

3. Access Application

• Frontend: http://localhost:8501
• Backend API: http://localhost:8000
• API Documentation: http://localhost:8000/docs

📖 API Documentation

The system provides RESTful APIs for all functionality:

Drug Interactions

• POST /api/check-interactions - Check drug interactions
• GET /api/interaction-details/{drug1}/{drug2} - Get interaction details

Dosage Calculations

• POST /api/age-dosage - Calculate age-specific dosage
• GET /api/dosage-guidelines/{drug_name} - Get dosage guidelines

Prescription Parsing

• POST /api/parse-prescription - Parse prescription text
• POST /api/extract-entities - Extract medical entities

Alternative Medications

• POST /api/alternative-drugs - Find alternative medications
• GET /api/drug-classes/{drug_name} - Get drug therapeutic classes

🔐 Security & Compliance

• HIPAA Considerations: The system is designed with healthcare compliance in mind
• Data Encryption: All sensitive data is encrypted at rest and in transit
• Access Controls: API authentication and authorization
• Audit Logging: Comprehensive logging for compliance tracking

⚠️ Important: This system is for educational/research purposes. Always consult healthcare professionals for medical decisions.

🧪 Testing

Unit Tests

# Run backend tests
cd backend
pytest tests/

# Run frontend tests  
cd frontend
pytest tests/

📊 Monitoring

The system includes built-in monitoring and analytics:

• API usage metrics
• Error tracking and logging
• Performance monitoring
• User interaction analytics

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

🙏 Acknowledgments

• Ollama for local AI processing with granite3.3: model
• Google for Gemini AI
• OpenFDA for adverse event data
• RxNorm for drug terminology

For support and questions:

• 🐛 Issues: GitHub Issues

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Disclaimer: This system is intended for educational and research purposes only. It should not be used as the sole basis for medical decisions. Always consult qualified healthcare professionals for medical advice.