MedIntel - Medical Records Management System

MedIntel is a comprehensive medical records management system that helps healthcare providers and patients manage medical records, analyze reports, and access medical information efficiently.

🌟 Features

• User Authentication

  • Secure signup and login system
  • Password hashing for security
  • Session management

• Medical Records Management

  • Upload and store medical reports (PDF, images)
  • Organize records by hospital and category
  • View and download medical files
  • Categorize reports automatically

• AI-Powered Analysis

  • Automatic categorization of medical reports
  • Extraction of key medical values
  • Integration with LLM for intelligent analysis
  • Support for multiple report types:
    • Blood Tests
    • Urine Tests
    • ECG Reports
    • Diabetes Reports
    • Cholesterol Reports
    • Cardiology Reports

• Data Visualization

  • Integration with Google Sheets for data storage
  • Visual representation of medical data
  • Trend analysis and historical data tracking

• Medical Information System

  • Disease information based on symptoms
  • Medicine information and dosage
  • Precautions for various diseases
  • Research tools and resources

🛠️ Technical Stack

• Backend

  • Python 3.x
  • Flask (Web Framework)
  • MongoDB (Database)
  • PyMongo (MongoDB Python Driver)
  • LangChain (LLM Integration)
  • Groq API (Language Model)
  • Google Sheets API

• Frontend

  • HTML5
  • CSS3
  • JavaScript
  • Bootstrap (UI Framework)

• Additional Tools

  • PyTesseract (OCR for report analysis)
  • PyMuPDF (PDF processing)
  • Pandas (Data analysis)
  • NumPy (Numerical computations)

📋 Prerequisites

Before you begin, ensure you have the following installed:

• Python 3.x
• MongoDB
• Tesseract OCR
• Git

🚀 Installation

1. Clone the repository

   git clone https://github.com/SaiPavankumar22/MedIntel.git
   cd MedIntel

2. Create and activate virtual environment

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Set up environment variables Create a .env file in the root directory with:

   FLASK_SECRET_KEY=your_secret_key
   GROQ_API_KEY=your_groq_api_key
   MONGO_URI=mongodb://localhost:27017/med
   

5. Set up Google Sheets API

   • Create a Google Cloud Project
   • Enable Google Sheets API
   • Create service account credentials
   • Download the JSON key file
   • Place it in the specified path in the code

6. Initialize MongoDB

   • Ensure MongoDB is running
   • The application will create necessary collections automatically

🔧 Configuration

1. Google Sheets Setup

   • Update SHEET_IDS in app.py with your Google Sheet IDs
   • Configure sheet names and columns according to your needs

2. LLM Configuration

   • Update the Groq API key in the environment variables
   • Configure model parameters in app.py

3. File Upload Configuration

   • The upload folder is set to 'uploads/'
   • Ensure the directory has proper write permissions

🏃‍♂️ Running the Application

1. Start MongoDB

   mongod

2. Run the Flask application

   python app.py

3. Access the application

   • Open your browser and go to http://localhost:5000

📁 Project Structure

MedIntel/
├── app.py                  # Main application file
├── requirements.txt        # Python dependencies
├── .env                    # Environment variables
├── uploads/               # Directory for uploaded files
├── static/                # Static files
│   ├── css/              # CSS stylesheets
│   ├── js/               # JavaScript files
│   └── images/           # Image assets
└── templates/             # HTML templates
    ├── accounts.html
    ├── dashboard.html
    ├── directory.html
    ├── landing.html
    ├── login.html
    ├── medical_records.html
    ├── research.html
    └── signup.html

🔐 Security Considerations

• All passwords are hashed using PBKDF2 with SHA256
• API keys are stored in environment variables
• File uploads are validated for type and size
• Session management for user authentication
• Secure file storage and access control

📊 Data Flow

1. Report Upload

   • User uploads medical report
   • System processes the file (OCR for PDF/images)
   • LLM analyzes and categorizes the report
   • Data is stored in MongoDB and Google Sheets

2. Data Analysis

   • System extracts key medical values
   • Data is visualized in the dashboard
   • Historical trends are analyzed
   • Reports are categorized automatically

🤝 Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

👨‍💻 Author

• Devisetti Sai Pavan Kumar
• GitHub: @SaiPavankumar22

🙏 Acknowledgments

• Flask community for the amazing web framework
• MongoDB for the database solution
• Groq for the LLM API
• Google for the Sheets API
• All contributors and users of the project