🏥 Hospital Management System

📋 Overview

A comprehensive hospital management system built with Streamlit, featuring real-time analytics, medical image analysis, patient prediction, and multilingual support. The system includes advanced features like brain tumor detection, chatbot assistance, and dynamic dashboards.

⭐ Features

🔷 Core Functionalities

• 📊 Dynamic Dashboard
  • Real-time patient flow monitoring
  • Interactive 3D data visualization
  • Department-wise statistics
  • Bed capacity tracking
  • Staff monitoring

🔬 Medical Analysis Tools

• 🧠 Brain Tumor Detection

  • AI-powered tumor classification
  • Support for multiple tumor types (Pituitary, Meningioma, Glioma)
  • Real-time image processing
  • Confidence score display

• 🔍 Medical Image Analysis

  • DICOM file support
  • Multiple format support (JPG, PNG)
  • Disease detection and classification
  • Automated reporting system
  • Visual annotations with confidence scores

👥 Patient Management

• 📈 Patient Prediction System

  • Readmission risk analysis
  • Multiple factor consideration
  • Automated recommendations
  • Risk factor visualization

• 📊 Analytics Dashboard

  • Patient flow trends
  • Department-wise statistics
  • Length of stay analysis
  • Interactive visualizations
  • Custom time period selection

🤝 Support Features

• 🤖 Hospital Assistant Chatbot

  • Natural language processing
  • PDF/TXT export functionality
  • Chat history management
  • Real-time responses

• 🚨 Emergency Contact System

  • Quick access to emergency services
  • Emergency alert submission
  • Location tracking
  • Priority-based routing

💻 Technical Requirements

📦 Dependencies

streamlit
pandas
numpy
plotly
opencv-python
tensorflow
pillow
pydicom
google-cloud-aiplatform
ultralytics
fpdf

⚙️ Additional Requirements

• Python 3.8+
• CUDA-compatible GPU (for AI models)
• Minimum 8GB RAM
• 50GB storage space

🚀 Installation

1. Clone the repository:

git clone https://github.com/PIYUSH-JOSHI1/Readmission-Prediction.git
cd Readmission-Prediction

2. Install required packages:

pip install -r requirements.txt

3. Set up environment variables:

export GOOGLE_APPLICATION_CREDENTIALS="path/to/credentials.json"
export API_KEY="your-api-key"

4. Run the application:

streamlit run Hospital_Streamlit.py

⚙️ Configuration

🌐 Language Settings

The system supports multiple languages:

• 🇺🇸 English (default)
• 🇪🇸 Spanish
• 🇫🇷 French

Configure language settings in the settings menu.

🎨 Theme Configuration

Currently supports:

• 🌙 Dark theme (default) Custom themes can be configured in dark_theme dictionary.

🔒 Security Features

• Secure file handling
• API key protection
• Session state management
• Secure data transmission

🤖 Model Information

🧠 Brain Tumor Detection Model

• Architecture: Custom CNN
• Input size: 224x224x3
• Output classes: 4 (Pituitary, No Tumor, Meningioma, Glioma)

🔍 Medical Image Analysis Model

• Framework: YOLO v8
• Supported formats: DICOM, JPG, PNG
• Real-time detection capabilities

👥 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.md file for details.

🙏 Acknowledgments

• TensorFlow team for the deep learning framework
• Streamlit team for the web framework
• YOLO team for the object detection model
• Google Cloud team for the AI Platform services

💬 Support

For support, email: drigoon2512M@gmail.com or raise an issue in the repository.

🗺️ Roadmap

• Integration with Electronic Health Records
• Mobile application development
• Additional language support
• Advanced analytics features
• Real-time patient monitoring
• Integration with medical devices

🏗️ System Architecture

hospital-management-system/
├── main.py
├── models/
│   ├── keras_model.h5
│   └── yolov8n.pt
├── uploads/
├── static/
│   └── assets/
├── utils/
│   ├── image_processing.py
│   └── data_analysis.py
└── config/
    └── settings.py