# EndoregDB - Professional Data Infrastructure for Clinical Research

EndoregDB is a comprehensive database framework designed to manage medical and research-related data for clinical trials. This repository focuses on efficient data processing, automated deployment, security, and reproducibility, offering a flexible setup for local development environments as well as distributed systems. It supports the integration of AI/ML tools and advanced image and report processing.

This infrastructure was originally designed for clinical research studies and is optimized for handling large data volumes, including:

- Medical reports,
- Patient imaging and video data,
- Clinical product and treatment data,
  and more.

## 🚀 Key Features

### System Architecture

- **Modular Design**: Built on scalable and reusable components to simplify integration into various environments.
- **Multi-System Support**: Manages configurations for local workstations and production servers.
- **Role-Specific Configuration**: Predefined roles for common use cases:
  - Medical data processing systems
  - AI/ML model deployment
  - Research workstation configuration

### Security & Data Management

- **Data Encryption**: All sensitive data is encrypted, and privacy policies are enforced.
- **Impermanence**: Stateless system configuration with persistence for critical data.
- **Access Control**: Role-based access and identity management integration.

### Data and Processing Environment

- **Data Processing**: Optimized for processing medical datasets with preprocessing tools.
- **AI/ML Support**:
  - Integration of machine learning tools for predictive analysis.
  - TensorFlow, PyTorch, and other frameworks supported for model training.
- **Image/Video Processing**: Support for analyzing patient images and clinical videos.

### Development Tools & Infrastructure

- **Data Science Toolchains**: Pre-configured environments for data processing, analysis, and visualization.
- **Monitoring & Logging**: Setup for continuous monitoring and logging to ensure system stability and performance.

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## 🛠 Getting Started

### Prerequisites

- A Linux-based system (Ubuntu/Debian recommended) or NixOS
- Hardware with sufficient storage for data processing (at least 1 TB recommended)

### Quick Start

1. Clone the repository:

   ```bash
   git clone https://github.com/wg-lux/endoreg-db.git
   cd endoreg-db
   ```

2. Set up your Python environment:
   _TODO_: Explain Devenv / point to other docs

   ```bash
   direnv allow
   ```

3. Run tests:
   Call Devenv Script to run tests

   ```bash
   runtests
   ```

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## 📁 Repository Structure

```
endoreg-db/
├── endoreg_db/                # Main Django app for medical data
│   ├── case_generator/        # Sample case generator
│   ├── data/                  # Medical knowledge base
│   ├── management/            # Data wrangling operations
│   ├── models/                # Data models
│   ├── migrations/            # Database migrations
│   └── serializers/           # Serializers for data
├── .gitignore                 # Git ignore file for unnecessary files
└── README.md                  # Project description and setup instructions
```

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## 🔒 Security Features

- **Data Encryption**: All sensitive patient data is encrypted.
- **Role-Based Access Control**: Configurable roles for managing access to various parts of the system.
- **Logging & Auditing**: Comprehensive logging system that tracks user activities and data changes.

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## 🖥️ Supported Systems

- **Workstations**: Local development or research workstations with low data processing demands.
- **Servers**: Scalable server infrastructure for processing large data volumes, integrated with cloud services for scalability.

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## 🛟 Support

For issues and questions:

- Create an issue in the repository
- Review the Deployment Guide for common issues

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## 📜 License

MIT - see LICENSE

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