PCOD/PCOS Management App

A comprehensive React Native mobile application for PCOD and PCOS diagnosis, monitoring, and personalized treatment through AI-powered insights and multi-tier data aggregation.

🎯 Project Overview

This app provides:

• Three-Tier Data Collection: Onboarding, Lab/Clinical (OCR), and Passive Health Streams
• AI Diagnostic Engine: PCOD vs PCOS differentiation with phenotype classification
• Digital Twin Simulator: Predictive "what-if" modeling for lifestyle changes
• Hormonal Sentinel: Proactive monitoring and intervention system
• Metabolic Vision: Computer vision-based food scanning
• Privacy-First Community: Federated learning for anonymized insights

🏗️ Tech Stack

• Mobile: React Native (Expo) with TypeScript
• Backend: Node.js/NestJS hosted on Azure App Service
• Database: Supabase (PostgreSQL + Auth + Real-time + Storage)
• Cloud Services: Microsoft Azure ecosystem
  • Azure Document Intelligence (OCR)
  • Azure Computer Vision (Food scanning)
  • Azure Machine Learning (Diagnostics)
  • Azure Health Data Services (FHIR API)
  • Azure Blob Storage
• State Management: Redux Toolkit
• Health Integration: Google Health Connect (Android)

📁 Project Structure

pcod_app/
├── src/                          # React Native source code
│   ├── modules/                  # Feature modules
│   │   ├── auth/                # Authentication
│   │   ├── onboarding/          # Tier 1: User onboarding
│   │   ├── labData/             # Tier 2: OCR & clinical data
│   │   ├── healthSync/          # Tier 3: Wearable integration
│   │   ├── diagnosis/           # AI diagnostic results
│   │   ├── digitalTwin/         # Lifestyle simulator
│   │   ├── sentinel/            # Hormonal sentinel agent
│   │   ├── hormonalNudges/      # Cycle-aware recommendations
│   │   ├── metabolicVision/     # Food scanning
│   │   ├── avatar/              # 3D health visualization
│   │   ├── community/           # Privacy-preserving insights
│   │   └── reports/             # Practitioner reports
│   ├── services/                # API services
│   ├── store/                   # Redux state management
│   ├── navigation/              # Navigation configuration
│   ├── components/              # Shared UI components
│   ├── types/                   # TypeScript types
│   └── lib/                     # Utilities & config
├── backend/                     # NestJS backend API
│   └── src/
│       ├── modules/             # API modules
│       └── config/              # Configuration
├── azure-services/              # Azure ML & OCR services
│   ├── ocr/                     # Document Intelligence
│   ├── models/                  # ML models
│   └── fhir/                    # Health Data Services
├── supabase/                    # Database schema & migrations
│   └── migrations/
└── docs/                        # Documentation

🚀 Getting Started

Prerequisites

• Node.js 18+ and npm
• Expo CLI
• Android Studio (for Android development)
• Azure account with student credits
• Supabase account

Installation

1. Clone the repository

   git clone <repository-url>
   cd pcod_app

2. Install dependencies

   npm install

3. Set up environment variables

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

4. Run the app

   npm run android

Backend Setup

1. Navigate to backend

   cd backend
   npm install

2. Configure environment

   cp .env.example .env
   # Add Supabase and Azure credentials

3. Run backend

   npm run start:dev

Azure Services Setup

1. Document Intelligence: Create Azure Document Intelligence resource
2. Computer Vision: Create Azure Computer Vision resource
3. Machine Learning: Set up Azure ML workspace
4. App Service: Deploy backend to Azure App Service

See ARCHITECTURE.md for detailed setup instructions.

📚 Documentation

• Architecture Overview - System design and data flow
• API Documentation - Backend API reference
• Contributing Guidelines - How to contribute
• Testing Guide - Testing strategies

🔐 Privacy & Compliance

• End-to-end encryption for medical data
• Row Level Security (RLS) in Supabase
• HIPAA/GDPR compliance through Azure services
• Federated learning for privacy-preserving insights
• User consent management

🎨 Features Roadmap

Phase 1: MVP (Current)

• User authentication and onboarding
• Basic health profile creation
• OCR for blood reports
• Simple diagnostic classification

Phase 2: AI Enhancement

• Digital Twin simulator
• Hormonal Sentinel agent
• Predictive nudges by cycle phase

Phase 3: Advanced Features

• Food scanning with Computer Vision
• 3D avatar visualization
• Federated learning insights
• Practitioner report generation

👥 Team Collaboration

Each team member can work on different modules independently:

• Frontend Team: Work on modules in src/modules/
• Backend Team: Develop APIs in backend/src/modules/
• ML Team: Build models in azure-services/models/
• OCR Team: Implement Document Intelligence in azure-services/ocr/

See CONTRIBUTING.md for branching strategy and workflow.

📄 License

This project is for educational and research purposes.

🤝 Support

For questions or issues, please open a GitHub issue or contact the team.

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Built with ❤️ for women's health