Personalized Health Recommendation Engine integrating lifestyle, wearable, and medical data with advanced ML, Generative AI, and agentic AI automation.
- Project Overview
- Key Features
- Technologies & Skills
- Getting Started
- Usage
- Project Structure
- Contributing
- License
HealthHelix is an AI-driven platform designed to empower individuals with personalized health insights by aggregating diverse data streams—such as activity metrics from wearables, lifestyle inputs, and medical records—into a unified, dynamic analytics system. It leverages machine learning for health profiling, generative AI for tailored advice generation, and agentic AI for autonomous health tracking and reminders.
- Automated Data Collection: Seamlessly ingest lifestyle, wearable, and medical data using web scraping, API integrations, and ETL pipelines.
- Hybrid Data Storage: Scalable storage with PostgreSQL and MongoDB to handle structured and unstructured health data.
- Exploratory Data Analysis (EDA): Visualize trends, anomalies, and key health insights from aggregated data.
- Machine Learning Profiling: Advanced clustering, prediction, and risk analysis to create individual health profiles.
- Generative AI Recommendations: Context-aware, personalized health advice generated via LLMs and prompt engineering.
- Agentic AI Automation: Proactive health tracking agents that send reminders, monitor changes, and automate checkups.
- Interactive Dashboard: Streamlit-powered interface for real-time monitoring, reporting, and user engagement.
- Data Collection: Python, requests, BeautifulSoup, Scrapy
- Backend: FastAPI, Flask
- Databases: PostgreSQL, MongoDB
- Data Processing: pandas, NumPy
- Machine Learning: scikit-learn, TensorFlow, PyTorch
- Generative AI: OpenAI API, LangChain
- Agentic AI: Autonomous agents framework, multi-agent orchestration
- Visualization: Streamlit, Plotly, Dash
- DevOps: Docker, GitHub Actions