Uncovering the Factors of Diabetes: A Multi-Level SDOH Analysis Using AI and Geospatial Techniques
App Motivations
Brian created this data application to support public health research and human-centered dashboard design. The goal is to move beyond traditional clinical measures by integrating social determinants of health with AI and geospatial analysis to guide intervention planning.
About the App
This multi-level dashboard connects social conditions to diabetes outcomes through two integrated levels:
National Dashboard
Uses AI and machine learning to rank top SDOH predictors such as food environment and air pollution.
State-Level Dashboard (Illinois)
Uses geospatial analysis and Healthy People 2030 measures to identify high-burden counties and support local intervention planning.
The dashboard prioritizes actionable insights and addresses data overload by focusing on key indicators.
Target Users
- Local health professionals
- Public health researchers
- MPH students
- SDOH & Place stakeholders
Features
AI/ML ranking of SDOH predictors
Uses AI/ML in a National Dashboard to rank key health predictors.
Multi-scale analysis from national to local
Provides a multi-level approach for broad public health strategy and local tactics and employs geospatial analysis to pinpoint local intervention areas.
Policy-relevant environmental and air quality indicators
Highlights specific policy levers for the environment and air quality.
Human-centered dashboard design
Addresses data overload by prioritizing actionable Social Determinants of Health (SDOH) indicators.
Web App
Tech Stack
AI/ML + GIS Dashboard + Geospatial Analytics
Social Media
linkedin.com/in/brian-chen-phd-ms-msas-applied-statistics-0b61a611/
