Salutogenesis
The Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READi) Grand Challenge
The Salutogenesis project seeks to create and share a flagship ethically-sourced dataset of type 2 diabetes. The data will be optimized for future artificial intelligence/machine learning (AI/ML) analysis that could provide critical insights and especially shine a light on the salutogenic pathways from diabetes to return to health. The team of investigators will aim to collect a cross-sectional dataset of 4,000+ people and longitudinal data from 10% of the study cohort across the US. The long-term objective for this project is to develop a foundational dataset in diabetes, agnostic to existing classification criteria or biases, which can be used to reconstruct a temporal atlas of T2DM development and reversal towards health (i.e., salutogenesis). Six cross-disciplinary project modules involving teams located across eight institutions will work together to develop this flagship dataset.
Who We Are
Aaron Lee
Contact Principal Investigator
University of Washington
Cecilia Lee
University of Washington
Sally Baxter
Other Principal Investigator
University of California San Diego
Christopher Chute
Other Principal Investigator
Johns Hopkins University
Megan Collins
Other Principal Investigator
Johns Hopkins University
Kadija Ferryman
Johns Hopkins University
Michelle Hribar
Other Principal Investigator
Oregon Health & Science University
Samantha Hurst
Other Principal Investigator
University of California San Diego
Hiroshi Ishikawa
Other Principal Investigator
Oregon Health & Science University
T. Y. Alvin Liu
Johns Hopkins University
Gerald McGwin
Other Principal Investigator
University of Alabama at Birmingham
Shannon McWeeney
Other Principal Investigator
Health & Science University
Camille Nebeker
Other Principal Investigator
University of California San Diego
Cynthia Owsley
Other Principal Investigator
University of Alabama at Birmingham