AI-READI study will collect and release a flagship medical dataset intended to accelerate machine learning applications and generate novel hypotheses about Type 2 Diabetes Mellitus.
Delve Health and UW Medicine are collaborating to provide new insights into endemic Type 2 diabetes research through artificial intelligence and machine learning (AI/ML) and remote data capture via Delve Health's Clinical StudyPal mobile and web-based digital healthcare platform for both clinical trials and remote patient monitoring.
The goal for UW Medicine researchers in the Artificial Intelligence Ready Equitable Atlas for Diabetes Insight (AI-READI) study is to collect and release a flagship medical dataset intended to accelerate machine learning applications and generate novel hypotheses about Type 2 Diabetes Mellitus. The study will collect fitness tracking data from a smart watch, along with continuous glucose monitoring, to build a biophysical profile of each participant. The data includes the patient's heart rate at 15 second intervals, activity level and SpO2 levels at those same intervals—simultaneously. Delve Health's digital health solution will monitor, track, analyze and report the patient's data analytics to the clinical study staff team.
The AI-READI trial hopes to collect a cross-sectional dataset of more than 4,000 people across the United States, with dual-balancing for self-reported race/ethnicity (e.g., White, Black, Asian-American and Hispanic) and four stages of diabetes severity (e.g., no diabetes, lifestyle controlled, oral medication controlled, and insulin dependent). Building balanced training datasets is critical for the development of unbiased ML models. Thus, rather than targeting the demographic distribution of the U.S. population, the study will intentionally recruit equal numbers of four racial/ethnic groups. The same rationale applies for balancing diabetic severity.
REFERENCE: Delve Health and UW Medicine Announce a Collaboration to Access Remote Data Capture for Diabetes Research.
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