Researchers have used a new data-driven approach to learn more about people with Type 1 diabetes, who account for about 5-10% of all diabetes diagnoses.
An interdisciplinary team of researchers from the University of Missouri, Children’s Mercy Kansas City and Texas Children’s Hospital has used a new data-driven approach to learn more about persons with Type 1 diabetes, who account for about 5-10% of all diabetes diagnoses. The team gathered its information through health informatics and applied artificial intelligence (AI) to better understand the disease.
In the study, the team analyzed publicly available, real-world data from about 16,000 participants enrolled in the T1D Exchange Clinic Registry. By applying a contrast pattern mining algorithm developed at the MU College of Engineering, the team was able to identify major differences in health outcomes among people living with Type 1 diabetes who do or do not have an immediate family history of the disease.
Chi-Ren Shyu, the director of the MU Institute for Data Science and Informatics (MUIDSI), led the AI approach used in the study, and said the technique is exploratory in nature.
“Here we let the computer do the work of connecting millions of dots in the data to identify only major contrasting patterns between individuals with and without a family history of Type 1 diabetes, and to do the statistical testing to make sure we are confident in our results,” said Shyu, the Paul K. and Dianne Shumaker Professor in the MU College of Engineering.