The researchers gathered information through health informatics and applied artificial intelligence (AI) to better understand the disease.
In the study, they 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, researchers were 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.
The technique is exploratory in nature, says Chi-Ren Shyu, professor in the University of Missouri College of Engineering, who led the AI approach.
“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,” says Shyu, director of the Institute for Data Science and Informatics (MUIDSI).
The analysis resulted in some unfamiliar findings, says Erin Tallon, a graduate student in the MUIDSI, and lead author of the paper in Diabetes Care.
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