Machine Learning approach for glycemia prediction in type 1 diabetes

Lead Investigator: Sabatina Criscuolo, University of Naples Federico II
Title of Proposal Research: Machine Learning approach for glycemia prediction in type 1 diabetes
Vivli Data Request: 10094
Funding Source: None
Potential Conflicts of Interest: None

Summary of the Proposed Research:

Type 1 Diabetes (T1D) is an autoimmune disease that affects millions of people worldwide. A critical issue in T1D patients is the managing of Postprandial Glucose Response (PGR), through the dosing of the insulin bolus to inject before meals. The Artificial Pancreas (AP), combining autonomous insulin delivery and blood glucose monitoring, is a promising solution. However, state-of-the-art APs require several information for bolus delivery, such as the estimated carbohydrate intake over the meals. This is mainly related to the limited knowledge of the determinants of PGR. Based on these considerations, this proposed research aim to determine the effect of nutritional factorson Blood Glucose Levels (BGLs) prediction was conducted by Machine Learning (ML) methods and Explainable Artificial Intelligence.

Requested Studies:

Type 1 Diabetes EXercise Initiative: The Effect of Exercise on Glycemic Control in Type 1 Diabetes Study
Data Contributor: Jaeb Center for Health Research Foundation, Inc.
Study ID: T1-DEXI
Sponsor ID: T1-DEXI

Type 1 Diabetes EXercise Initiative Pediatric Study (T1DexiP): The Effect of Exercise on Glycemic Control in Youth with Type 1 Diabetes
Data Contributor: Jaeb Center for Health Research Foundation, Inc.
Study ID: T1-DEXIP
Sponsor ID: T1-DEXIP