Towards optimal glycemic control: A case-based reasoning system for predicting postprandial glucose

Abstract

Managing type 1 diabetes presents a daily challenge for patients. Advanced technologies have emerged to simplify disease management and support patients and caregivers. Notably, dosing prandial insulin remains a complex and error-prone task. This study introduces a case-based reasoning system to predict postprandial blood glucose by considering several attributes that influenceglycemic metabolism. The case-based reasoning system leverages the knowledge of historical cases to forecast blood glucose levelsand use it to optimize insulin bolus calculation. The proposed approach holds promise for enhancing glycemic control, offeringpatients a more accurate and personalized insulin regimen.