GlucoseAssist: Personalized Blood Glucose Level Predictions and Early Dysglycemia Detection

Regulating blood glucose concentration is crucial for every individual, particularly for patients with diabetes or prediabetes to manage their metabolic health. Poor glucose control results in dysglycemia. Frequent dysglycemia exposure increases the risk of cardiovascular disease, seizures, loss of consciousness, and potentially death. Patients often struggle with glucose control due to a multitude of interrelated behavioral, physiological, and biological factors such as food, insulin intake, and metabolism rate. There is a need for a solution that can accurately predict future adverse dysglycemic events and important parameters such as the area under the glucose curve (AUC). However, current research uses limited input parameters, lacks potential meal-based predictions, is data-hungry and computationally expensive, and predicts a single health outcome. In this research, GlucoseAssist, a novel, personalized, AI-driven system was developed to predict glucose response and area under the glucose curve in real-time and identify dysglycemic events based on diet, health, and medication data. Importantly, the devised tiered architecture uses a multimodal convolutional neural network and random forest classifier with time series data from a clinical dataset with 20,040 Continuous Glucose Monitor (CGM) records. GlucoseAssist accurately predicts blood glucose response for the next 30 minutes with a Root Mean Squared Error of 1.23, Mean Absolute Error of 0.920, and an accuracy of 97.07% for the identification of dysglycemic events.

Conference Paper

IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN’23)