GlucoseAssist: Personalized Blood Glucose Level Predictions and Early Dysglycemia Detection

Abstract

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.

Asiful Arefeen

Graduate Research Assistant

I am a PhD student at Arizona State University (ASU). I am working under the supervision of Professor Hassan Ghasemzadeh at the Embedded Machine Intelligence Lab (EMIL). My research topics include machine learning, health monitoring system development and mobile health. I received my B.S. in Electrical and Electronic Engineering from Bangladesh University of Engineering & Technology (BUET) in 2019.

Hassan Ghasemzadeh

Director

Hassan Ghasemzadeh is an Associate Professor of Biomedical Informatics at Arizona State University (ASU) and a Computer Science Adjunct Faculty at Washington State University (WSU).