Blood Glucose Prediction and Control for Type I Diabetes Management: A Machine Learning Approach

Abstract

Type I Diabetes is a chronic disease in which patients cannot make insulin or make very little insulin to regulate their blood glucose. It affects over 1.7 million adults in the United States. People with Type I Diabetes are reliant on taking insulin every day, and recently insulin pumps and specifically Automated Insulin Delivery (AID) systems have revolutionized diabetes care, making treatment easier and more effective. There are three components needed for an AID system: a Continuous Glucose Monitor which relates patient blood glucose, an Insulin Pump which infuses insulin into the body, and an algorithm which translates information from the first two components to an amount of insulin necessary to keep blood glucose in the target range. Our focus will be on the last component. First, this thesis will provide an overview of machine learning techniques for blood glucose prediction on the novel DiaTrend dataset (2023) which has not been extensively studied before (although research on machine learning models has been applied to previous datasets). Our work finds that adding complexity to our model only barely improves performance and does not justify longer run times and less interpretable results. Rather, we recommend a simple Autoregressive time series model which reaches similar impressive performance to the rest of our models while being simpler for healthcare providers to interpret. In the second part of the thesis, we propose two new AID algorithms which utilize our Autoregressive model: the Threshold Controller and IOB Controller. Rather than a PID or MPC approach, these algorithms rely on a set of simple heuristics similar to what an actual patient would use. We find that in a stressful scenario, these controllers are able to improve time in Target Range by up to 12% more than the leading Open Source OpenAPS oref0 algorithm, while providing safety by mitigating low blood glucose. This work lays the foundation for researchers and healthcare providers to implement new AID algorithms which utilize a combination of machine learning models and patient-based heuristics.