922622

research-article2020

DSTXXX10.1177/1932296820922622Journal of Diabetes Science and TechnologyDave et al.

Original Article

Feature-Based Machine Learning Model for Real-Time Hypoglycemia Prediction

Journal of Diabetes Science and Technology 2021, Vol. 15(4) 842­–855 © 2020 Diabetes Technology Society Article reuse guidelines: sagepub.com/journals-permissions https://doi.org/10.1177/1932296820922622 DOI: 10.1177/1932296820922622 journals.sagepub.com/home/dst

Darpit Dave, MS1, Daniel J. DeSalvo, MD2,3, Balakrishna Haridas, PhD4, Siripoom McKay, MD2,3, Akhil Shenoy, MD2, Chester J. Koh, MD2,3, Mark Lawley, PhD1, and Madhav Erraguntla, PhD1 

Abstract Background: Hypoglycemia is a serious health concern in youth with type 1 diabetes (T1D). Real-time data from continuous glucose monitoring (CGM) can be used to predict hypoglycemic risk, allowing patients to take timely intervention measures. Methods: A machine learning model is developed for probabilistic prediction of hypoglycemia (91% sensitivity for 30- and 60-minute prediction horizons while maintaining specificity >90%. Inclusion of insulin and carbohydrate data yielded performance improvement for 60-minute but not for 30-minute predictions. Model performance was highest for nocturnal hypoglycemia (~95% sensitivity). Shortterm (less than one hour) and medium-term (one to four hours) features for good prediction performance are identified. Conclusions: Innovative feature identification facilitated high performance for hypoglycemia risk prediction in pediatric youth with T1D. Timely alerts of impending hypoglycemia may enable proactive measures to avoid severe hypoglycemia and achieve optimal glycemic control. The model will be deployed on a patient-facing smartphone application in an upcoming pilot study. Keywords continuous glucose monitoring, feature extraction, machine learning, hypoglycemia prediction, insulin pump data, carbohydrate intake

Introduction

1

A prevalent and feared consequence of diabetes management is severe hypoglycemia, which can result in seizures, loss of consciousness, and death. Fear of hypoglycemia is prevalent in adults with diabetes1 and in parents of children with diabetes.2 This fear is greatest during high risk activities such as sleeping, exercising, and driving,3 and often leads to more conservative glucose control, increasing the risks of hyperglycemia, which may lead to long-term micro- and macrovascular complications.4-6

Corresponding Author: Madhav Erraguntla, PhD, Department of Industrial and Systems Engineering, Texas A&M University, 4021 Emerging Technology Building, College Station, TX 77843, USA. Email: [email protected]

Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA 2 Baylor College of Medicine, Houston, TX, USA 3 Texas Children’s Hospital, Houston, TX, USA 4 Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA

Dave et al. Continuous glucose monitoring (CGM) allows frequent, automated sensor glucose readings from interstitial fluid in the subcutaneous tissue space. CGM has been shown to improve glycemic control and reduce glycemic excursion—decreasing both hypoglycemia and hyperglycemia.7 CGM can be used in combination with insulin pumps via sensor augmented pump therapy.8 Realtime CGM devices provide real-time auditory alerts for glucose excursions above or below customized thresholds but do not yet predict impending hypoglycemic events (