Multi-step ahead predictive model for blood glucose concentrations of type-1 diabetic patients

Scientific Reports, vol. 11, pp. 24332

Abstract

Continuous monitoring of blood glucose (BG) levels is a key aspect of diabetes management. Patients with Type-1 diabetes (T1D) require an effective tool to monitor these levels in order to make appropriate decisions regarding insulin administration and food intake to keep BG levels in target range. Effectively and accurately predicting future BG levels at multi-time steps ahead benefits a patient with diabetes by helping them decrease the risks of extremes in BG including hypo- and hyperglycemia. In this study, we present a novel multi-component deep learning model BG-Predict that predicts the BG levels in a multi-step look ahead fashion. The model is evaluated both quantitatively and qualitatively on actual blood glucose data for 97 patients. For the prediction horizon (PH) of 30 mins, the average values for root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and normalized mean squared error (NRMSE) are [Formula: see text] mg/dL, 16.77 ± 4.87 mg/dL, [Formula: see text] and [Formula: see text] respectively. When Clarke and Parkes error grid analyses were performed comparing predicted BG with actual BG, the results showed average percentage of points in Zone A of [Formula: see text] and [Formula: see text] respectively. We offer this tool as a mechanism to enhance the predictive capabilities of algorithms for patients with T1D.

Authors 6

1. Syed Mohammed Arshad Zaidi corresponding

   University at Buffalo, State University of New York

   Affiliation as printed

   Computer Science and Engineering, University at Buffalo-SUNY, Buffalo, 14260, USA. szaidi2@buffalo.edu

   Computer Science and Engineering, University at Buffalo-SUNY, Buffalo, 14260, USA

2. Varun Chandola

   University at Buffalo, State University of New York

   Affiliation as printed

   Computer Science and Engineering, University at Buffalo-SUNY, Buffalo, 14260, USA

3. Muhanned Ibrahim

   University at Buffalo, State University of New York

   Affiliation as printed

   Computer Science and Engineering, University at Buffalo-SUNY, Buffalo, 14260, USA

4. Bianca Romanski Aachen Medical Information Technology (MedIT)

   RWTH Aachen University

   Affiliation as printed

   Medical Information Technology, RWTH Aachen University, Pauwelsstr. 20, 52074, Aachen, Germany

5. Lucy D. Mastrandrea

   University at Buffalo, State University of New York

   Affiliation as printed

   Division of Pediatric Endocrinology, University at Buffalo-SUNY, Buffalo, 14203, USA

6. Tarunraj Singh

   University at Buffalo, State University of New York

   Affiliation as printed

   Mechanical and Aerospace Engineering, University at Buffalo-SUNY, Buffalo, 14260, USA