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Multi-step ahead predictive model for blood glucose concentrations of type-1 diabetic patients
Authors
Zaidi, S.M.A., Chandola, V., Ibrahim, M., Romaski, B, Mastrandrea L.D., and Singh, T.,
Source
Scientific Reports,
11(24332),2021.
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
23.22 ± 6.39 mg/dL, 16.77 ± 4.87 mg/dL, 12.84 ± 3.68 and 0.08 ± 0.01 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 80.17 ± 9.20 and 84.81 ± 6.11, respectively. We offer this
tool as a mechanism to enhance the predictive capabilities of algorithms for patients with T1D.
@article{zaidi2021multi,
title={Multi-Step ahead Predictive Model for Blood Glucose Concentrations of Type-1 Diabetic Patients},
author={Zaidi, Syed Mohammed Arshad and Chandola, Varun and Ibrahim, Muhanned and Romanski, Bianca and Mastrandrea, Lucy D and Singh, Tarunraj},
year={2021}
}
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