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. 2018 Feb:71:129-141.
doi: 10.1016/j.conengprac.2017.10.013.

Model-Fusion-Based Online Glucose Concentration Predictions in People with Type 1 Diabetes

Xia Yu  1 , Kamuran Turksoy  2 , Mudassir Rashid  3 , Jianyuan Feng  3 , Nicole Frantz  2 , Iman Hajizadeh  3 , Sediqeh Samadi  3 , Mert Sevil  2 , Caterina Lazaro  4 , Zacharie Maloney  2 , Elizabeth Littlejohn  5 , Laurie Quinn  6 , Ali Cinar  2   3
Affiliations

Model-Fusion-Based Online Glucose Concentration Predictions in People with Type 1 Diabetes

Xia Yu et al. Control Eng Pract. 2018 Feb.

Abstract

Accurate predictions of glucose concentrations are necessary to develop an artificial pancreas (AP) system for people with type 1 diabetes (T1D). In this work, a novel glucose forecasting paradigm based on a model fusion strategy is developed to accurately characterize the variability and transient dynamics of glycemic measurements. To this end, four different adaptive filters and a fusion mechanism are proposed for use in the online prediction of future glucose trajectories. The filter fusion mechanism is developed based on various prediction performance indexes to guide the overall output of the forecasting paradigm. The efficiency of the proposed model fusion based forecasting method is evaluated using simulated and clinical datasets, and the results demonstrate the capability and prediction accuracy of the data-based fusion filters, especially in the case of limited data availability. The model fusion framework may be used in the development of an AP system for glucose regulation in patients with T1D.

Keywords: adaptive filtering algorithms; model fusion strategy; online glucose prediction; type 1 diabetes.

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Figures

Figure 1
Figure 1
Flowchart of multi-model data fusion procedure for online prediction.
Figure 2
Figure 2
Comparison of representative measured and 30-min predicted glucose concentration profiles for Subjects #5 (top), #3 (middle) and #8 (bottom) based on hybrid predictor. Associated PRED-EGA analysis shown in Figure 3 and summary statistics provided in Table 7.
Figure 3
Figure 3
PRED-EGA for adult subjects #5 (a), adolescent subject #3 (b) and child subject #8 (c) based on hybrid predictor. P-EGA compares the predicted CGM to the actual measured CGM. R-EGA compares the rate of change of the predicted CGM (Estimated Rate) to the actual rate of change of the measured CGM (Reference Rate). Associated glucose prediction profiles shown in Figure 2 and summary statistics provided in Table 7.
Figure 4
Figure 4
Comparison of measurements and 30-min predicted glucose profiles for subjects (A1, B1 and C1) in clinical experiments based on hybrid predictor. Subplot (c) plots the first 600 samples and subplot (d) the complete 2000 samples from subject C1 for easy comparison of trends. Associated PRED-EGA analysis shown in Figure 5 and summary statistics provided in Table 8.
Figure 5
Figure 5
PRED-EGA for a select number of subjects (A1, B1 and C1) in clinical experiments based on hybrid predictor. P-EGA compares the predicted CGM to the actual measured CGM. R-EGA compares the rate of change of the predicted CGM (Estimated Rate) to the actual rate of change of the measured CGM (Reference Rate). Associated glucose prediction profiles shown in Figure 4 and summary statistics provided in Table 8.
Figure 6
Figure 6
Prediction accuracy assessed with different prediction horizons (PH) by the median and first/third quartile of the RMSE.
See this image and copyright information in PMC

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