TY - JOUR
T1 - A Glucose-Insulin Mixture Model and Application to Short-Term Hypoglycemia Prediction in the Night Time
AU - Wang, Weijie
AU - Wang, Shaoping
AU - Wang, Xingjian
AU - Liu, Di
AU - Geng, Yixuan
AU - Wu, Teresa
N1 - Funding Information:
The authors acknowledge funding support from Beijing Advanced Innovation Center for Big Data-based Precision Medicine. Authors thank Prof. Mileta Tomovic, Old Dominion University, for his contribution to the language of this paper. Authors also recognize efforts and support by Department of Endocrinology, Xuanwu Hospital Capital Medical University, China, and Department of Endocrinology, The First People’s Hospital of Yangquan City, China for recruitment of subjects and collection of physiological data.
Publisher Copyright:
© 1964-2012 IEEE.
PY - 2021/3
Y1 - 2021/3
N2 - Objective: Insulin-induced hypoglycemia is recognized as a critical problem for diabetic patients, especially at night. To give glucose prediction and advance warning of hypoglycemia of at least 30 minutes, various glucose-insulin models have been proposed. Recognizing the complementary nature of the models, this research proposes a Glucose-Insulin Mixture (GIM) model to predict the glucose values for hypoglycemia detection, by optimally fusing different models with its adjusted parameters to address the inter-and intra-individual variability. Methods: Two types of classic glucose-insulin models, the Ruan model, with single-compartment glucose kinetics, and the Hovorka model, with two-compartment glucose kinetics, are selected as two candidate models. Based on Bayesian inference, GIM is introduced with quantified contributions from the models with the associated parameters. GIM is then applied to predict the glucose values and hypoglycemia events. Results: The proposed model is validated by the nocturnal glucose data collected from 12 participants with type 1 diabetes. The GIM model has promising fitting of RMSE within 0.3465 mmol/L and predicting of RMSE within 0.5571 mmol/L. According to the literature, the hypoglycemia is defined as 3.9 mmol/L, and the GIM model shows good short-Term hypoglycemia prediction performance with the data collected within the last hour (accuracy: 95.97%, precision: 91.77%, recall: 95.60%). In addition, the probability of hypoglycemia event in 30 minutes is inferred. Conclusion: GIM, by fusing various glucose-insulin models via Bayesian inference, has the promise to capture glucose dynamics and predict hypoglycemia. Significance: GIM based short-Term hypoglycemia prediction has potential clinical utility for timely intervention.
AB - Objective: Insulin-induced hypoglycemia is recognized as a critical problem for diabetic patients, especially at night. To give glucose prediction and advance warning of hypoglycemia of at least 30 minutes, various glucose-insulin models have been proposed. Recognizing the complementary nature of the models, this research proposes a Glucose-Insulin Mixture (GIM) model to predict the glucose values for hypoglycemia detection, by optimally fusing different models with its adjusted parameters to address the inter-and intra-individual variability. Methods: Two types of classic glucose-insulin models, the Ruan model, with single-compartment glucose kinetics, and the Hovorka model, with two-compartment glucose kinetics, are selected as two candidate models. Based on Bayesian inference, GIM is introduced with quantified contributions from the models with the associated parameters. GIM is then applied to predict the glucose values and hypoglycemia events. Results: The proposed model is validated by the nocturnal glucose data collected from 12 participants with type 1 diabetes. The GIM model has promising fitting of RMSE within 0.3465 mmol/L and predicting of RMSE within 0.5571 mmol/L. According to the literature, the hypoglycemia is defined as 3.9 mmol/L, and the GIM model shows good short-Term hypoglycemia prediction performance with the data collected within the last hour (accuracy: 95.97%, precision: 91.77%, recall: 95.60%). In addition, the probability of hypoglycemia event in 30 minutes is inferred. Conclusion: GIM, by fusing various glucose-insulin models via Bayesian inference, has the promise to capture glucose dynamics and predict hypoglycemia. Significance: GIM based short-Term hypoglycemia prediction has potential clinical utility for timely intervention.
KW - Bayesian inference
KW - glucose-insulin model
KW - model uncertainty
KW - parameter uncertainty
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U2 - 10.1109/TBME.2020.3015199
DO - 10.1109/TBME.2020.3015199
M3 - Article
C2 - 32776874
AN - SCOPUS:85101764430
SN - 0018-9294
VL - 68
SP - 834
EP - 845
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 3
M1 - 9163275
ER -