Authors:
Sarala Ghimire
1
;
Turgay Celik
2
;
Martin Gerdes
1
and
Christian W. Omlin
2
Affiliations:
1
Department of Information and Communication Technologies, Centre for e-Health, University of Agder, Grimstad, Norway
;
2
Department of Information and Communication Technologies, Centre for Artificial Intelligence Research (CAIR), University of Agder, Grimstad, Norway
Keyword(s):
Blood Glucose, Data-Driven, Hybrid, Mathematical Model, Prediction, Physiology.
Abstract:
Modelling blood glucose and insulin dynamics using mathematical equations requires a deep understanding of individual physiology and relying on numerous predefined parameters necessitating extensive clinical and personal data, making direct use of these models for blood glucose prediction computationally intensive and inaccurate. Though data-driven models are more efficient and require no individual physiology, they produce predictions that are inconsistent with known glucose-insulin interactions. Thus, this study aims to investigate the potentiality of physiological models integrated with data-driven approach for predicting blood glucose level. It intends to extract simple physiological dynamics of blood glucose kinetics and incorporate them into a data-driven model, with less reliance on detailed individual data. The result demonstrated that the model integrating physiological modelling of insulin and meal absorption significantly improved the performance particularly in larger win
dow size that enabled the model to better capture longer-term trends and temporal dependencies.
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