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Affiliation

Department of Information and Communication Technologies, Centre for Artificial Intelligence Research (CAIR), University of Agder, Grimstad, Norway

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1 Papers

Physiology-Guided Blood Glucose Predictive Model Using Minimal Blood Glucose Dynamics

Topics: Pattern Recognition and Machine Learning; Medical Informatics; Application of Health Informatics in Clinical Cases; Medical Artificial Intelligence

Authors: Sarala Ghimire ¹ , Turgay Celik ² , Martin Gerdes ¹ and Christian 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

Digital Object Identifier: 10.5220/0013395200003911

Proceedings of HEALTHINF 2025, ISBN: 978-989-758-731-3

Quick 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 window size that enabled the model to better capture longer-term trends and temporal dependencies.