Abstract
Diabetes is one of the most common and difficult non-communicable diseases to deal with in our days. People with diabetes need to keep their glucose levels within a certain range to avoid health complications. Some patients must inject insulin to regulate their glucose levels, and estimating the necessary dose is not an easy task. In this paper, we investigate how to obtain expressions that predict glucose levels using variables such as previous glucose values, food ingestion (in carbohydrates), basal insulin dosing, and dosing of bolus of insulin. This paper proposes the combination of structured grammatical evolution and sparse identification to obtain difference equations governing the dynamics of the glucose levels over time. Glucose prediction serves as a tool for deciding the most convenient insulin dosing. Our technique produces promising results that provide explainable equations and use information usually managed by people with diabetes.
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Acknowledgments
Work financed by the regional government of Madrid and co-financed by the EU Structural Funds through the Community of Madrid project B2017/BMD3773 (GenObIA-CM). Also financed by the PhD project IND2020/TIC-17435 and Spanish Ministry of Economy and Competitiveness with number RTI2018-095180-B-I00 and PID2021-125549OB-I00.
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Parra, D. et al. (2022). Obtaining Difference Equations for Glucose Prediction by Structured Grammatical Evolution and Sparse Identification. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2022. EUROCAST 2022. Lecture Notes in Computer Science, vol 13789. Springer, Cham. https://doi.org/10.1007/978-3-031-25312-6_22
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DOI: https://doi.org/10.1007/978-3-031-25312-6_22
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