Abstract
A mathematical model of glucose process is generated using symbolic regression. Considering a record of data of glucose and insulin of a patient with type II diabetes, a data driven model is generated. Neural networks are black boxes and symbolic regression can generate equations that express explicit a relationship between input variables with respect to the output response. This model is a personalized version of the metabolism of the patient and different treatments can be considered using this model.
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CITATION STYLE
Torres-Treviño, L. M. (2017). Mathematical model of glucose metabolism by symbolic regression α β. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10062 LNAI, pp. 185–190). Springer Verlag. https://doi.org/10.1007/978-3-319-62428-0_15
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