Subjects suffering from Type 1 diabetes mellitus need to constantly receive insulin injections. To improve their life quality, a desirable solution is represented by the implementation of an artificial pancreas. In this paper we move a preliminary step towards this goal. Namely, we work at the knowledge base for such a device. One of the main problems is to estimate the Blood Glucose (BG) values, starting from the easily available Interstitial Glucose (IG) ones, and this is the aim of our paper. To face this regression task we avail ourselves of Genetic Programming over a real-world database containing both BG and IG measurements for several subjects suffering from Type 1 diabetes, aiming at finding an explicit relationship between BG and IG values under the form of a mathematical expression. This latter could be the core of the knowledge base part of an artificial pancreas. Experimental comparisons against the state-of-the-art models evidence the quality of the proposed approach.

Accurate estimate of Blood Glucose through Interstitial Glucose by Genetic Programming

SCAFURI, UMBERTO;Della Cioppa, Antonio
2017

Abstract

Subjects suffering from Type 1 diabetes mellitus need to constantly receive insulin injections. To improve their life quality, a desirable solution is represented by the implementation of an artificial pancreas. In this paper we move a preliminary step towards this goal. Namely, we work at the knowledge base for such a device. One of the main problems is to estimate the Blood Glucose (BG) values, starting from the easily available Interstitial Glucose (IG) ones, and this is the aim of our paper. To face this regression task we avail ourselves of Genetic Programming over a real-world database containing both BG and IG measurements for several subjects suffering from Type 1 diabetes, aiming at finding an explicit relationship between BG and IG values under the form of a mathematical expression. This latter could be the core of the knowledge base part of an artificial pancreas. Experimental comparisons against the state-of-the-art models evidence the quality of the proposed approach.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4705009
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