Blood glucose concentration control is a classic negative feedback problem with insulin secreted by the pancreas as a control variable. Type 1 Diabetes is a chronic metabolic disease caused by a cellular-mediated autoimmune destruction of the pancreas beta-cells, so exogenous insulin administration is needed to regulate the glycaemia. Postprandial glucose regulation is typically based on the knowledge of an estimation of the ingested carbohydrates, of the Carbohydrate-to-insulin ratio, of the correction factor, of the insulin still active and of a measure of the glycaemia just before the meal. Despite the use of this information meal compensation is yet a key unsolved issue. In this letter a new approach based on machine-learning methodologies is proposed to improve postprandial glucose regulation. The proposed approach uses the multiple K-nearest neighbors classification algorithm to predict postprandial glucose profile due to the nominal therapy and to suggest a correction to time and/or amount of the meal bolus. This approach has been successfully validated on the adult in silico population of the UVA/PADOVA simulator, which has been accepted by the Food and Drug Administration as a substitute to animal trials.

Postprandial glucose regulation via KNN meal classification in type 1 diabetes / Aiello, ELEONORA MARIA; Toffanin, Chiara; Messori, Mirko; Cobelli, Claudio; Magni, Lalo. - In: IEEE CONTROL SYSTEMS LETTERS. - ISSN 2475-1456. - 3:2(2019), pp. 230-235. [10.1109/LCSYS.2018.2844179]

Postprandial glucose regulation via KNN meal classification in type 1 diabetes

AIELLO, ELEONORA MARIA
Primo
;
2019-01-01

Abstract

Blood glucose concentration control is a classic negative feedback problem with insulin secreted by the pancreas as a control variable. Type 1 Diabetes is a chronic metabolic disease caused by a cellular-mediated autoimmune destruction of the pancreas beta-cells, so exogenous insulin administration is needed to regulate the glycaemia. Postprandial glucose regulation is typically based on the knowledge of an estimation of the ingested carbohydrates, of the Carbohydrate-to-insulin ratio, of the correction factor, of the insulin still active and of a measure of the glycaemia just before the meal. Despite the use of this information meal compensation is yet a key unsolved issue. In this letter a new approach based on machine-learning methodologies is proposed to improve postprandial glucose regulation. The proposed approach uses the multiple K-nearest neighbors classification algorithm to predict postprandial glucose profile due to the nominal therapy and to suggest a correction to time and/or amount of the meal bolus. This approach has been successfully validated on the adult in silico population of the UVA/PADOVA simulator, which has been accepted by the Food and Drug Administration as a substitute to animal trials.
2019
2
Aiello, ELEONORA MARIA; Toffanin, Chiara; Messori, Mirko; Cobelli, Claudio; Magni, Lalo
Postprandial glucose regulation via KNN meal classification in type 1 diabetes / Aiello, ELEONORA MARIA; Toffanin, Chiara; Messori, Mirko; Cobelli, Claudio; Magni, Lalo. - In: IEEE CONTROL SYSTEMS LETTERS. - ISSN 2475-1456. - 3:2(2019), pp. 230-235. [10.1109/LCSYS.2018.2844179]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/402405
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