Case-base maintenance of a personalised and adaptive CBR bolus insulin recommender system for type 1 diabetes

Text Complet
AM-CaseBaseMaintenance.pdf embargoed access
Sol·licita còpia a l'autor de l'article
En omplir aquest formulari esteu demanant una còpia de l'article dipositat al repositori institucional (DUGiDocs) al seu autor o a l'autor principal de l'article. Serà el mateix autor qui decideixi lliurar una còpia del document a qui ho sol•liciti si ho creu convenient. En tot cas, la Biblioteca de la UdG no intervé en aquest procés ja que no està autoritzada a facilitar articles quan aquests són d'accés restringit.
People with type 1 diabetes must control their blood glucose level through insulin infusion either with several daily injections or with an insulin pump. However, estimating the required insulin dose is not easy. Recommender systems, mainly based on Case-Based Reasoning (CBR), are being developed to provide recommendations to users. These systems are designed to keep the experiences or cases of the user in a case-base, which requires maintenance to keep system's response accurate and efficient. This paper proposes a case-base maintenance methodology that combines case-base redundancy reduction and attribute weight learning. Contrary to previous approaches designed for classification problems, the maintenance methodology presented in this paper deals with numerical recommendations. It can manage a potentially huge case-base due to the combinatorial derived from the number of attributes used to represent a case. The proposed approach has been tested using the UVA/PADOVA type 1 diabetes simulator and the results demonstrate that it can accomplish better levels of accuracy than other insulin recommender systems mentioned in the literature, when a large number of attributes is considered ​
​Tots els drets reservats