Case-base maintenance of a personalised and adaptive CBR bolus insulin recommender system for type 1 diabetes. (1st May 2019)

• Record Type: Journal Article
• Authors: Torrent-Fontbona, Ferran; Massana, Joaquim; López, Beatriz
• Is Part Of: Expert systems with applications. Volume 121(2019)
• Page Start: 338
• Page End: 346
• Publication Date: 2019-05-01
• Subjects: Case-based reasoning -- Insulin recommender system -- Case-base maintenance -- Attribute weight learning -- Patient empowerment -- Diabetes; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2018.12.036
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Case-base maintenance of a CBR bolus recommender system. Personalised and adaptive attributes weight learning of the CBR recommender. System tested with UVA/PADOVA diabetes simulator with positive results. Abstract: 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.