Case-base maintenance of a personalised and adaptive CBR bolus insulin recommender system for type 1 diabetes
dc.contributor.author
dc.date.accessioned
2019-01-23T08:03:45Z
dc.date.available
2019-01-23T08:03:45Z
dc.date.issued
2019-05-01
dc.identifier.issn
0957-4174
dc.identifier.uri
dc.description.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
dc.description.sponsorship
This project has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No. 689810, www.pepper.eu.com/, PEPPER, and the grant of the University of Girona 20162018 (MPCUdG2016). The work has been developed
with the support of the research group SITES awarded with distinction by the Generalitat de
Catalunya (SGR 20142016)
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.relation.isformatof
Versió postprint del document publicat a: https://doi.org/10.1016/j.eswa.2018.12.036
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© Expert Systems with Applications, 2019, vol. 121, p. 338-346
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Articles publicats (D-EEEiA)
dc.rights
Tots els drets reservats
dc.subject
dc.title
Case-base maintenance of a personalised and adaptive CBR bolus insulin recommender system for type 1 diabetes
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.date.embargoEndDate
info:eu-repo/date/embargoEnd/2021-05-01
dc.relation.projectID
info:eu-repo/grantAgreement/EC/H2020/689810/EU/Patient Empowerment through Predictive PERsonalised decision support/PEPPER
dc.type.version
info:eu-repo/semantics/acceptedVersion
dc.identifier.doi
dc.identifier.idgrec
029229
dc.type.peerreviewed
peer-reviewed
dc.relation.FundingProgramme
dc.relation.ProjectAcronym