Universitat de Girona
Ministerio de Economía y Competitividad (Espanya)
2015
This work is devoted to providing patients and physicians with a novel tool to analyse and extract information for better management of type I diabetes. We use a clustering methodology based on the normalised compression distance to identify different profiles of days. The methodology has been validated using data generated by a simulator of virtual patients, which include an exercise model. Profiling daily data can help physicians and patients cope with information overload and assist in future planning for improved treatments and self-management of diabetes type 1
Partially supported by the Spanish Ministry of Science and Innovation through grant DPI 2013-46982-C2-2-R and the Government of Catalonia trough grant SGR14-1052
Article
Versió acceptada
peer-reviewed
Anglès
Diabetis; Diabetes; Glucèmia; Blood sugar; Anàlisi de conglomerats; Cluster analysis; Informació, Teoria de la; Information theory; Sèries temporals -- Anàlisi; Time-series analysis
Elsevier
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ifacol.2015.10.170
info:eu-repo/semantics/altIdentifier/eissn/1474-6670
DPI2013‐46982‐C2‐2‐R
info:eu-repo/grantAgreement/MINECO//DPI2013-46982-C2-2-R/ES/NUEVOS METODOS PARA LA EFICIENCIA Y SEGURIDAD DEL PANCREAS ARTIFICIAL DOMICILIARIO EN DIABETES TIPO 1/
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
