title: A self-learning nurse call system.
abstract: The complexity of continuous care settings has increased due to an ageing population, a dwindling number of caregivers and increasing costs. Electronic healthcare (eHealth) solutions are often introduced to deal with these issues. This technological equipment further increases the complexity of healthcare as the caregivers are responsible for integrating and configuring these solutions to their needs. Small differences in user requirements often occur between various environments where the services are deployed. It is difficult to capture these nuances at development time. Consequently, the services are not tuned towards the users' needs. This paper describes our experiences with extending an eHealth application with self-learning components such that it can automatically adjust its parameters at run-time to the users' needs and preferences. These components gather information about the usage of the application. This collected information is processed by data mining techniques to learn the parameter values for the application. Each discovered parameter is associated with a probability, which expresses its reliability. Unreliable values are filtered. The remaining parameters and their reliability are integrated into the application. The eHealth application is the ontology-based Nurse Call System (oNCS), which assesses the priority of a call based on the current context and assigns the most appropriate caregiver to a call. Decision trees and Bayesian networks are used to learn and adjust the parameters of the oNCS. For a realistic dataset of 1050 instances, correct parameter values are discovered very efficiently as the components require at most 100ms execution time and 20MB memory.
authors.affiliation: Department of Information Technology (INTEC), Ghent University - iMinds, Gaston Crommenlaan 8 bus 201, B-9050 Ghent, Belgium. Electronic address: Femke.Ongenae@intec.Ugent.be.
authors.forename: Femke
authors.initials: F
authors.lastname: Ongenae
authors.affiliation: Department of Information Technology (INTEC), Ghent University - iMinds, Gaston Crommenlaan 8 bus 201, B-9050 Ghent, Belgium. Electronic address: Maxim.Claeys@intec.UGent.be.
authors.forename: Maxim
authors.initials: M
authors.lastname: Claeys
authors.affiliation: Department of Information Technology (INTEC), Ghent University - iMinds, Gaston Crommenlaan 8 bus 201, B-9050 Ghent, Belgium. Electronic address: Wannes.Kerckhove@intec.UGent.be.
authors.forename: Wannes
authors.initials: W
authors.lastname: Kerckhove
authors.affiliation: Department of Information Technology (INTEC), Ghent University - iMinds, Gaston Crommenlaan 8 bus 201, B-9050 Ghent, Belgium. Electronic address: Thomas.Dupont@intec.UGent.be.
authors.forename: Thomas
authors.initials: T
authors.lastname: Dupont
authors.affiliation: iMinds VZW, Gaston Crommenlaan 8 bus 102, B-9050 Ghent, Belgium. Electronic address: Piet.Verhoeve@iminds.be.
authors.forename: Piet
authors.initials: P
authors.lastname: Verhoeve
authors.affiliation: Department of Information Technology (INTEC), Ghent University - iMinds, Gaston Crommenlaan 8 bus 201, B-9050 Ghent, Belgium. Electronic address: Filip.DeTurck@intec.UGent.be.
authors.forename: Filip
authors.initials: F
authors.lastname: De Turck
mesh_terms: D006792:House Calls; D006801:Humans; D009726:Nurses; D017216:Telemedicine / Q000295:instrumentation* / Q000379:methods*
keywords: Adaptive; Nurse call system; Ontology; Self-learning; eHealth