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dc.contributor.authorGarcía Rudolph, Alejandro
dc.contributor.authorGibert, Karina
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa
dc.date.accessioned2015-07-01T08:22:03Z
dc.date.available2015-07-01T08:22:03Z
dc.date.created2014-09-01
dc.date.issued2014-09-01
dc.identifier.citationGarcía-Rudolph, A.; Gibert, Karina. A data mining approach to identify cognitive NeuroRehabilitation Range in Traumatic Brain Injury patients. "Expert systems with applications", 01 Setembre 2014, vol. 41, núm. 11, p. 5238-5251.
dc.identifier.issn0957-4174
dc.identifier.urihttp://hdl.handle.net/2117/28480
dc.description.abstractCognitive rehabilitation (CR) treatment consists of hierarchically organized tasks that require repetitive use of impaired cognitive functions in a progressively more demanding sequence. Active monitoring of the progress of the subjects is therefore required, and the difficulty of the tasks must be progressively increased, always pushing the subjects to reach a goal just beyond what they can attain. There is an important lack of well-established criteria by which to identify the right tasks to propose to the patient. In this paper, the NeuroRehabilitation Range (NRR) is introduced as a means of identifying formal operational models. These are to provide the therapist with dynamic decision support information for assigning the most appropriate CR plan to each patient. Data mining techniques are used to build data-driven models for NRR. The Sectorized and Annotated Plane (SAP) is proposed as a visual tool by which to identify NRR, and two data-driven methods to build the SAP are introduced and compared. Application to a specific representative cognitive task is presented. The results obtained suggest that the current clinical hypothesis about NRR might be reconsidered. Prior knowledge in the area is taken into account to introduce the number of task executions and task performance into NRR models and a new model is proposed which outperforms the current clinical hypothesis. The NRR is introduced as a key concept to provide an operational model identifying when a patient is experiencing activities in his or her Zone of Proximal Development and, consequently, experiencing maximum improvement. For the first time, data collected through a CR platform has been used to find a model for the NRR. © 2014 Elsevier Ltd. All rights reserved.
dc.format.extent14 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística aplicada::Estadística biosanitària
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica::Anàlisi multivariant
dc.subject.lcshMultivariate analysis
dc.subject.otherAlgorithms
dc.subject.otherMachine learning
dc.subject.otherStatistics
dc.titleA data mining approach to identify cognitive NeuroRehabilitation Range in Traumatic Brain Injury patients
dc.typeArticle
dc.subject.lemacAnàlisi multivariable
dc.contributor.groupUniversitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic
dc.identifier.doi10.1016/j.eswa.2014.03.001
dc.description.peerreviewedPeer Reviewed
dc.subject.ams62H Multivariate analysis
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0957417414001249
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac14578323
dc.description.versionPostprint (published version)
local.citation.authorGarcía-Rudolph, A.; Gibert, Karina
local.citation.publicationNameExpert systems with applications
local.citation.volume41
local.citation.number11
local.citation.startingPage5238
local.citation.endingPage5251


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