Artificial intelligence techniques to support cognitive rehabilitation

In recent years, the Guttmann Institute has incorporated an intelligent assistant as a predicted and personalized decision support system (PPDSS). This PPDSS helps plan rehabilitation sessions for patients suffering from acquired brain injury (ABI). Results show questionable planning when comparing...

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Detalles Bibliográficos
Autor: Hoeksma, Sara
Tipo de recurso: tesis de maestría
Fecha de publicación:2018
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/197546
Acceso en línea:http://hdl.handle.net/10261/197546
Access Level:acceso abierto
Palabra clave:Machine learning
Acquired brain injury
Decision support
Classifiers
Cognitive rehabilitation
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spelling Artificial intelligence techniques to support cognitive rehabilitationHoeksma, SaraMachine learningAcquired brain injuryDecision supportClassifiersCognitive rehabilitationIn recent years, the Guttmann Institute has incorporated an intelligent assistant as a predicted and personalized decision support system (PPDSS). This PPDSS helps plan rehabilitation sessions for patients suffering from acquired brain injury (ABI). Results show questionable planning when comparing patient profiles and their assigned tasks. The distribution of percentage of effort does not perfectly match the distribution of the cognitive profile. This paper provides a thorough analysis of the patient profiles, showing that a patient’s initial profile and the task execution scores during their first few sessions can be used to better predict their final improvement, to a certain degree of accuracy. Furthermore, results show that more executions of tasks does not automatically lead to improvement. Practice does not seem to make perfect. The proposed technique involves the incorporation of task-weights in the new scheduler.Peer reviewedUniversidad de BarcelonaCSIC - Instituto de Investigación en Inteligencia Artificial (IIIA)Arcos Rosell, Josep LluísCerquides, JesúsConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202020202018info:eu-repo/semantics/masterThesishttp://purl.org/coar/resource_type/c_bdcchttp://hdl.handle.net/10261/197546reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttp://hdl.handle.net/2117/121646Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1975462026-05-22T06:33:51Z
dc.title.none.fl_str_mv Artificial intelligence techniques to support cognitive rehabilitation
title Artificial intelligence techniques to support cognitive rehabilitation
spellingShingle Artificial intelligence techniques to support cognitive rehabilitation
Hoeksma, Sara
Machine learning
Acquired brain injury
Decision support
Classifiers
Cognitive rehabilitation
title_short Artificial intelligence techniques to support cognitive rehabilitation
title_full Artificial intelligence techniques to support cognitive rehabilitation
title_fullStr Artificial intelligence techniques to support cognitive rehabilitation
title_full_unstemmed Artificial intelligence techniques to support cognitive rehabilitation
title_sort Artificial intelligence techniques to support cognitive rehabilitation
dc.creator.none.fl_str_mv Hoeksma, Sara
author Hoeksma, Sara
author_facet Hoeksma, Sara
author_role author
dc.contributor.none.fl_str_mv Arcos Rosell, Josep Lluís
Cerquides, Jesús
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Machine learning
Acquired brain injury
Decision support
Classifiers
Cognitive rehabilitation
topic Machine learning
Acquired brain injury
Decision support
Classifiers
Cognitive rehabilitation
description In recent years, the Guttmann Institute has incorporated an intelligent assistant as a predicted and personalized decision support system (PPDSS). This PPDSS helps plan rehabilitation sessions for patients suffering from acquired brain injury (ABI). Results show questionable planning when comparing patient profiles and their assigned tasks. The distribution of percentage of effort does not perfectly match the distribution of the cognitive profile. This paper provides a thorough analysis of the patient profiles, showing that a patient’s initial profile and the task execution scores during their first few sessions can be used to better predict their final improvement, to a certain degree of accuracy. Furthermore, results show that more executions of tasks does not automatically lead to improvement. Practice does not seem to make perfect. The proposed technique involves the incorporation of task-weights in the new scheduler.
publishDate 2018
dc.date.none.fl_str_mv 2018
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/masterThesis
http://purl.org/coar/resource_type/c_bdcc
format masterThesis
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/197546
url http://hdl.handle.net/10261/197546
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://hdl.handle.net/2117/121646
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Universidad de Barcelona
CSIC - Instituto de Investigación en Inteligencia Artificial (IIIA)
publisher.none.fl_str_mv Universidad de Barcelona
CSIC - Instituto de Investigación en Inteligencia Artificial (IIIA)
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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