Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/26060
Full metadata record
DC FieldValueLanguage
dc.contributor.authorHaraldsson, Saemunduren_UK
dc.contributor.authorBrynjolfsdottir, Ragnheidur Den_UK
dc.contributor.authorWoodward, Johnen_UK
dc.contributor.authorSiggeirsdottir, Kristinen_UK
dc.contributor.authorGudnason, Vilmunduren_UK
dc.date.accessioned2017-12-23T00:08:22Z-
dc.date.available2017-12-23T00:08:22Z-
dc.date.issued2017-09-04en_UK
dc.identifier.urihttp://hdl.handle.net/1893/26060-
dc.description.abstractWith the expanding load on healthcare and consequent strain on budget, the demand for tools to increase efficiency in treatments is rising. The use of prediction models throughout the treatment to identify risk factors might be a solution. In this paper we present a novel implementation of a prediction tool and the first use of a dynamic predictor in vocational rehabilitation practice. The tool is periodically updated and improved with Genetic Improvement of software. The predictor has been in use for 10 months and is evaluated on predictions made during that time by comparing them with actual treatment outcome. The results show that the predictions have been consistently accurate throughout the patients' treatment. After approximately 3 week learning phase, the predictor classified patients with 100% accuracy and precision on previously unseen data. The predictor is currently being successfully used in a complex live system where specialists have used it to make informed decisions.en_UK
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.relationHaraldsson S, Brynjolfsdottir RD, Woodward J, Siggeirsdottir K & Gudnason V (2017) The use of predictive models in dynamic treatment planning. In: 2017 IEEE Symposium on Computers and Communications (ISCC). IEEE Symposium on Computers and Communications (ISCC 2017), Heraklion, Greece, 03.07.2017-06.07.2017. Piscataway, NJ, USA: IEEE, pp. 242-247. https://doi.org/10.1109/ISCC.2017.8024536en_UK
dc.rights© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_UK
dc.subjectPrediction Modelsen_UK
dc.subjectHealthcareen_UK
dc.subjectDynamic Planingen_UK
dc.subjectMachine Learningen_UK
dc.subjectVocational Rehabilitationen_UK
dc.subjectGenetic Improvement of Softwareen_UK
dc.titleThe use of predictive models in dynamic treatment planningen_UK
dc.typeConference Paperen_UK
dc.identifier.doi10.1109/ISCC.2017.8024536en_UK
dc.citation.spage242en_UK
dc.citation.epage247en_UK
dc.citation.publicationstatusPublisheden_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.contributor.funderEngineering and Physical Sciences Research Councilen_UK
dc.citation.btitle2017 IEEE Symposium on Computers and Communications (ISCC)en_UK
dc.citation.conferencedates2017-07-03 - 2017-07-06en_UK
dc.citation.conferencelocationHeraklion, Greeceen_UK
dc.citation.conferencenameIEEE Symposium on Computers and Communications (ISCC 2017)en_UK
dc.citation.date04/09/2017en_UK
dc.citation.isbn978-1-5386-1630-7en_UK
dc.citation.isbn978-1-5386-1629-1en_UK
dc.publisher.addressPiscataway, NJ, USAen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationJanus Rehabilitationen_UK
dc.contributor.affiliationComputing Science and Mathematics - Divisionen_UK
dc.contributor.affiliationJanus Rehabilitationen_UK
dc.contributor.affiliationJanus Rehabilitationen_UK
dc.identifier.scopusid2-s2.0-85030561482en_UK
dc.identifier.wtid515597en_UK
dc.contributor.orcid0000-0003-0395-5884en_UK
dc.contributor.orcid0000-0002-2093-8990en_UK
dc.date.accepted2017-02-24en_UK
dcterms.dateAccepted2017-02-24en_UK
dc.date.filedepositdate2017-10-30en_UK
dc.relation.funderprojectDAASE: Dynamic Adaptive Automated Software Engineeringen_UK
dc.relation.funderrefEP/J017515/1en_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeConference Paper/Proceeding/Abstracten_UK
rioxxterms.versionAMen_UK
local.rioxx.authorHaraldsson, Saemundur|0000-0003-0395-5884en_UK
local.rioxx.authorBrynjolfsdottir, Ragnheidur D|en_UK
local.rioxx.authorWoodward, John|0000-0002-2093-8990en_UK
local.rioxx.authorSiggeirsdottir, Kristin|en_UK
local.rioxx.authorGudnason, Vilmundur|en_UK
local.rioxx.projectEP/J017515/1|Engineering and Physical Sciences Research Council|http://dx.doi.org/10.13039/501100000266en_UK
local.rioxx.freetoreaddate2017-10-30en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/all-rights-reserved|2017-10-30|en_UK
local.rioxx.filenamepredictive_models_dynamic_revision.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source978-1-5386-1629-1en_UK
Appears in Collections:Computing Science and Mathematics Journal Articles

Files in This Item:
File Description SizeFormat 
predictive_models_dynamic_revision.pdfFulltext - Accepted Version760.14 kBAdobe PDFView/Open


This item is protected by original copyright



Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved https://creativecommons.org/publicdomain/zero/1.0/

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.