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dc.contributor.authorLuzum, Geske
dc.contributor.authorThrane, Gyrd
dc.contributor.authorAam, Stina
dc.contributor.authorEldholm, Rannveig Sakshaug
dc.contributor.authorGrambaite, Ramune
dc.contributor.authorMunthe-Kaas, Ragnhild
dc.contributor.authorThingstad, Anne Pernille Mæhle
dc.contributor.authorSaltvedt, Ingvild
dc.contributor.authorAskim, Torunn
dc.date.accessioned2024-06-12T13:31:39Z
dc.date.available2024-06-12T13:31:39Z
dc.date.issued2024-01-17
dc.description.abstractObjective - This study aimed to predict fatigue 18 months post-stroke by utilizing comprehensive data from the acute and sub-acute phases after stroke in a machine-learning set-up.<p> <p>Design - A prospective multicenter cohort-study with 18-month follow-up.<p> <p>Setting - Outpatient clinics at 3 university hospitals and 2 local hospitals.<p> <p>Participants - 474 participants with the diagnosis of acute stroke (mean ± SD age; 70.5 (11.3), 59% male; N=474).<p> <p>Interventions - Not applicable.<p> <p>Main Outcome Measures - The primary outcome, fatigue at 18 months, was assessed using the Fatigue Severity Scale (FSS-7). FSS-7≥5 was defined as fatigue. In total, 45 prediction variables were collected, at initial hospital-stay and 3-month post-stroke.<p> <p>Results - The best performing model, random forest, predicted 69% of all subjects with fatigue correctly with a sensitivity of 0.69 (95% CI: 0.50, 0.86), a specificity of 0.74 (95% CI: 0.66, 0.83), and an Area under the Receiver Operator Characteristic curve of 0.79 (95% CI: 0.69, 0.87) in new unseen data. The proportion of subjects predicted to suffer from fatigue, who truly suffered from fatigue at 18-months was estimated to 0.41 (95% CI: 0.26, 0.57). The proportion of subjects predicted to be free from fatigue who truly did not have fatigue at 18-months was estimated to 0.90 (95% CI: 0.83, 0.96).<p> <p>Conclusions - Our findings indicate that the model has satisfactory ability to predict fatigue in the chronic phase post-stroke and may be applicable in clinical settings.en_US
dc.identifier.citationLuzum, Thrane, Aam, Eldholm, Grambaite, Munthe-Kaas, Thingstad, Saltvedt, Askim. A Machine Learning Approach to Predict Post-stroke Fatigue. The Nor-COAST study. Archives of Physical Medicine and Rehabilitation. 2014;105(5):921-939
dc.identifier.cristinIDFRIDAID 2229206
dc.identifier.doi10.1016/j.apmr.2023.12.005
dc.identifier.issn0003-9993
dc.identifier.issn1532-821X
dc.identifier.urihttps://hdl.handle.net/10037/33783
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.journalArchives of Physical Medicine and Rehabilitation
dc.rights.holderCopyright 2014 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleA Machine Learning Approach to Predict Post-stroke Fatigue. The Nor-COAST studyen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US


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Attribution 4.0 International (CC BY 4.0)
Med mindre det står noe annet, er denne innførselens lisens beskrevet som Attribution 4.0 International (CC BY 4.0)