首页|Arctic University of Norway (UiT) Reports Findings in Machine Learning (A machine learning approach to predict post-stroke fatigue. The Nor-COAST study)
Arctic University of Norway (UiT) Reports Findings in Machine Learning (A machine learning approach to predict post-stroke fatigue. The Nor-COAST study)
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New research on Machine Learning is the subject of a report. According to news reporting originating from Tromso, Norway, by NewsRx correspondents, research stated, “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. A prospective multicenter cohort-study with 18-month follow-up.” Our news editors obtained a quote from the research from the Arctic University of Norway (UiT), “Outpatient clinics at 3 university hospitals and 2 local hospitals. 474 participants with the diagnosis of acute stroke (mean (SD) age; 70.5 (11.3), 59% male). Not applicable. 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. 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).”
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