FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4321446859> ?p ?o ?g. }

• W4321446859 abstract "Accelerometers allow for direct measurement of upper limb (UL) activity. Recently, multi-dimensional categories of UL performance have been formed to provide a more complete measure of UL use in daily life. Prediction of motor outcomes after stroke have tremendous clinical utility and a next step is to explore what factors might predict someone's subsequent UL performance category.To explore how different machine learning techniques can be used to understand how clinical measures and participant demographics captured early after stroke are associated with the subsequent UL performance categories.This study analyzed data from two time points from a previous cohort (n = 54). Data used was participant characteristics and clinical measures from early after stroke and a previously established category of UL performance at a later post stroke time point. Different machine learning techniques (a single decision tree, bagged trees, and random forests) were used to build predictive models with different input variables. Model performance was quantified with the explanatory power (in-sample accuracy), predictive power (out-of-bag estimate of error), and variable importance.A total of seven models were built, including one single decision tree, three bagged trees, and three random forests. Measures of UL impairment and capacity were the most important predictors of the subsequent UL performance category, regardless of the machine learning algorithm used. Other non-motor clinical measures emerged as key predictors, while participant demographics predictors (with the exception of age) were generally less important across the models. Models built with the bagging algorithms outperformed the single decision tree for in-sample accuracy (26-30% better classification) but had only modest cross-validation accuracy (48-55% out of bag classification).UL clinical measures were the most important predictors of the subsequent UL performance category in this exploratory analysis regardless of the machine learning algorithm used. Interestingly, cognitive and affective measures emerged as important predictors when the number of input variables was expanded. These results reinforce that UL performance, in vivo, is not a simple product of body functions nor the capacity for movement, instead being a complex phenomenon dependent on many physiological and psychological factors. Utilizing machine learning, this exploratory analysis is a productive step toward the prediction of UL performance. Trial registration NA." @default.
• W4321446859 created "2023-02-22" @default.
• W4321446859 creator A5029560239 @default.
• W4321446859 creator A5066229467 @default.
• W4321446859 creator A5072815353 @default.
• W4321446859 creator A5090783545 @default.
• W4321446859 date "2023-02-21" @default.
• W4321446859 modified "2023-10-14" @default.
• W4321446859 title "Predicting later categories of upper limb activity from earlier clinical assessments following stroke: an exploratory analysis" @default.
• W4321446859 cites W1963546294 @default.
• W4321446859 cites W1983648848 @default.
• W4321446859 cites W1989818488 @default.
• W4321446859 cites W1999586653 @default.
• W4321446859 cites W2015627186 @default.
• W4321446859 cites W2046626275 @default.
• W4321446859 cites W2051411536 @default.
• W4321446859 cites W2056257562 @default.
• W4321446859 cites W2060836531 @default.
• W4321446859 cites W2061339782 @default.
• W4321446859 cites W2074172762 @default.
• W4321446859 cites W2079124738 @default.
• W4321446859 cites W2085043781 @default.
• W4321446859 cites W2088315154 @default.
• W4321446859 cites W2089147159 @default.
• W4321446859 cites W2092343939 @default.
• W4321446859 cites W2099286920 @default.
• W4321446859 cites W2106875358 @default.
• W4321446859 cites W2111195519 @default.
• W4321446859 cites W2112188224 @default.
• W4321446859 cites W2115949595 @default.
• W4321446859 cites W2140632361 @default.
• W4321446859 cites W2141846678 @default.
• W4321446859 cites W2144588257 @default.
• W4321446859 cites W2162567509 @default.
• W4321446859 cites W2165758561 @default.
• W4321446859 cites W2169506005 @default.
• W4321446859 cites W2189831690 @default.
• W4321446859 cites W2239486811 @default.
• W4321446859 cites W2338271042 @default.
• W4321446859 cites W2344439428 @default.
• W4321446859 cites W2487770199 @default.
• W4321446859 cites W2511464381 @default.
• W4321446859 cites W2605945377 @default.
• W4321446859 cites W2735686146 @default.
• W4321446859 cites W2766416772 @default.
• W4321446859 cites W2810551427 @default.
• W4321446859 cites W2811214029 @default.
• W4321446859 cites W2884721133 @default.
• W4321446859 cites W2911964244 @default.
• W4321446859 cites W2914365144 @default.
• W4321446859 cites W2953750796 @default.
• W4321446859 cites W2969222507 @default.
• W4321446859 cites W2969655456 @default.
• W4321446859 cites W3003712552 @default.
• W4321446859 cites W3011124015 @default.
• W4321446859 cites W3031425449 @default.
• W4321446859 cites W3037827818 @default.
• W4321446859 cites W3041961736 @default.
• W4321446859 cites W3044242131 @default.
• W4321446859 cites W3048600264 @default.
• W4321446859 cites W3094049186 @default.
• W4321446859 cites W3100679196 @default.
• W4321446859 cites W3142004501 @default.
• W4321446859 cites W3153014166 @default.
• W4321446859 cites W3186491522 @default.
• W4321446859 cites W3193284772 @default.
• W4321446859 cites W3197367317 @default.
• W4321446859 cites W3199981535 @default.
• W4321446859 cites W3204891220 @default.
• W4321446859 cites W3206382133 @default.
• W4321446859 cites W3206817444 @default.
• W4321446859 cites W4242883546 @default.
• W4321446859 cites W4253713129 @default.
• W4321446859 cites W4283030265 @default.
• W4321446859 doi "https://doi.org/10.1186/s12984-023-01148-1" @default.
• W4321446859 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36810072" @default.
• W4321446859 hasPublicationYear "2023" @default.
• W4321446859 type Work @default.
• W4321446859 citedByCount "0" @default.
• W4321446859 crossrefType "journal-article" @default.
• W4321446859 hasAuthorship W4321446859A5029560239 @default.
• W4321446859 hasAuthorship W4321446859A5066229467 @default.
• W4321446859 hasAuthorship W4321446859A5072815353 @default.
• W4321446859 hasAuthorship W4321446859A5090783545 @default.
• W4321446859 hasBestOaLocation W43214468591 @default.
• W4321446859 hasConcept C105795698 @default.
• W4321446859 hasConcept C111472728 @default.
• W4321446859 hasConcept C119857082 @default.
• W4321446859 hasConcept C127413603 @default.
• W4321446859 hasConcept C138885662 @default.
• W4321446859 hasConcept C144024400 @default.
• W4321446859 hasConcept C149923435 @default.
• W4321446859 hasConcept C154945302 @default.
• W4321446859 hasConcept C169258074 @default.
• W4321446859 hasConcept C2778136018 @default.
• W4321446859 hasConcept C2780084366 @default.
• W4321446859 hasConcept C2780645631 @default.
• W4321446859 hasConcept C33923547 @default.
• W4321446859 hasConcept C41008148 @default.
• W4321446859 hasConcept C45804977 @default.

• next