Cross-validation of predictive models for functional recovery after post-stroke rehabilitation.
Background: Rehabilitation treatments and services are essential for the recovery of post-stroke patients' functions; however, the increasing number of available therapies and the lack of consensus among outcome measures compromises the possibility to determine an appropriate level of evidence. Mach...
| Publicado en: | Journal of NeuroEngineering & Rehabilitation (JNER) Vol. 19; no. 1; pp. 1 - 12 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
9/7/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158960118&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158960118 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17430003 1CUC jtl: Journal of NeuroEngineering & Rehabilitation (JNER) issn: 17430003 maglogo: N pubinfo: dt: 9/7/2022 vid: 19 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 158960118 158960118 NLM36071452 158960118 10.1186/s12984-022-01075-7 NLM36071452 158960118 ppf: 1 ppct: 11 formats: tig: atl: Cross-validation of predictive models for functional recovery after post-stroke rehabilitation. aug: au: Campagnini, Silvia Liuzzi, Piergiuseppe Mannini, Andrea Basagni, Benedetta Macchi, Claudio Carrozza, Maria Chiara Cecchi, Francesca affil: The Biorobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, 56025, Pontedera, Italy sug: subj: Stroke Recovery Scales Barthel Index ab: Background: Rehabilitation treatments and services are essential for the recovery of post-stroke patients' functions; however, the increasing number of available therapies and the lack of consensus among outcome measures compromises the possibility to determine an appropriate level of evidence. Machine learning techniques for prognostic applications offer accurate and interpretable predictions, supporting the clinical decision for personalised treatment. The aim of this study is to develop and cross-validate predictive models for the functional prognosis of patients, highlighting the contributions of each predictor.Methods: A dataset of 278 post-stroke patients was used for the prediction of the class transition, obtained from the modified Barthel Index. Four classification algorithms were cross-validated and compared. On the best performing model on the validation set, an analysis of predictors contribution was conducted.Results: The Random Forest obtained the best overall results on the accuracy (76.2%), balanced accuracy (74.3%), sensitivity (0.80), and specificity (0.68). The combination of all the classification results on the test set, by weighted voting, reached 80.2% accuracy. The predictors analysis applied on the Support Vector Machine, showed that a good trunk control and communication level, and the absence of bedsores retain the major contribution in the prediction of a good functional outcome.Conclusions: Despite a more comprehensive assessment of the patients is needed, this work paves the way for the implementation of solutions for clinical decision support in the rehabilitation of post-stroke patients. Indeed, offering good prognostic accuracies for class transition and patient-wise view of the predictors contributions, it might help in a personalised optimisation of the patients' rehabilitation path. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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