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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...

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Autores principales: Campagnini, Silvia, Liuzzi, Piergiuseppe, Mannini, Andrea, Basagni, Benedetta, Macchi, Claudio, Carrozza, Maria Chiara, Cecchi, Francesca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9454118/
https://www.ncbi.nlm.nih.gov/pubmed/36071452
http://dx.doi.org/10.1186/s12984-022-01075-7
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author Campagnini, Silvia
Liuzzi, Piergiuseppe
Mannini, Andrea
Basagni, Benedetta
Macchi, Claudio
Carrozza, Maria Chiara
Cecchi, Francesca
author_facet Campagnini, Silvia
Liuzzi, Piergiuseppe
Mannini, Andrea
Basagni, Benedetta
Macchi, Claudio
Carrozza, Maria Chiara
Cecchi, Francesca
author_sort Campagnini, Silvia
collection PubMed
description 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.
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spelling pubmed-94541182022-09-09 Cross-validation of predictive models for functional recovery after post-stroke rehabilitation Campagnini, Silvia Liuzzi, Piergiuseppe Mannini, Andrea Basagni, Benedetta Macchi, Claudio Carrozza, Maria Chiara Cecchi, Francesca J Neuroeng Rehabil Research 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. BioMed Central 2022-09-07 /pmc/articles/PMC9454118/ /pubmed/36071452 http://dx.doi.org/10.1186/s12984-022-01075-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Campagnini, Silvia
Liuzzi, Piergiuseppe
Mannini, Andrea
Basagni, Benedetta
Macchi, Claudio
Carrozza, Maria Chiara
Cecchi, Francesca
Cross-validation of predictive models for functional recovery after post-stroke rehabilitation
title Cross-validation of predictive models for functional recovery after post-stroke rehabilitation
title_full Cross-validation of predictive models for functional recovery after post-stroke rehabilitation
title_fullStr Cross-validation of predictive models for functional recovery after post-stroke rehabilitation
title_full_unstemmed Cross-validation of predictive models for functional recovery after post-stroke rehabilitation
title_short Cross-validation of predictive models for functional recovery after post-stroke rehabilitation
title_sort cross-validation of predictive models for functional recovery after post-stroke rehabilitation
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9454118/
https://www.ncbi.nlm.nih.gov/pubmed/36071452
http://dx.doi.org/10.1186/s12984-022-01075-7
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