Login
Communauté Vinci
Extérieur
Si votre nom d'utilisateur ne se termine pas par @vinci.be ou @student.vinci.be, utilisez le formulaire ci-dessous pour accéder à votre compte de lecteur.
Titre : | Using Machine Learning to Develop a Short-Form Measure Assessing 5 Functions in Patients With Stroke (2022) |
Auteurs : | Gong-Hong Lin ; Chih-Ying Li ; Ching-Fan Sheu ; Chien-Yu Huang ; Shih-Chieh Lee ; Yu-Hui Huang ; Ching-Lin Hsieh |
Type de document : | Article |
Dans : | Archives of Physical Medicine and Rehabilitation (Vol. 103, n° 8, 2022) |
Article en page(s) : | p. 1574-1581 |
Note générale : | https://doi.org/10.1016/j.apmr.2021.12.006 |
Langues: | Anglais |
Descripteurs : |
HE Vinci Accident vasculaire cérébral (AVC) ; Activités de la vie quotidienne ; Apprentissage machine ; Équilibre postural ; Réadaptation |
Résumé : |
Objective
This study aimed to develop and validate a machine learning-based short measure to assess 5 functions (the ML-5F) (activities of daily living [ADL], balance, upper extremity [UE] and lower extremity [LE] motor function, and mobility) in patients with stroke. Design Secondary data from a previous study. A follow-up study assessed patients with stroke using the Barthel Index (BI), Postural Assessment Scale for Stroke (PASS), and Stroke Rehabilitation Assessment of Movement (STREAM) at hospital admission and discharge. Setting A rehabilitation unit in a medical center. Participants Patients (N=307) with stroke. Interventions Not applicable. Main Outcome Measures The BI, PASS, and STREAM. Results A machine learning algorithm, Extreme Gradient Boosting, was used to select 15 items from the BI, PASS, and STREAM, and transformed the raw scores of the selected items into the scores of the ML-5F. The ML-5F demonstrated good concurrent validity (Pearson's r, 0.88-0.98) and responsiveness (standardized response mean, 0.28-1.01). Conclusions The ML-5F comprises only 15 items but demonstrates sufficient concurrent validity and responsiveness to assess ADL, balance, UE and LE functions, and mobility in patients with stroke. The ML-5F shows great potential as an efficient outcome measure in clinical settings. |
Disponible en ligne : | Oui |
En ligne : | https://login.ezproxy.vinci.be/login?url=https://www.sciencedirect.com/science/article/pii/S000399932101769X#! |