NDLI: Predicting patient-reported outcome of activities of daily living in stroke rehabilitation: a machine learning study

Content Provider	Springer Nature : BioMed Central
Author	Chen, Yu-Wen Lin, Keh-chung Li, Yi-chun Lin, Chia-Jung
Abstract	Background Machine Learning is increasingly used to predict rehabilitation outcomes in stroke in the context of precision rehabilitation and patient-centered care. However, predictors for patient-centered outcome measures for activities and participation in stroke rehabilitation requires further investigation. Methods This study retrospectively analyzed data collected for our previous studies from 124 participants. Machine Learning models were built to predict postintervention improvement of patient-reported outcome measures of daily activities (i.e, the Motor Activity Log and the Nottingham Extended Activities of Daily Living) and participation (i.e, the Activities of Daily Living domain of the Stroke Impact Scale). Three groups of 18 potential predictors were included: patient demographics, stroke characteristics, and baseline assessment scores that encompass all three domains under the framework of International Classification of Functioning, Disability and Health. For each target variable, classification models were built with four algorithms, logistic regression, k-nearest neighbors, support vector machine, and random forest, and with all 18 potential predictors and the most important predictors identified by feature selection. Results Predictors for the four target variables partially overlapped. For all target variables, their own baseline scores were among the most important predictors. Upper-limb motor function and selected demographic and stroke characteristics were also among the important predictors across the target variables. For the four target variables, prediction accuracies of the best-performing models with 18 features ranged between 0.72 and 0.96. Those of the best-performing models with fewer features ranged between 0.72 and 0.84. Conclusions Our findings support the feasibility of using Machine Learning for the prediction of stroke rehabilitation outcomes. The study was the first to use Machine Learning to identify important predictors for postintervention improvement on four patient-reported outcome measures of activities and participation in chronic stroke. The study contributes to precision rehabilitation and patient-centered care, and the findings may provide insights into the identification of patients that are likely to benefit from stroke rehabilitation.
Related Links	https://jneuroengrehab.biomedcentral.com/counter/pdf/10.1186/s12984-023-01151-6.pdf
Ending Page	12
Page Count	12
Starting Page	1
File Format	HTM / HTML
ISSN	17430003
DOI	10.1186/s12984-023-01151-6
Journal	Journal of NeuroEngineering and Rehabilitation
Issue Number	1
Volume Number	20
Language	English
Publisher	BioMed Central
Publisher Date	2023-02-23
Access Restriction	Open
Subject Keyword	Neurosciences Neurology Rehabilitation Medicine Biomedical Engineering and Bioengineering Stroke rehabilitation Prognosis Prediction Patient-reported outcome measures Activities of daily living Machine learning
Content Type	Text
Resource Type	Article
Subject	Health Informatics Rehabilitation
Journal Impact Factor	5.2/2023
5-Year Journal Impact Factor	5.6/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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