ArticleArchives of physical medicine and rehabilitation2022
Using Machine Learning to Develop a Short-Form Measure Assessing 5 Functions in Patients With Stroke.
Article in Archives of physical medicine and rehabilitation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The trial behind it
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Who cites it
4 citing papers in PubMed.
- Scale abbreviation with supervised machine learning: A comparison of feature selection techniques.Behavior research methods · 2026Article
- Relevance and use of health policy, health systems and health services research for strengthening rehabilitation in real-life settings: methodological considerations.European journal of physical and rehabilitation medicine · 2024Article
- Developing a Short-Form Buss-Warren Aggression Questionnaire Based on Machine Learning.Behavioral sciences (Basel, Switzerland) · 2023Article
- Wearable Intelligent Machine Learning Rehabilitation Assessment for Stroke Patients Compared with Clinician Assessment.Journal of clinical medicine · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
objectiveThis 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.
designSecondary 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.
settingA rehabilitation unit in a medical center.
participantsPatients (N=307) with stroke.
interventionsNot applicable.
main outcome measuresThe BI, PASS, and STREAM.
resultsA 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).
conclusionsThe 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.
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Registered trials
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