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Using Machine Learning to develop a short-form measure assessing 5 functions in patients with stroke

  • Gong-Hong Lin
    Affiliations
    Master Program in Long-term Care, College of Nursing, Taipei Medical University, Taipei, Taiwan
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  • Chih-Ying Li
    Affiliations
    Department of Occupational Therapy, School of Health Professions, University of Texas Medical Branch, Galveston, Texas
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  • Ching-Fan Sheu
    Affiliations
    Institute of Education, National Cheng Kung University, Tainan, Taiwan
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  • Chien-Yu Huang
    Affiliations
    Department of Occupational Therapy, College of Medicine, I-Shou University, Kaohsiung, Taiwan
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  • Shih-Chieh Lee
    Affiliations
    School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan
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  • Author Footnotes
    # These authors contributed equally.
    Yu-Hui Huang
    Correspondence
    Corresponding authors: Yu-Hui Huang, No.110, Sec. 1, Jianguo N. Rd., South Dist., Taichung City 402367, Taiwan (R.O.C.), Tel: +886-4-24739595
    Footnotes
    # These authors contributed equally.
    Affiliations
    School of Medicine, Chung Shan Medical University, Taichung, Taiwan

    Department of Physical Medicine and Rehabilitation, Chung Shan Medical University Hospital, Taichung, Taiwan
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  • Author Footnotes
    # These authors contributed equally.
    Ching-Lin Hsieh
    Correspondence
    Corresponding authors: Ching-Lin Hsieh, Rm. 418, 4F., No. 14, Xuzhou Rd., School of Occupational Therapy, Taipei, Taiwan (R.O.C.),Tel: +886-2-33668177
    Footnotes
    # These authors contributed equally.
    Affiliations
    School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan

    Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Taipei, Taiwan

    Department of Occupational Therapy, College of Medical and Health Science, Asia University, Taichung, Taiwan
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  • Author Footnotes
    # These authors contributed equally.
Published:December 30, 2021DOI:https://doi.org/10.1016/j.apmr.2021.12.006

      Abstract

      Objective: This study aimed to develop and validate a machine learning based short measure (the ML-5F) to assess 5 functions (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: A total of 307 patients.
      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.

      Keywords

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