Using Machine Learning to Develop a Short-Form Measure Assessing 5 Functions in Patients With Stroke

Gong-Hong Lin; Chih-Ying Li; Ching-Fan Sheu; Chien-Yu Huang; Shih-Chieh Lee; Yu-Hui Huang; Ching-Lin Hsieh

doi:10.1016/j.apmr.2021.12.006

Using Machine Learning to Develop a Short-Form Measure Assessing 5 Functions in Patients With Stroke

Arch Phys Med Rehabil. 2022 Aug;103(8):1574-1581. doi: 10.1016/j.apmr.2021.12.006. Epub 2021 Dec 31.

Authors

Gong-Hong Lin¹, Chih-Ying Li², Ching-Fan Sheu³, Chien-Yu Huang⁴, Shih-Chieh Lee⁵, Yu-Hui Huang⁶, Ching-Lin Hsieh⁷

Affiliations

¹ Master Program in Long-Term Care, College of Nursing, Taipei Medical University, Taipei, Taiwan.
² Department of Occupational Therapy, School of Health Professions, University of Texas Medical Branch, Galveston, TX.
³ Institute of Education, National Cheng Kung University, Tainan, Taiwan.
⁴ Department of Occupational Therapy, College of Medicine, I-Shou University, Kaohsiung, Taiwan; Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Taipei, Taiwan; Department of Occupational Therapy, College of Medicine, I-Shou University, Kaohsiung, Taiwan.
⁵ School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Occupational Therapy, College of Medicine, National Cheng Kung University, Tainan City, Taiwan; Institute of Long-Term Care, MacKay Medical College, New Taipei City, Taiwan.
⁶ School of Medicine, Chung Shan Medical University, Taichung, Taiwan; Department of Physical Medicine and Rehabilitation, Chung Shan Medical University Hospital, Taichung, Taiwan.
⁷ School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan; School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Occupational Therapy, College of Medical and Health Science, Asia University, Taichung, Taiwan. Electronic address: clhsieh@ntu.edu.tw.

Abstract

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.

Keywords: Activities of daily living; Machine learning; Postural balance; Rehabilitation; Stroke.

MeSH terms

Activities of Daily Living*
Follow-Up Studies
Humans
Machine Learning*
Reproducibility of Results
Stroke Rehabilitation
Stroke* / physiopathology

Grants and funding

K01 HD101589/HD/NICHD NIH HHS/United States