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
List of abbreviations:
ADL (activities of daily living), BI (Barthel Index), LE (lower extremity), CAT-5F (computerized adaptive testing system for assessing 5 functions), ML (machine learning), ML-5F (machine learning-based short measure to assess 5 functions), PASS (Postural Assessment Scale for Stroke), SRM (standardized response mean), STREAM (Stroke Rehabilitation Assessment of Movement), UE (upper extremity)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: December 30, 2021
Accepted:
December 10,
2021
Received in revised form:
October 26,
2021
Received:
July 22,
2020
Footnotes
This study was supported by the Ministry of Science and Technology in Taiwan (MOST106-2314-B-002-252-MY3, 109-2314-B-038 -147, and 110-2636-B-214-001), Chung Shan Medical University Hospital (CSH-2018-C-025), and the National Center for Medical Rehabilitation Research, National Institute of Child Health and Human Development, National Institutes of Health (K01HD101589).
Disclosures: none.
Identification
Copyright
© 2021 by the American Congress of Rehabilitation Medicine.
