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Machine learning corroborates subjective ratings of walking and balance difficulty in multiple sclerosis

Machine learning can discern meaningful information from large datasets. Applying machine learning techniques to raw sensor data from instrumented walkways could automatically detect subtle changes in walking and balance. Multiple sclerosis (MS) is a neurological disorder in which patients report va...

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Autores principales: Hu, Wenting, Combden, Owen, Jiang, Xianta, Buragadda, Syamala, Newell, Caitlin J., Williams, Maria C., Critch, Amber L., Ploughman, Michelle
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9556653/
https://www.ncbi.nlm.nih.gov/pubmed/36248625
http://dx.doi.org/10.3389/frai.2022.952312
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author Hu, Wenting
Combden, Owen
Jiang, Xianta
Buragadda, Syamala
Newell, Caitlin J.
Williams, Maria C.
Critch, Amber L.
Ploughman, Michelle
author_facet Hu, Wenting
Combden, Owen
Jiang, Xianta
Buragadda, Syamala
Newell, Caitlin J.
Williams, Maria C.
Critch, Amber L.
Ploughman, Michelle
author_sort Hu, Wenting
collection PubMed
description Machine learning can discern meaningful information from large datasets. Applying machine learning techniques to raw sensor data from instrumented walkways could automatically detect subtle changes in walking and balance. Multiple sclerosis (MS) is a neurological disorder in which patients report varying degrees of walking and balance disruption. This study aimed to determine whether machine learning applied to walkway sensor data could classify severity of self-reported symptoms in MS patients. Ambulatory people with MS (n = 107) were asked to rate the severity of their walking and balance difficulties, from 1-No problems to 5-Extreme problems, using the MS-Impact Scale-29. Those who scored less than 3 (moderately) were assigned to the “mild” group (n = 35), and those scoring higher were in the “moderate” group (n = 72). Three machine learning algorithms were applied to classify the “mild” group from the “moderate” group. The classification achieved 78% accuracy, a precision of 85%, a recall of 90%, and an F1 score of 87% for distinguishing those people reporting mild from moderate walking and balance difficulty. This study demonstrates that machine learning models can reliably be applied to instrumented walkway data and distinguish severity of self-reported impairment in people with MS.
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spelling pubmed-95566532022-10-14 Machine learning corroborates subjective ratings of walking and balance difficulty in multiple sclerosis Hu, Wenting Combden, Owen Jiang, Xianta Buragadda, Syamala Newell, Caitlin J. Williams, Maria C. Critch, Amber L. Ploughman, Michelle Front Artif Intell Artificial Intelligence Machine learning can discern meaningful information from large datasets. Applying machine learning techniques to raw sensor data from instrumented walkways could automatically detect subtle changes in walking and balance. Multiple sclerosis (MS) is a neurological disorder in which patients report varying degrees of walking and balance disruption. This study aimed to determine whether machine learning applied to walkway sensor data could classify severity of self-reported symptoms in MS patients. Ambulatory people with MS (n = 107) were asked to rate the severity of their walking and balance difficulties, from 1-No problems to 5-Extreme problems, using the MS-Impact Scale-29. Those who scored less than 3 (moderately) were assigned to the “mild” group (n = 35), and those scoring higher were in the “moderate” group (n = 72). Three machine learning algorithms were applied to classify the “mild” group from the “moderate” group. The classification achieved 78% accuracy, a precision of 85%, a recall of 90%, and an F1 score of 87% for distinguishing those people reporting mild from moderate walking and balance difficulty. This study demonstrates that machine learning models can reliably be applied to instrumented walkway data and distinguish severity of self-reported impairment in people with MS. Frontiers Media S.A. 2022-09-29 /pmc/articles/PMC9556653/ /pubmed/36248625 http://dx.doi.org/10.3389/frai.2022.952312 Text en Copyright © 2022 Hu, Combden, Jiang, Buragadda, Newell, Williams, Critch and Ploughman. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
Hu, Wenting
Combden, Owen
Jiang, Xianta
Buragadda, Syamala
Newell, Caitlin J.
Williams, Maria C.
Critch, Amber L.
Ploughman, Michelle
Machine learning corroborates subjective ratings of walking and balance difficulty in multiple sclerosis
title Machine learning corroborates subjective ratings of walking and balance difficulty in multiple sclerosis
title_full Machine learning corroborates subjective ratings of walking and balance difficulty in multiple sclerosis
title_fullStr Machine learning corroborates subjective ratings of walking and balance difficulty in multiple sclerosis
title_full_unstemmed Machine learning corroborates subjective ratings of walking and balance difficulty in multiple sclerosis
title_short Machine learning corroborates subjective ratings of walking and balance difficulty in multiple sclerosis
title_sort machine learning corroborates subjective ratings of walking and balance difficulty in multiple sclerosis
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9556653/
https://www.ncbi.nlm.nih.gov/pubmed/36248625
http://dx.doi.org/10.3389/frai.2022.952312
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