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Adopting Text Mining on Rehabilitation Therapy Repositioning for Stroke

Stroke is a common disabling disease that severely affects the daily life of patients. Accumulating evidence indicates that rehabilitation therapy can improve movement function. However, no clear guidelines have specific and effective rehabilitation therapy schemes, and the development of new rehabi...

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Autores principales: Meng, Guilin, Huang, Yong, Yu, Qi, Ding, Ying, Wild, David, Zhao, Yanxin, Liu, Xueyuan, Song, Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6433708/
https://www.ncbi.nlm.nih.gov/pubmed/30941028
http://dx.doi.org/10.3389/fninf.2019.00017
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author Meng, Guilin
Huang, Yong
Yu, Qi
Ding, Ying
Wild, David
Zhao, Yanxin
Liu, Xueyuan
Song, Min
author_facet Meng, Guilin
Huang, Yong
Yu, Qi
Ding, Ying
Wild, David
Zhao, Yanxin
Liu, Xueyuan
Song, Min
author_sort Meng, Guilin
collection PubMed
description Stroke is a common disabling disease that severely affects the daily life of patients. Accumulating evidence indicates that rehabilitation therapy can improve movement function. However, no clear guidelines have specific and effective rehabilitation therapy schemes, and the development of new rehabilitation techniques has been relatively slow. This study used a text mining approach, the ABC model, to identify an existing rehabilitation candidate therapy method that is most likely to be repositioned for stroke. In the model, we built the internal links of stroke (A), assessment scales (B), and rehabilitation therapies (C) in PubMed and the links were related to upper limb function measurements for patients with stroke. In the first step, using E-utility, we retrieved both stroke-related assessment scales and rehabilitation therapy records and then compiled two datasets, which were called Stroke_Scales and Stroke_Therapies, respectively. In the next step, we crawled all rehabilitation therapies co-occurring with the Stroke_Therapies and then named them as All_Therapies. Therapies that were already included in Stroke_Therapies were deleted from All_Therapies; therefore, the remaining therapies were the potential rehabilitation therapies, which could be repositioned for stroke after subsequent filtration by a manual check. We identified the top-ranked repositioning rehabilitation therapy and subsequently examined its clinical validation. Hand-arm bimanual intensive training (HABIT) was ranked the first in our repositioning rehabilitation therapies and had the most interaction links with Stroke_Scales. HABIT significantly improved clinical scores on assessment scales [Fugl-Meyer Assessment (FMA) and action research arm test (ARAT)] in the clinical validation study for acute stroke patients with upper limb dysfunction. Therefore, based on the ABC model and clinical validation, HABIT is a promising repositioned rehabilitation therapy for stroke, and the ABC model is an effective text mining approach for rehabilitation therapy repositioning. The findings in this study would be helpful in clinical knowledge discovery.
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spelling pubmed-64337082019-04-02 Adopting Text Mining on Rehabilitation Therapy Repositioning for Stroke Meng, Guilin Huang, Yong Yu, Qi Ding, Ying Wild, David Zhao, Yanxin Liu, Xueyuan Song, Min Front Neuroinform Neuroscience Stroke is a common disabling disease that severely affects the daily life of patients. Accumulating evidence indicates that rehabilitation therapy can improve movement function. However, no clear guidelines have specific and effective rehabilitation therapy schemes, and the development of new rehabilitation techniques has been relatively slow. This study used a text mining approach, the ABC model, to identify an existing rehabilitation candidate therapy method that is most likely to be repositioned for stroke. In the model, we built the internal links of stroke (A), assessment scales (B), and rehabilitation therapies (C) in PubMed and the links were related to upper limb function measurements for patients with stroke. In the first step, using E-utility, we retrieved both stroke-related assessment scales and rehabilitation therapy records and then compiled two datasets, which were called Stroke_Scales and Stroke_Therapies, respectively. In the next step, we crawled all rehabilitation therapies co-occurring with the Stroke_Therapies and then named them as All_Therapies. Therapies that were already included in Stroke_Therapies were deleted from All_Therapies; therefore, the remaining therapies were the potential rehabilitation therapies, which could be repositioned for stroke after subsequent filtration by a manual check. We identified the top-ranked repositioning rehabilitation therapy and subsequently examined its clinical validation. Hand-arm bimanual intensive training (HABIT) was ranked the first in our repositioning rehabilitation therapies and had the most interaction links with Stroke_Scales. HABIT significantly improved clinical scores on assessment scales [Fugl-Meyer Assessment (FMA) and action research arm test (ARAT)] in the clinical validation study for acute stroke patients with upper limb dysfunction. Therefore, based on the ABC model and clinical validation, HABIT is a promising repositioned rehabilitation therapy for stroke, and the ABC model is an effective text mining approach for rehabilitation therapy repositioning. The findings in this study would be helpful in clinical knowledge discovery. Frontiers Media S.A. 2019-03-19 /pmc/articles/PMC6433708/ /pubmed/30941028 http://dx.doi.org/10.3389/fninf.2019.00017 Text en Copyright © 2019 Meng, Huang, Yu, Ding, Wild, Zhao, Liu and Song. http://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 Neuroscience
Meng, Guilin
Huang, Yong
Yu, Qi
Ding, Ying
Wild, David
Zhao, Yanxin
Liu, Xueyuan
Song, Min
Adopting Text Mining on Rehabilitation Therapy Repositioning for Stroke
title Adopting Text Mining on Rehabilitation Therapy Repositioning for Stroke
title_full Adopting Text Mining on Rehabilitation Therapy Repositioning for Stroke
title_fullStr Adopting Text Mining on Rehabilitation Therapy Repositioning for Stroke
title_full_unstemmed Adopting Text Mining on Rehabilitation Therapy Repositioning for Stroke
title_short Adopting Text Mining on Rehabilitation Therapy Repositioning for Stroke
title_sort adopting text mining on rehabilitation therapy repositioning for stroke
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6433708/
https://www.ncbi.nlm.nih.gov/pubmed/30941028
http://dx.doi.org/10.3389/fninf.2019.00017
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