Adopting text mining on rehabilitation therapy repositioning for stroke

Guilin Meng, Yong Huang, Qi Yu, Ying Ding, David Wild, Yanxin Zhao, Xueyuan Liu, Min Song

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Article number17
JournalFrontiers in Neuroinformatics
Volume13
DOIs
StatePublished - Feb 7 2019

Fingerprint

Data Mining
Patient rehabilitation
Rehabilitation
Stroke
Therapeutics
Arm
Hand
Upper Extremity
Data mining
Validation Studies
Health Services Research

Keywords

  • ABC model
  • Hand-arm bimanual intensive training
  • Stroke
  • Text mining
  • Upper extremity

ASJC Scopus subject areas

  • Neuroscience (miscellaneous)
  • Biomedical Engineering
  • Computer Science Applications

Cite this

Meng, G., Huang, Y., Yu, Q., Ding, Y., Wild, D., Zhao, Y., ... Song, M. (2019). Adopting text mining on rehabilitation therapy repositioning for stroke. Frontiers in Neuroinformatics, 13, [17]. https://doi.org/10.3389/fninf.2019.00017

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.

In: Frontiers in Neuroinformatics, Vol. 13, 17, 07.02.2019.

Research output: Contribution to journalArticle

Meng, G, Huang, Y, Yu, Q, Ding, Y, Wild, D, Zhao, Y, Liu, X & Song, M 2019, 'Adopting text mining on rehabilitation therapy repositioning for stroke', Frontiers in Neuroinformatics, vol. 13, 17. https://doi.org/10.3389/fninf.2019.00017
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. In: Frontiers in Neuroinformatics. 2019 ; Vol. 13.
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