NDLI: Prediction of robotic neurorehabilitation functional ambulatory outcome in patients with neurological disorders

Content Provider	Springer Nature : BioMed Central
Author	Kuo, Chao-Yang Liu, Chia-Wei Lai, Chien-Hung Kang, Jiunn-Horng Tseng, Sung-Hui Su, Emily Chia-Yu
Abstract	Introduction Conflicting results persist regarding the effectiveness of robotic-assisted gait training (RAGT) for functional gait recovery in post-stroke survivors. We used several machine learning algorithms to construct prediction models for the functional outcomes of robotic neurorehabilitation in adult patients. Methods and materials Data of 139 patients who underwent Lokomat training at Taipei Medical University Hospital were retrospectively collected. After screening for data completeness, records of 91 adult patients with acute or chronic neurological disorders were included in this study. Patient characteristics and quantitative data from Lokomat were incorporated as features to construct prediction models to explore early responses and factors associated with patient recovery. Results Experimental results using the random forest algorithm achieved the best area under the receiver operating characteristic curve of 0.9813 with data extracted from all sessions. Body weight (BW) support played a key role in influencing the progress of functional ambulation categories. The analysis identified negative correlations of BW support, guidance force, and days required to complete 12 Lokomat sessions with the occurrence of progress, while a positive correlation was observed with regard to speed. Conclusions We developed a predictive model for ambulatory outcomes based on patient characteristics and quantitative data on impairment reduction with early-stage robotic neurorehabilitation. RAGT is a customized approach for patients with different conditions to regain walking ability. To obtain a more-precise and clearer predictive model, collecting more RAGT training parameters and analyzing them for each individual disorder is a possible approach to help clinicians achieve a better understanding of the most efficient RAGT parameters for different patients. Trial registration: Retrospectively registered.
Related Links	https://jneuroengrehab.biomedcentral.com/counter/pdf/10.1186/s12984-021-00965-6.pdf
Ending Page	12
Page Count	12
Starting Page	1
File Format	HTM / HTML
ISSN	17430003
DOI	10.1186/s12984-021-00965-6
Journal	Journal of NeuroEngineering and Rehabilitation
Issue Number	1
Volume Number	18
Language	English
Publisher	BioMed Central
Publisher Date	2021-12-18
Access Restriction	Open
Subject Keyword	Neurosciences Neurology Rehabilitation Medicine Biomedical Engineering and Bioengineering Robotic neurorehabilitation Machine learning Lokomat Functional ambulatory outcome Random forest
Content Type	Text
Resource Type	Article
Subject	Health Informatics Rehabilitation
Journal Impact Factor	5.2/2023
5-Year Journal Impact Factor	5.6/2023

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