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. 2021 Dec 18;18(1):174.
doi: 10.1186/s12984-021-00965-6.

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

Chao-Yang Kuo #  1 , Chia-Wei Liu #  2 , Chien-Hung Lai  2   3 , Jiunn-Horng Kang  2   3 , Sung-Hui Tseng  4   5 , Emily Chia-Yu Su  6   7   8
Affiliations

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

Chao-Yang Kuo et al. J Neuroeng Rehabil. .

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.

Keywords: Functional ambulatory outcome; Lokomat; Machine learning; Random forest; Robotic neurorehabilitation.

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Conflict of interest statement

We have no competing interests to declare.

Figures

Fig. 1
Fig. 1
Analytic flowchart to develop models for predicting the effectiveness of robot-assisted gait training (RAGT) for patients with neurological disorders
Fig. 2
Fig. 2
Variable ranking based on the discriminative power to predict the effectiveness of robot-assisted gait training (RAGT) for functional gait recovery
Fig. 3
Fig. 3
Partial dependence plots of important variables
See this image and copyright information in PMC

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