Disability and rehabilitation. Assistive technology
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Disabil Rehabil Assist Technol · Jul 2020
ReviewIterative processes: a review of semi-supervised machine learning in rehabilitation science.
Purpose: To define semi-supervised machine learning (SSML) and explore current and potential applications of this analytic strategy in rehabilitation research. Method: We conducted a scoping review using PubMed, GoogleScholar and Medline. Studies were included if they: (1) described a semi-supervised approach to apply machine learning algorithms during data analysis and (2) examined constructs encompassed by the International Classification of Functioning, Disability and Health (ICF). ⋯ Semi-supervised machine learning approaches uses a combination of labelled and unlabelled data to produce accurate predictive models, thereby requiring less user-input data than other machine learning approaches (i.e., supervised, unsupervised), reducing resource cost and user-burden. Semi-supervised machine learning is an iterative process that, when applied to rehabilitation assessment and outcomes, could produce accurate personalized models for treatment. Rehabilitation researchers and data scientists should collaborate to implement semi-supervised machine learning approaches in rehabilitation research, optimizing the power of large datasets that are becoming more readily available within the field (e.g., EEG signals, sensors, smarthomes).