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KMID : 1143320220110040023
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Therapeutic Science for Neurorehabilitation
2022 Volume.11 No. 4 p.23 ~ p.39
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Predicting Functional Outcomes of Patients With Stroke Using Machine Learning: A Systematic Review
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Bae Su-Yeong
Lee Mi-Jung
Nam Sang-Hun
Hong Ick-Pyo
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Abstract
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Objective : To summarize clinical and demographic variables and machine learning uses for predicting functional outcomes of patients with stroke.
Methods : We searched PubMed, CINAHL and Web of Science to identify published articles from 2010 to 2021.
The search terms were “machine learning OR data mining AND stroke AND function OR prediction OR/AND rehabilitation”. Articles exclusively using brain imaging techniques, deep learning method and articles without available full text were excluded in this study.
Results : Nine articles were selected for this study. Support vector machines (19.05%) and random forests (19.05%) were two most frequently used machine learning models. Five articles (55.56%) demonstrated that the impact of patient initial and/or discharge assessment scores such as modified ranking scale (mRS) or functional independence measure (FIM) on stroke patients’ functional outcomes was higher than their clinical characteristics.
Conclusions : This study showed that patient initial and/or discharge assessment scores such as mRS or FIM could influence their functional outcomes more than their clinical characteristics. Evaluating and reviewing initial and or discharge functional outcomes of patients with stroke might be required to develop the optimal therapeutic interventions to enhance functional outcomes of patients with stroke.
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KEYWORD
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Machine learning, Occupational therapy, Physical therapy, Recovery of function, Rehabilitation research, Stroke
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FullTexts / Linksout information
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