| 목적 : To summarize clinical and demographic variables and machine learning uses for predicting functional
outcomes of patients with stroke |
| 연구방법 : We searched PubMed, CINAHL and Web of S cience 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 tex t were excluded in this study. |
| 결과 : 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. |
| 결론 : 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. |