康复
个性化
随机森林
决策树
集合(抽象数据类型)
结果(博弈论)
机器人
比例(比率)
物理医学与康复
计算机科学
冲程(发动机)
机器学习
人工智能
物理疗法
医学
工程类
数学
机械工程
物理
数理经济学
量子力学
万维网
程序设计语言
作者
C Camardella,G Cappiello,Z. Curto,Marco Germanotta,Irene Aprile,Stefano Mazzoleni,A Scoglio,A Frisoli
标识
DOI:10.1109/icorr55369.2022.9896509
摘要
Robotic-based rehabilitation administered by means of serious games certainly represents the frontier of rehabilitation treatments, offering a high degree of customization of therapy, to meet individual patients’ needs and to tailor a proper rehabilitation therapy. Despite the rush on developing complex rehabilitation systems, they often do not provide clinicians with long-term information about the outcome of rehabilitation, thus, not supporting them in the initial set-up phase of the therapy. In this paper, a Random-Forest based system was trained and tested to provide a prediction at discharge of several clinical scales outcomes (i.e. FMA, ARAT, and MI), having clinical scale scores and measures from the robotic system at the enrollment as inputs. The dataset includes 25 post-stroke patients from different clinics, that underwent a variable number of days of rehabilitation with a robotic treatment. Results have shown that the system is able to predict the final outcome with an accuracy ranging from 60% to 73% on the selected scales. Also results provide information on which variables are more relevant for the prediction of outcome of therapy, in particular clinical scales scores such as FMA, ARAT, MI, NRS, PCS, and MCS and robotic automatically extracted measurements related to patient’s work expenditure and time. This supports the idea of using such a system in a clinical environment in a decision support tool for clinicians.
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