We don't need predictions, we need EXACT PROTOCOLS THAT DELIVER RECOVERY. Are you that fucking clueless?
A Random Tree Forest decision support system to personalize upper extremity robot-assisted rehabilitation in stroke: a pilot study
All Authors
Abstract:
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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