Clinical machine learning predicting best stroke rehabilitation responders to exoskeletal robotic gait rehabilitation.

Journal: NeuroRehabilitation
PMID: 38943406

Abstract

BACKGROUND: Although clinical machine learning (ML) algorithms offer promising potential in forecasting optimal stroke rehabilitation outcomes, their specific capacity to ascertain favorable outcomes and identify responders to robotic-assisted gait training (RAGT) in individuals with hemiparetic stroke undergoing such intervention remains unexplored.

Authors

  • Seonmi Park
    Department of Physical Therapy, Sports·Movement Artificial-Intelligence Robotics Technology (SMART) Institute, Yonsei University, Wonju, South Korea.
  • Jongeun Choi
    MSU Center for Orthopedic Research, Michigan State University, Lansing, MI, USA; School of Mechanical Engineering, Yonsei University, Seoul, Republic of Korea.
  • Yonghoon Kim
    Chungdam Rehabilitation Hospital Center, Seoul, South Korea.
  • Joshua Sung H You
    Department of Physical Therapy, Sports Movement Artificial Robotics Technology (SMART) Institute, Yonsei University, Wonju, Republic of Korea.

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