Machine Learning Algorithm Identifies the Importance of Environmental Factors for Hospital Discharge to Home of Stroke Patients using Wheelchair after Discharge.

Journal: Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
PMID: 34029887

Abstract

BACKGROUND AND PURPOSE: Physical environmental factors are generally likely to become barriers for discharge to home of wheelchair users, compared with non-wheelchair users. However, the importance of environmental factors has not been investigated adequately. Application of machine learning technology might efficiently identify the most influential factors, although it is not easy to interpret and integrate various information including individual and environmental factors in clinical stroke rehabilitation. This study aimed to identify the influential factors affecting home discharge in the stroke patients who use a wheelchair after discharge by using machine learning technology.

Authors

  • Takeshi Imura
    Department of Rehabilitation, Araki Neurosurgical Hospital, Hiroshima, Japan.
  • Yuji Iwamoto
    Department of Rehabilitation, Araki Neurosurgical Hospital, Hiroshima, Japan. Electronic address: yuji_ooooot@yahoo.co.jp.
  • Yuki Azuma
    Department of Surgery, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. Electronic address: yazuma@ims.u-tokyo.ac.jp.
  • Tetsuji Inagawa
    Department of Neurosurgery, Araki Neurosurgical Hospital, Hiroshima, Japan.
  • Naoki Imada
    Department of Rehabilitation, Araki Neurosurgical Hospital, Hiroshima, Japan.
  • Ryo Tanaka
    Graduate School of Humanities and Social Sciences, Hiroshima University, Japan.
  • Hayato Araki
    Department of Neurosurgery, Araki Neurosurgical Hospital, Hiroshima, Japan.
  • Osamu Araki
    Department of Neurosurgery, Araki Neurosurgical Hospital, Hiroshima, Japan.

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