Decision Tree Algorithm Identifies Stroke Patients Likely Discharge Home After Rehabilitation Using Functional and Environmental Predictors.

Journal: Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
PMID: 33545520

Abstract

BACKGROUND AND PURPOSE: The importance of environmental factors for stroke patients to achieve home discharge was not scientifically proven. There are limited studies on the application of the decision tree algorithm with various functional and environmental variables to identify stroke patients with a high possibility of home discharge. The present study aimed to identify the factors, including functional and environmental factors, affecting home discharge after stroke inpatient rehabilitation using the machine learning method.

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.
  • 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.
  • Haruki Toda
    Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo Japan.
  • Yu Inoue
    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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