Development and Validation of Machine Learning-Based Prediction for Dependence in the Activities of Daily Living after Stroke Inpatient Rehabilitation: A Decision-Tree Analysis.

Journal: Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
PMID: 32992179

Abstract

BACKGROUND AND PURPOSE: Accurate prediction using simple and changeable variables is clinically meaningful because some known-predictors, such as stroke severity and patients age cannot be modified with rehabilitative treatment. There are limited clinical prediction rules (CPRs) that have been established using only changeable variables to predict the activities of daily living (ADL) dependence of stroke patients. This study aimed to develop and assess the CPRs using machine learning-based methods to identify ADL dependence in stroke patients.

Authors

  • Yuji Iwamoto
    Department of Rehabilitation, Araki Neurosurgical Hospital, Hiroshima, Japan. Electronic address: yuji_ooooot@yahoo.co.jp.
  • Takeshi Imura
    Department of Rehabilitation, Araki Neurosurgical Hospital, Hiroshima, Japan.
  • Ryo Tanaka
    Graduate School of Humanities and Social Sciences, Hiroshima University, Japan.
  • Naoki Imada
    Department of Rehabilitation, Araki Neurosurgical Hospital, Hiroshima, Japan.
  • Tetsuji Inagawa
    Department of Neurosurgery, Araki Neurosurgical Hospital, Hiroshima, 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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