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Iwamoto, Y., Imura, T., Tanaka, R., Imada, N., Inagawa, T., Araki, H., et al. (2020) 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 of Stroke & Cerebrovascular Diseases, 29, Article ID: 105332.
https://doi.org/10.1016/j.jstrokecerebrovasdis.2020.105332

has been cited by the following article:

• TITLE: Investigate Targeted Factors to Achieve Prediction Goal in Stroke Convalescence in Terms of Causal Relationships of Prediction Error

  AUTHORS: Takashi Kimura

  KEYWORDS: Prediction Error, Functional Independence Measure, Stroke, Convalescent Phase, Structural Equation Modeling

  JOURNAL NAME: Open Journal of Therapy and Rehabilitation, Vol.10 No.4, November 11, 2022

  ABSTRACT: Background and Purpose: To investigate target functional independence measure (FIM) items to achieve the prediction goal in terms of the causal relationships between prognostic prediction error and FIM among stroke patients in the convalescent phase using the structural equation modeling (SEM) analysis. Methods: A total of 2992 stroke patients registered in the Japanese Rehabilitation Database were analyzed retrospectively. The prediction error was calculated based on a prognostic prediction formula proposed in a previous study. An exploratory factor analysis (EFA) then the factor was determined using confirmatory factorial analysis (CFA). Finally, multivariate analyses were performed using SEM analysis. Results: The fitted indices of the hypothesized model estimated based on EFA were confirmed by CFA. The factors estimated by EFA were applied, and interpreted as follows: “Transferring (T-factor),” “Dressing (D-factor),” and “Cognitive function (C-factor).” The fit of the structural model based on the three factors and prediction errors was supported by the SEM analysis. The effects of the D- and C-factors yielded similar causal relationships on prediction error. Meanwhile, the effects between the prediction error and the T-factor were low. Observed FIM items were related to their domains in the structural model, except for the dressing of the upper body and memory (p