Inpatient stroke rehabilitation: Prediction of clinical outcomes using a machine-learning approach

Oh fuck, more useless and shitworthy prediction crapola.  Survivors don't care about predictions, predictions are based upon the complete failure to get survivors 100% recovered using the tyranny of low expectations. Change that recovery failure by creating effective rehab protocols, NOT GUIDELINES!

 

Inpatient stroke rehabilitation: Prediction of clinical outcomes using a machine-learning approach

Journal of NeuroEngineering and Rehabilitation , Volume 17(71)

NARIC Accession Number: J83958.  What's this?
ISSN: 1743-0003.
Author(s): Harari, Yaar ; O’Brien, Megan K. ; Lieber, Richard L. ; Jayaraman, Arun.
Publication Year: 2020.
Number of Pages: 10.

Abstract: 

Study used machine-learning algorithms to develop predictive models for four standardized clinical outcome measures (Functional Independence Measure, Ten-Meter Walk Test, Six-Minute Walk Test, and Berg Balance Scale) after inpatient stroke rehabilitation. Fifty stroke survivors admitted to a United States inpatient rehabilitation hospital participated in this study. Predictors chosen for the clinical discharge scores included demographics, stroke characteristics, and scores of clinical tests at admission. The Pearson product-moment and Spearman’s rank correlation coefficients were used to calculate correlations among clinical outcome measures and predictors. A cross-validated Lasso regression was used to develop predictive equations for discharge scores of each clinical outcome measure, and a Random Forest-based permutation analysis compared the relative importance of the predictors. Results showed the predictive equations explained 70 to 77 percent of the variance in discharge scores and resulted in a normalized error of 13 to 15 percent for predicting the outcomes of new patients. The most important predictors were clinical test scores at admission. Additional variables that affected the discharge score of at least one clinical outcome were time from stroke onset to rehabilitation admission, age, sex, body mass index, race, and diagnosis of dysphasia or speech impairment. The models presented in this study could help clinicians and researchers to predict the discharge scores of clinical outcomes for individuals enrolled in an inpatient stroke rehabilitation program that adheres to Medicare standards.
Descriptor Terms: CLIENT CHARACTERISTICS, DEMOGRAPHICS, EQUILIBRIUM, MOBILITY, OUTCOMES, PHYSICAL THERAPY, POSTURE, PREDICTION, QUANTITATIVE ANALYSIS, REHABILITATION, STROKE.


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Get this Document: https://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-020-00704-3.

Citation: Harari, Yaar , O’Brien, Megan K. , Lieber, Richard L. , Jayaraman, Arun. (2020). Inpatient stroke rehabilitation: Prediction of clinical outcomes using a machine-learning approach.  Journal of NeuroEngineering and Rehabilitation , 17(71) Retrieved 7/18/2020, from REHABDATA database.