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

Changing stroke rehab and research worldwide now.Time is Brain! trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.

Saturday, July 18, 2020

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.

Can this document be ordered through NARIC's document delivery service*?: Y.
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.