Deans' stroke musings: Regression techniques employing feature selection to predict clinical outcomes in stroke

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.

Friday, April 17, 2020

Regression techniques employing feature selection to predict clinical outcomes in stroke

Oh God, prediction crapola once again. When are our stroke leaders going to read the riot act to them to work on the only goal in stroke, 100% recovery? Not this lazy crapola of prediction. Why not focus on what doctor interventions did to get to 100% recovery? Never mind, your doctor is doing nothing to stop the 5 causes of the neuronal cascade of death in the first week. Resulting in extra millions of neurons dying. Ask your doctor for a count of how many neurons died due to their inaction. $1000 a dead neuron sounds about right for billing your doctor.

Regression techniques employing feature selection to predict clinical outcomes in stroke

PLoS One , Volume 13(10) , Pgs. e0205639.

NARIC Accession Number: J83221. What's this?
ISSN: 1932-6203.
Author(s): Abdel Majeed, Yazan; Awadalla, Saria S. ; Patton, James L..
Project Number: 90RE5010 (formerly H133E120010).
Publication Year: 2018.
Number of Pages: 17.
Abstract: Study investigated the impact of patient demographic characteristics, movement features, and a three-week upper-extremity intervention on the post-treatment change in the Upper Extremity portion of the Fugl-Meyer (UEFM) and the Wolf Motor Function Test (WMFT). The study trained 26 chronic stroke survivors for a two-week period (six 1-hour training sessions) and extracted variables pertaining to three domains: patient movement, clinical state and demographics. Prediction models were used to identify the set of salient demographic and movement features related to change in the UEFM and WMFT. Of the regression models tested, the least absolute shrinkage and selection operator (LASSO) models performed best. In models based on LASSO, validation tests accounted for 65 and 86 percent of the variability in the UEFM and the WMFT, respectively. The results showed that age, affected limb, and several measures describing the patient's ability to efficiently direct motions with a single burst of speed were the most consequential in predicting clinical recovery. On the other hand, the upper-extremity intervention was not a significant predictor of recovery. Beyond a simple prognostic tool, these results suggest that focusing therapy on the more-important features is likely to improve recovery. Such validation-intensive methods are a novel approach to determining the relative importance of patient-specific metrics and may help guide the design of customized therapy.
Descriptor Terms: BODY MOVEMENT, CLIENT CHARACTERISTICS, DEMOGRAPHICS, INTERVENTION, LIMBS, MEASUREMENTS, MOTOR SKILLS, OUTCOMES, PREDICTION, QUANTITATIVE ANALYSIS, STROKE.

Can this document be ordered through NARIC's document delivery service*?: Y.
Get this Document: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0205639.

Citation: Abdel Majeed, Yazan, Awadalla, Saria S. , Patton, James L.. (2018). Regression techniques employing feature selection to predict clinical outcomes in stroke. PLoS One , 13(10), Pgs. e0205639. Retrieved 4/17/2020, from REHABDATA database.