Deans' stroke musings: Machine learning methods predict individual upper-limb motor impairment following therapy in chronic 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.

Saturday, July 18, 2020

Machine learning methods predict individual upper-limb motor impairment following therapy in chronic stroke

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!

Machine learning methods predict individual upper-limb motor impairment following therapy in chronic stroke

Neurorehabilitation and Neural Repair (NNR) , Volume 34(5) , Pgs. 428-439.

NARIC Accession Number: J83842. What's this?
ISSN: 1545-9683.
Author(s): Tozlu, Ceren ; Edwards, Dylan ; Boes, Aaron ; Labar, Douglas ; Tsagaris, Zoe ; Silverstein, Joshua ; Lane, Heather P. ; Sabuncu, Mert R. ; Liu, Charles ; Kuceyeski, Amy.
Publication Year: 2020.
Number of Pages: 12.

Abstract:

Study evaluated the performance of 5 machine learning methods in predicting postintervention upper-extremity motor impairment in chronic stroke patients using demographic, clinical, neurophysiological, and imaging input variables. A total of 102 patients were included. The upper-extremity Fugl-Meyer Assessment (UE-FMA) was used to assess motor impairment of the upper limb before and after intervention. Elastic net (EN), support vector machines, artificial neural networks, classification and regression trees, and random forest were used to predict postintervention UE-FMA. Analysis compared the performances of the machine learning methods. Results showed that EN performed significantly better than other methods in predicting postintervention UE-FMA using demographic and baseline clinical data. Preintervention UE-FMA and the difference in motor threshold (MT) between the affected and unaffected hemispheres were the strongest predictors. The difference in MT had greater importance than the absence or presence of a motor-evoked potential in the affected hemisphere. Findings suggest that machine learning methods may enable clinicians to accurately predict a chronic stroke patient’s postintervention UE-FMA. Interhemispheric difference in the MT is an important predictor of chronic stroke patients’ response to therapy and, therefore, could be included in prospective studies.
Descriptor Terms: BRAIN, CLIENT CHARACTERISTICS, ELECTRICAL STIMULATION, ELECTROPHYSIOLOGY, FUNCTIONAL LIMITATIONS, IMAGING, LIMBS, MOTOR SKILLS, OUTCOMES, PREDICTION, QUANTITATIVE ANALYSIS, STROKE.

Can this document be ordered through NARIC's document delivery service*?: Y.
Get this Document: https://journals.sagepub.com/doi/10.1177/1545968320909796.

Citation: Tozlu, Ceren , Edwards, Dylan , Boes, Aaron , Labar, Douglas , Tsagaris, Zoe , Silverstein, Joshua , Lane, Heather P. , Sabuncu, Mert R. , Liu, Charles , Kuceyeski, Amy. (2020). Machine learning methods predict individual upper-limb motor impairment following therapy in chronic stroke. Neurorehabilitation and Neural Repair (NNR) , 34(5), Pgs. 428-439. Retrieved 7/18/2020, from REHABDATA database.