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Article

A Bayesian optimization approach for rapidly mapping residual network function in stroke.

Autor/s:

Lorenz R, Johal M, Dick F, Hampshire A, Leech R, Geranmayeh F.

Any publicació:

2021

Número de revista:

Brain vol. 144 n. 7

https://academic.oup.com/brain/article/144/7/2120/6174117

Article

A DATA MINING APPROACH TO IDENTIFY COGNITIVE NEUROREHABILITATION RANGE IN TRAUMATIC BRAIN INJURY PATIENTS

Autor/s:

García-Rudolph A, Gibert K

Any publicació:

2014

https://www.sciencedirect.com/science/article/pii/S0957417414001249

Article

A Machine Learning Approach to Predict Post-stroke Fatigue. The Nor-COAST study.

Autor/s:

Luzum G, Thrane G, Aam S, Eldholm RS, Grambaite R, Munthe-Kaas R, Thingstad P, Saltvedt I, Askim T.

Any publicació:

2024

Número de revista:

Archives of Physical Medicine and Rehabilitation. vol. 105 n. 5

https://www.sciencedirect.com/science/article/pii/S0003999323007086

Article

Center of Pressure- and Machine Learning-based Gait Score and Clinical Risk Factors for Predicting Functional Outcome in Acute Ischemic Stroke.

Autor/s:

Jeon ET, Lee SH, Eun MY, Jung JM.

Any publicació:

2024

Número de revista:

Archives of Physical Medicine and Rehabilitation. vol. 105 n. 12

https://www.archives-pmr.org/article/S0003-9993(24)01183-3/fulltext

Article

Challenges of Developing a Natural Language Processing Method With Electronic Health Records to Identify Persons With Chronic Mobility Disability.

Autor/s:

Agaronnik ND, Lindvall C, El-Jawahri A, He W, Iezzoni LI.

Any publicació:

2020

Número de revista:

Archives of Physical Medicine and Rehabilitation. vol. 101 n. 10

https://www.archives-pmr.org/article/S0003-9993(20)30291-4/fulltext

Article

Clinical machine learning predicting best stroke rehabilitation responders to exoskeletal robotic gait rehabilitation.

Autor/s:

Park S, Choi J, Kim Y, You JSH.

Any publicació:

2024

Número de revista:

NeuroRehabilitation vol. 54 n. 3

https://content.iospress.com/articles/neurorehabilitation/nre240060

Article

Decision Tree Analysis Accurately Predicts Discharge Destination After Spinal Cord Injury Rehabilitation.

Autor/s:

Kato C, Uemura O, Sato Y, Tsuji T.

Any publicació:

2024

Número de revista:

Archives of Physical Medicine and Rehabilitation. vol. 105 n. 1

https://www.sciencedirect.com/science/article/pii/S0003999323004793

Article

Identifying best fall-related balance factors and robotic-assisted gait training attributes in 105 post-stroke patients using clinical machine learning models.

Autor/s:

Kim H, Shin J, Kim Y, Lee Y, You JSH

Any publicació:

2024

Número de revista:

NeuroRehabilitation vol. 55 n. 1

https://content.iospress.com/articles/neurorehabilitation/nre240116

Article

Improving Accelerometry-Based Measurement of Functional Use of the Upper Extremity After Stroke: Machine Learning Versus Counts Threshold Method.

Autor/s:

Lum PS, Shu L, Bochniewicz EM, Tran T, Chang LC, Barth J, Dromerick AW.

Any publicació:

2020

Número de revista:

Neurorehabilitation and Neural Repair vol. 34 n. 12

https://journals.sagepub.com/doi/full/10.1177/1545968320962483

Article

Machine Learning for Predicting Motor Improvement After Acute Subcortical Infarction Using Baseline Whole Brain Volumes.

Autor/s:

Liu G, Wu J, Dang C, Tan S, Peng K, Guo Y, Xing S, Xie C, Zeng J, Tang X.

Any publicació:

2022

Número de revista:

Neurorehabilitation and Neural Repair vol. 36 n. 1

https://journals.sagepub.com/doi/full/10.1177/15459683211054178

Una iniciativa de:

Cercador

A Bayesian optimization approach for rapidly mapping residual network function in stroke.

A DATA MINING APPROACH TO IDENTIFY COGNITIVE NEUROREHABILITATION RANGE IN TRAUMATIC BRAIN INJURY PATIENTS

A Machine Learning Approach to Predict Post-stroke Fatigue. The Nor-COAST study.

Center of Pressure- and Machine Learning-based Gait Score and Clinical Risk Factors for Predicting Functional Outcome in Acute Ischemic Stroke.

Challenges of Developing a Natural Language Processing Method With Electronic Health Records to Identify Persons With Chronic Mobility Disability.

Clinical machine learning predicting best stroke rehabilitation responders to exoskeletal robotic gait rehabilitation.

Decision Tree Analysis Accurately Predicts Discharge Destination After Spinal Cord Injury Rehabilitation.

Identifying best fall-related balance factors and robotic-assisted gait training attributes in 105 post-stroke patients using clinical machine learning models.

Improving Accelerometry-Based Measurement of Functional Use of the Upper Extremity After Stroke: Machine Learning Versus Counts Threshold Method.

Machine Learning for Predicting Motor Improvement After Acute Subcortical Infarction Using Baseline Whole Brain Volumes.

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