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Specialty: Neurology
Education: Learning

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Total 1309 results found since Jan 2013.

Machine learning is a valid method for predicting prehospital delay after acute ischemic stroke
ConclusionsMachine learning algorithms were not inferior to logistic regression models for prediction of prehospital delay after stroke. All models provided good discrimination, thereby creating valuable diagnostic programs for prehospital delay prediction.
Source: Brain and Behavior - August 17, 2020 Category: Neurology Authors: Li Yang, Qinqin Liu, Qiuli Zhao, Xuemei Zhu, Ling Wang Tags: ORIGINAL RESEARCH Source Type: research

Predicting 6-Month Unfavorable Outcome of Acute Ischemic Stroke Using Machine Learning
Conclusions: Compared with the HIAT score, the THRIVE score, and the NADE nomogram, the RFC model can improve the prediction of 6-month outcome in Chinese AIS patients.
Source: Frontiers in Neurology - November 19, 2020 Category: Neurology Source Type: research

Electroencephalography-Derived Prognosis of Functional Recovery in Acute Stroke Through Machine Learning Approaches.
Abstract Stroke, if not lethal, is a primary cause of disability. Early assessment of markers of recovery can allow personalized interventions; however, it is difficult to deliver indexes in the acute phase able to predict recovery. In this perspective, evaluation of electrical brain activity may provide useful information. A machine learning approach was explored here to predict post-stroke recovery relying on multi-channel electroencephalographic (EEG) recordings of few minutes performed at rest. A data-driven model, based on partial least square (PLS) regression, was trained on 19-channel EEG recordings perform...
Source: International Journal of Neural Systems - November 25, 2020 Category: Neurology Authors: Chiarelli AM, Croce P, Assenza G, Merla A, Granata G, Giannantoni NM, Pizzella V, Tecchio F, Zappasodi F Tags: Int J Neural Syst Source Type: research

Learning and Stroke Recovery: Parallelism of Biological Substrates
Semin Neurol DOI: 10.1055/s-0041-1725136Stroke is a debilitating disease. Current effective therapies for stroke recovery are limited to neurorehabilitation. Most stroke recovery occurs in a limited and early time window. Many of the mechanisms of spontaneous recovery after stroke parallel mechanisms of normal learning and memory. While various efforts are in place to identify potential drug targets, an emerging approach is to understand biological correlates between learning and stroke recovery. This review assesses parallels between biological changes at the molecular, structural, and functional levels during learning an...
Source: Seminars in Neurology - March 9, 2021 Category: Neurology Authors: Joy, Mary Teena Carmichael, S Thomas Tags: Review Article Source Type: research

Explicit motor sequence learning after stroke: a neuropsychological study
Exp Brain Res. 2021 Jun 5. doi: 10.1007/s00221-021-06141-5. Online ahead of print.ABSTRACTMotor learning interacts with and shapes experience-dependent cerebral plasticity. In stroke patients with paresis of the upper limb, motor recovery was proposed to reflect a process of re-learning the lost/impaired skill, which interacts with rehabilitation. However, to what extent stroke patients with hemiparesis may retain the ability of learning with their affected limb remains an unsolved issue, that was addressed by this study. Nineteen patients, with a cerebrovascular lesion affecting the right or the left hemisphere, underwent...
Source: Brain Research - June 6, 2021 Category: Neurology Authors: Cristina Russo Laura Veronelli Carlotta Casati Alessia Monti Laura Perucca Francesco Ferraro Massimo Corbo Giuseppe Vallar Nadia Bolognini Source Type: research

MRI radiomic features-based machine learning approach to classify ischemic stroke onset time
ConclusionsA unique deep learning model based on DWI/ADC radiomic features was constructed for TSS classification, which could aid in decision making for thrombolysis in patients with unknown stroke onset.
Source: Journal of Neurology - July 4, 2021 Category: Neurology Source Type: research

Prediction of 30-Day Readmission After Stroke Using Machine Learning and Natural Language Processing
Conclusion: NLP-enhanced machine learning models potentially advance our ability to predict readmission after stroke. However, further improvement is necessary before being implemented in clinical practice given the weak discrimination.
Source: Frontiers in Neurology - July 13, 2021 Category: Neurology Source Type: research

The acute effects of aerobic exercise on sensorimotor adaptation in chronic stroke
CONCLUSIONS: These results indicate a potential role for aerobic exercise to promote the recovery of sensorimotor function in chronic stroke survivors.PMID:34569981 | DOI:10.3233/RNN-211175
Source: Restorative Neurology and Neuroscience - September 27, 2021 Category: Neurology Authors: Christopher P Mackay Sandra G Brauer Suzanne S Kuys Mia A Schaumberg Li-Ann Leow Source Type: research