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Stroke mortality prediction using machine learning: systematic review
Accurate prognostication of stroke may help in appropriate therapy and rehabilitation planning. In the past few years, several machine learning (ML) algorithms were applied for prediction of stroke outcomes. We aimed to examine the performance of machine learning –based models for the prediction of mortality after stroke, as well as to identify the most prominent factors for mortality.
Source: Journal of the Neurological Sciences - December 20, 2022 Category: Neurology Authors: Lihi Schwartz, Roi Anteby, Eyal Klang, Shelly Soffer Tags: Review Article Source Type: research

Stroke mortality prediction using machine learning: A systematic review
Accurate prognostication of stroke may help in appropriate therapy and rehabilitation planning. In the past few years, several machine learning (ML) algorithms were applied for prediction of stroke outcomes. We aimed to examine the performance of machine learning –based models for the prediction of mortality after stroke, as well as to identify the most prominent factors for mortality.
Source: Journal of the Neurological Sciences - December 20, 2022 Category: Neurology Authors: Lihi Schwartz, Roi Anteby, Eyal Klang, Shelly Soffer Tags: Review Article Source Type: research

Machine learning algorithms identify demographics, dietary features, and blood biomarkers associated with stroke records
We conducted a comprehensive evaluation of features associated with stroke records.
Source: Journal of the Neurological Sciences - July 8, 2022 Category: Neurology Authors: Jundong Liu, Elizabeth L. Chou, Kui Kai Lau, Peter Y.M. Woo, Jun Li, Kei Hang Katie Chan Source Type: research

Neuropsychological and neuroimaging evidences of cerebral dysfunction in stroke-free patients with atrial fibrillation: A review
Atrial fibrillation (AF) is the most common heart arrhythmia, with the highest prevalence in the elderly. AF has been correlated with silent lesions and cognitive impairment, even in the absence of stroke. The cognitive impairment in AF represents a risk of functional decline, morbidity, mortality and high costs, constituting a public health problem due to the increasing prevalence of this arrhythmia. Cognitive analysis of patients with AF without stroke has shown poor performance in executive, memory and learning functions.
Source: Journal of the Neurological Sciences - February 18, 2019 Category: Neurology Authors: D.S. Silva, A.C. Coan, W.M. Avelar Tags: Review Article Source Type: research

Computational models and motor learning paradigms: Could they provide insights for neuroplasticity after stroke? An overview
Computational approaches for modelling the central nervous system (CNS) aim to develop theories on processes occurring in the brain that allow the transformation of all information needed for the execution of motor acts. Computational models have been proposed in several fields, not only to interpret the CNS functioning, but also its efferent behaviour. Computational model theories can provide insights into neuromuscular and brain function allowing us to reach a deeper understanding of neuroplasticity.
Source: Journal of the Neurological Sciences - August 10, 2016 Category: Neurology Authors: Pawel Kiper, Andrzej Szczudlik, Annalena Venneri, Joanna Stozek, Carlos Luque-Moreno, Jozef Opara, Alfonc Baba, Michela Agostini, Andrea Turolla Source Type: research