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Expression of Cytokines and Chemokines as Predictors of Stroke Outcomes in Acute Ischemic Stroke
Conclusions: Machine learning algorithms can be employed to develop prognostic predictive biomarkers for stroke outcomes in ischemic stroke patients, particularly in regard to identifying acute gene expression changes that occur during stroke.
Source: Frontiers in Neurology - January 14, 2020 Category: Neurology Source Type: research

Prediction-Driven Decision Support for Patients With Mild Stroke: A Model Based on Machine Learning Algorithms
Conclusions: DAMS and R-DAMS, as prediction-driven decision support tools, were designed to aid clinical decision-making for mild stroke patients in emergency contexts. In addition, even within a narrow range of baseline scores, NIHSS on admission is the strongest feature that contributed to the prediction.
Source: Frontiers in Neurology - December 23, 2021 Category: Neurology Source Type: research

Machine Learning-Based Prediction of Brain Tissue Infarction in Patients With Acute Ischemic Stroke Treated With Theophylline as an Add-On to Thrombolytic Therapy: A Randomized Clinical Trial Subgroup Analysis
Conclusions: The predicted follow-up brain lesions for each patient were not significantly different for patients virtually treated with theophylline or placebo, as an add-on to thrombolytic therapy. Thus, this study confirmed the lack of neuroprotective effect of theophylline shown in the main clinical trial and is contrary to the results from preclinical stroke models.
Source: Frontiers in Neurology - May 21, 2021 Category: Neurology Source Type: research

Predicting Chronic Subdural Hematoma Recurrence and Stroke Outcomes While Withholding Antiplatelet and Anticoagulant Agents
Conclusion: ML modeling is feasible. However, large well-designed prospective multicenter studies are needed for accurate ML so that clinicians can balance the risks of recurrence with the risk of TEEs, especially for high-risk anticoagulated patients.
Source: Frontiers in Neurology - January 14, 2020 Category: Neurology Source Type: research

CT-based thrombus radiomics nomogram for predicting secondary embolization during mechanical thrombectomy for large vessel occlusion
ConclusionThis nomogram could be used to optimize the surgical MT procedure for LVO based on the risk of developing SE.
Source: Frontiers in Neurology - May 12, 2023 Category: Neurology Source Type: research