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Application of Machine Learning Techniques to Identify Data Reliability and Factors Affecting Outcome After Stroke Using Electronic Administrative Records
Conclusion: Electronic administrative records from this cohort produced reliable outcome prediction and identified clinically appropriate factors negatively impacting most outcome variables following hospital admission with stroke. This presents a means of future identification of modifiable factors associated with patient discharge destination. This may potentially aid in patient selection for certain interventions and aid in better patient and clinician education regarding expected discharge outcomes.
Source: Frontiers in Neurology - September 27, 2021 Category: Neurology Source Type: research

Correlation Between Intracranial Arterial Calcification and Imaging of Cerebral Small Vessel Disease
Conclusion: Intracranial artery calcification is common in patients with ischemic cerebrovascular disease and the intracranial carotid artery is most frequently affected. Intracranial arterial calcifications might be associated with imaging markers of SVD and are highly correlated with WMHs, lacunes, and CMBs. Quantification of calcification on CT provides additional information on the pathophysiology of SVD. Intracranial arterial calcification could act as a potential marker of SVD. Introduction Atherosclerosis is a systemic vascular process that is considered a major cause of cerebrovascular and cardiovascular di...
Source: Frontiers in Neurology - April 30, 2019 Category: Neurology Source Type: research