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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

Machine Learning Techniques in Blood Pressure Management During the Acute Phase of Ischemic Stroke
ConclusionThis is the first study to address BP management in the acute phase of ischemic stroke using ML techniques. The results indicate that the treatment choice should be adjusted to different clinical and BP parameters, thus, providing a better decision-making approach.
Source: Frontiers in Neurology - February 14, 2022 Category: Neurology Source Type: research

Potential Applications of Remote Limb Ischemic Conditioning for Chronic Cerebral Circulation Insufficiency
Conclusion Due to its long-term and often invisible course, CCCI has received less attention than acute cerebral ischemic stroke. However, without appropriate intervention, CCCI may lead to a variety of adverse events. Because the pathophysiological changes associated with CCCI are complex, pharmacological research in this area has been disappointing. Recent research suggests that RLIC, which is less invasive and more well-tolerated than drug treatment, can activate endogenous protective mechanisms during CCCI. In the present report, we reviewed studies related to CCCI (Table 1), as well as those related to stroke and sta...
Source: Frontiers in Neurology - May 2, 2019 Category: Neurology Source Type: research

Modifiable Lifestyle Factors and Cognitive Function in Older People: A Cross-Sectional Observational Study
Conclusions: Lifestyle factors, such as physical activity, sleep, and social activity appear to be associated with cognitive function among older people. Physical activity and appropriate durations of sleep and conversation are important for cognitive function. Introduction Dementia is a major public health issue worldwide, with a serious burden for patients, caregivers, and society, as well as substantial economic impacts (1). Although the prevalence of late-life cognitive impairment and dementia are expected to increase in future, effective disease-modifying treatments are currently unavailable. Therefore, unders...
Source: Frontiers in Neurology - April 23, 2019 Category: Neurology Source Type: research