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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

COVID-19 Can Increase Risk of Psychiatric Disorders for Up to Two Years
The increased risk of depression and anxiety that patients experience after developing COVID-19 typically subsides within two months, according to astudy published yesterday inThe Lancet Psychiatry. However, patients may have an elevated risk for developing other psychiatric and neurological conditions, such as psychosis, brain fog, and seizures, for up to two years after their infections.“The results have important implications for patients and health services as it suggests new cases of neurological conditions linked to COVID-19 infection are likely to occur for a considerable time after the pandemic has subsided,” s...
Source: Psychiatr News - August 18, 2022 Category: Psychiatry Tags: adults anxiety brain fog children COVID-19 delta dementia depression older adults omicron psychotic disorder seizures The Lancet Psychiatry Source Type: research