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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

One-Third of COVID-19 Survivors May Develop a Neuropsychiatric Disorder Within Months of Infection
One-third of individuals diagnosed with COVID-19 developed a psychiatric or neurological problem within six months of their diagnosis, according to astudy published Tuesday inThe Lancet Psychiatry. The prevalence of a post-COVID neurologic or psychiatric diagnosis was even greater among individuals with severe illness who had required hospitalization.“Given the size of the pandemic and the chronicity of many of the diagnoses and their consequences (for example, dementia, stroke, and intracranial hemorrhage), substantial effects on health and social care systems are likely to occur,” wrote Maxime Taque, Ph.D., of the Un...
Source: Psychiatr News - April 7, 2021 Category: Psychiatry Tags: anxiety COVID-19 electronic health records hospitalizations mood disorders neuropsychiatric disorders The Lancet Psychiatry Source Type: research