Publications by authors named "Katarina Petrikova"
Article Synopsis
- Recent advances in machine learning enable better analysis of patient data to predict outcomes, particularly for cardiogenic shock (CS) in patients with acute coronary syndrome using the MIMIC III database.
- This study focuses on improving data quality through various imputation techniques to handle missing data, comparing methods like k-nearest neighbors and Multiple Imputation by Chained Equations.
- The final model demonstrated strong classification performance with a mean area under the curve of 0.805, suggesting the potential utility of this data processing pipeline for other predictive analytics in healthcare.
Objectives: Purpose of this study was to evaluate properties of apelin, a peptide detectable in peripheral blood, for atrial fibrillation (AF) detection in a diverse population of patients covering a broad spectrum from healthy to polymorbid patients.
Background: AF is the most common cardiac arrhythmia with constantly increasing incidence and prevalence. Currently available diagnostic tools do not provide sufficient detection rate.
Atrial fibrillation (AF) is associated with high risk of stroke preventable by timely initiation of anticoagulation. Currently available screening tools based on ECG are not optimal due to inconvenience and high costs. Aim of this study was to study the diagnostic value of apelin for AF in patients with high risk of stroke.
View Article and Find Full Text PDFThe genus is a rapidly expanding group of ubiquitous bacteria that occur mainly in different animal species, but some can also be transmitted to humans. Three species, , , and , are responsible for the majority of human cases. The severity of the clinical symptoms often depends on the immune status of the patient, but others factors such as the species of the pathogen, virulence factors, and bacterial load also can play an important role.
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