Personalized USB Biosensor Module for Effective ECG Monitoring.

The burden of chronic disease and associated disability present a major threat to financial sustainability of healthcare delivery systems. The need for cost-effective early diagnosis and disease prevention is evident driving the development of personalized home health solutions. The proposed solution presents an easy to use ECG monitoring system.

DATA MINING APPROACH FOR IN-HOSPITAL TREATMENT OUTCOME IN PATIENTS WITH ACUTE CORONARY SYNDROME.

Introduction: Risk stratification is nowadays crucial when estimating the patient's prognosis in terms of treatment outcome and it also helps in clinical decision making. Several risk assessment models have been developed to predict short-term outcomes in patients with acute coronary syndrome. This study was aimed at developing an outcome prediction model for patients with acute coronary syndrome submitted to percutaneus coronary intervention using data mining approach.

THE LEVEL OF GRAMMAR SCHOOL STUDENTS' KNOWLEDGE ON CARDIOVASCULAR DISEASE RISK FACTORS.

Introduction: Cardiovascular diseases are one of the leading causes of mortality and morbidity worldwide. The atherosclerotic process in the aorta starts in childhood, while atheroclerotic changes of coronary heart vessels start in adolescence. The aim of the study was to evaluate the knowledge of the students attending all four grades of grammar school about the risk factors for cardiovascular disease, with special attention to the risk factors that can be influenced by modification of life-style.

Echocardiographic parameters as predictors of in-hospital mortality in patients with acute coronary syndrome undergoing percutaneous coronary intervention.

Different ways have been used to stratify risk in acute coronary syndrome (ACS) patients. The aim of the study was to examine the usefulness of echocardiographic parameters as predictors of in-hospital outcome in patients with ACS after percutaneous coronary intervention (PCI). A data of 2030 patients with diagnosis of ACS hospitalized from December 2008 to December 2011 was used to develop a risk model based on echocardiographic parameters using the binary logistic regression.