Acute coronary syndromes are among main complains for patients presenting to emergency department. Risk classification systems are used to classify patients to appropriate risks and help physicians manage diagnosis strategies and treatments. Purpose of this study is to develop a clinical decision support system for patients presenting to emergency department with the help of statistical machine learning.
Acute coronary syndromes are among main complains for patients presenting to emergency department and create a burden to emergency departments and hospitals. Risk classification systems were developed and used to classify patients to appropriate risk groups. According to risk classification, different diagnosis and treatment modalities can be used and help physicians manage diagnosis strategies and treatments. Purpose of this study is to develop a clinical decision support system for patients presenting to emergency department with the help of statistical machine learning.
Study Type
OBSERVATIONAL
Enrollment
400
Study patients will be reviewed whether they will have coronary interventions in 30 days
Izmir Bozyaka Training and Research Hospital
Izmir, Turkey (Türkiye)
RECRUITINGNon fatal myocardial infarction, cardiac related death or coronary intervention in 30 days
Patients will be screened for 30 days for Non fatal myocardial infarction, cardiac related death or coronary intervention (coronary angiogram, coronary artery bypass surgery)
Time frame: 30 days
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