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The possible role of machine learning in detection of increased cardiovascular risk patients – KSC MR Study (design)
 
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2nd Department of Cardiology, Faculty of Medicine, Pavol Jozef Safarik University and East Slovak Institute of Cardiovascular Diseases, Košice, Slovak Republic
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SLOVACRIN & Medical Science Park, Faculty of Medicine, Pavol Jozef Safarik University, Kosice, Slovak Republic
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Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University of Kosice, Kosice, Slovak Republic
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2nd Department of Internal Medicine, Pavol Jozef Safarik University and Louis Pasteur University Hospital, Kosice, Slovak Republic
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1st Department of Cardiology, Faculty of Medicine, Pavol Jozef Safarik University and East Slovak Institute of Cardiovascular Diseases, Košice, Slovak Republic
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Department of Cardiosurgery, Faculty of Medicine, Pavol Jozef Safarik University and East Slovak Institute of Cardiovascular Diseases, Košice, Slovak Republic
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Institute of Medical and Clinical Biochemistry, Faculty of Medicine, Pavol Jozef Safarik University, Kosice, Slovak Republic
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2nd Department of Psychiatry, Pavol Jozef Safarik University and Louis Pasteur University Hospital, Kosice, Slovak Republic
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1st Dental Clinic, Pavol Jozef Safarik University and Louis Pasteur University Hospital, Kosice, Slovak Republic
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Institute of Nursing, Faculty of Medicine, Pavol Jozef Safarik University, Slovak Republ
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Department of Infectology and Travel Medicine, Pavol Jozef Safarik University and Louis Pasteur University Hospital, Kosice, Slovak Republic
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Department of Medical Informatics, Faculty of Medicine, Pavol Jozef Safarik University, Košice, Slovak Republic
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Academy Dental Centre and Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Pavol Jozef Safarik University and Louis Pasteur University Hospital, Kosice, Slovak Republic
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Cardiovascular Disease Centre, J.A. Reiman Faculty Hospital Presov, Presov, Slovak Republic
Submission date: 2020-04-24
Final revision date: 2020-06-14
Acceptance date: 2020-06-14
Online publication date: 2020-09-21
 
 
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ABSTRACT
Currently, just a few major parameters are used for cardiovascular (CV) risk quantification to identify many of the high-risk subjects; however, they leave a lot of them with an underestimated level of CV risk which does not reflect the reality. The submitted study design of the Kosice Selective Coronarography Multiple Risk (KSC MR) Study will use computer analysis of coronary angiography results of admitted patients along with broad patients’ characteristics based on questionnaires, physical findings, laboratory and many other examinations. Obtained data will undergo machine learning protocols with the aim of developing algorithms which will include all available parameters and accurately calculate the probability of coronary artery disease. The KSC MR study results, if positive, could establish a base for development of proper software for revealing high-risk patients, as well as patients with suggested positive coronary angiography findings, based on the principles of personalised medicine.
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ISSN:1734-1922