The possible role of machine learning in detection of increased cardiovascular risk patients - KSC MR Study (design).
Arch Med Sci
; 18(4): 991-997, 2022.
Article
in En
| MEDLINE
| ID: mdl-35832722
ABSTRACT
Introduction:
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. Material andmethods:
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.Results:
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.Conclusions:
The KSC MR study results, if positive, could establisha 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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Type of study:
Diagnostic_studies
/
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Language:
En
Journal:
Arch Med Sci
Year:
2022
Document type:
Article