Machine learning for assessing the pretest probability of obstructive and non-obstructive coronary artery disease
The review presents an analysis of publications on use of machine learning (ML) to assess the pretest probability of obstructive and non-obstructive coronary artery disease (CAD). Data on the high prevalence of non-obstructive CAD among patients referred for coronary angiography are presented, which...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | Russian |
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«FIRMA «SILICEA» LLC
2020-06-01
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| Series: | Российский кардиологический журнал |
| Subjects: | |
| Online Access: | https://russjcardiol.elpub.ru/jour/article/view/3802 |
| _version_ | 1826561139569328128 |
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| author | B. I. Geltser M. M. Tsivanyuk K. I. Shakhgeldyan V. Yu. Rublev |
| author_facet | B. I. Geltser M. M. Tsivanyuk K. I. Shakhgeldyan V. Yu. Rublev |
| author_sort | B. I. Geltser |
| collection | DOAJ |
| description | The review presents an analysis of publications on use of machine learning (ML) to assess the pretest probability of obstructive and non-obstructive coronary artery disease (CAD). Data on the high prevalence of non-obstructive CAD among patients referred for coronary angiography are presented, which served as a reason for the development of ML-based models for pretest assessment of coronary anatomy. The use of modern modeling technologies has great potential in verification of obstructive and non-obstructive CAD. It is emphasized that the improvement of prognostic models and their practical implementation is an important element of medical decision making and should be carried out with interdisciplinary cooperation of clinicians and information technology specialists. |
| first_indexed | 2024-04-09T20:46:05Z |
| format | Article |
| id | doaj.art-cbd26f0c80d54a95849b3a61b76a0aec |
| institution | Directory Open Access Journal |
| issn | 1560-4071 2618-7620 |
| language | Russian |
| last_indexed | 2025-03-14T09:27:26Z |
| publishDate | 2020-06-01 |
| publisher | «FIRMA «SILICEA» LLC |
| record_format | Article |
| series | Российский кардиологический журнал |
| spelling | doaj.art-cbd26f0c80d54a95849b3a61b76a0aec2025-03-02T11:42:51Zrus«FIRMA «SILICEA» LLCРоссийский кардиологический журнал1560-40712618-76202020-06-0125510.15829/1560-4071-2020-38022905Machine learning for assessing the pretest probability of obstructive and non-obstructive coronary artery diseaseB. I. Geltser0M. M. Tsivanyuk1K. I. Shakhgeldyan2V. Yu. Rublev3Far Eastern Federal University, School of BiomedicineFar Eastern Federal University, School of BiomedicineFar Eastern Federal University, School of Biomedicine; Vladivostok State University of Economics and Service, Institute of Information TechnologiesFar Eastern Federal University, School of BiomedicineThe review presents an analysis of publications on use of machine learning (ML) to assess the pretest probability of obstructive and non-obstructive coronary artery disease (CAD). Data on the high prevalence of non-obstructive CAD among patients referred for coronary angiography are presented, which served as a reason for the development of ML-based models for pretest assessment of coronary anatomy. The use of modern modeling technologies has great potential in verification of obstructive and non-obstructive CAD. It is emphasized that the improvement of prognostic models and their practical implementation is an important element of medical decision making and should be carried out with interdisciplinary cooperation of clinicians and information technology specialists.https://russjcardiol.elpub.ru/jour/article/view/3802pre-test probabilitymachine learningcoronary artery disease |
| spellingShingle | B. I. Geltser M. M. Tsivanyuk K. I. Shakhgeldyan V. Yu. Rublev Machine learning for assessing the pretest probability of obstructive and non-obstructive coronary artery disease Российский кардиологический журнал pre-test probability machine learning coronary artery disease |
| title | Machine learning for assessing the pretest probability of obstructive and non-obstructive coronary artery disease |
| title_full | Machine learning for assessing the pretest probability of obstructive and non-obstructive coronary artery disease |
| title_fullStr | Machine learning for assessing the pretest probability of obstructive and non-obstructive coronary artery disease |
| title_full_unstemmed | Machine learning for assessing the pretest probability of obstructive and non-obstructive coronary artery disease |
| title_short | Machine learning for assessing the pretest probability of obstructive and non-obstructive coronary artery disease |
| title_sort | machine learning for assessing the pretest probability of obstructive and non obstructive coronary artery disease |
| topic | pre-test probability machine learning coronary artery disease |
| url | https://russjcardiol.elpub.ru/jour/article/view/3802 |
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