A review on machine learning applications: CVI risk assessment
| dc.authorid | 0000-0001-5148-3784 | |
| dc.authorid | 0000-0002-1766-2778 | |
| dc.contributor.author | Birlik, Ayşe Banu | |
| dc.contributor.author | Tozan, Hakan | |
| dc.contributor.author | Köse, Kevser Banu | |
| dc.date.accessioned | 2024-07-17T06:58:19Z | |
| dc.date.available | 2024-07-17T06:58:19Z | |
| dc.date.issued | 2024 | |
| dc.department | İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Biyomedikal Mühendisliği Bölümü | |
| dc.department | İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Enstitüsü, Sağlık Sistemleri Mühendisliği Anabilim Dalı | |
| dc.description.abstract | Comprehensive literature has been published on the development of digital health applications using machine learning methods in cardiovascular surgery. Many machine learning methods have been applied in clinical decision-making processes, particularly for risk estimation models. This review of the literature shares an update on machine learning applications for cardiovascular intervention (CVI) risk assessment. This study selected peer-reviewed scientific publications providing sufficient detail about machine learning methods and outcomes predicting short-term CVI risk in cardiac surgery. Thirteen articles fulfilling pre-set criteria were reviewed and tables were created presenting the relevant characteristics of the studies. The review demonstrates the usefulness of machine learning methods in high-risk CVI applications, identifies the need for improvement, and provides efficient support for future prediction models for the healthcare system. | |
| dc.identifier.citation | Birlik, A. B., Tozan, H. ve Köse, K. B. (2024). A review on machine learning applications: CVI risk assessment. Tehnicki Vjesnik, 31(4), 1422-1430. http://dx.doi.org/10.17559/TV-20230326000480 | |
| dc.identifier.doi | 10.17559/TV-20230326000480 | |
| dc.identifier.endpage | 1430 | |
| dc.identifier.issn | 1330-3651 | |
| dc.identifier.issn | 1848-6339 | |
| dc.identifier.issue | 4 | |
| dc.identifier.scopus | 2-s2.0-85197731497 | |
| dc.identifier.scopusquality | Q3 | |
| dc.identifier.startpage | 1422 | |
| dc.identifier.uri | http://dx.doi.org/10.17559/TV-20230326000480 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12511/12723 | |
| dc.identifier.volume | 31 | |
| dc.identifier.wos | 001258435200044 | en_US |
| dc.identifier.wosquality | Q3 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.institutionauthor | Birlik, Ayşe Banu | |
| dc.institutionauthor | Köse, Kevser Banu | |
| dc.language.iso | en | |
| dc.relation.ispartof | Tehnicki Vjesnik | en_US |
| dc.relation.publicationcategory | Diğer | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | Cardiovascular | |
| dc.subject | Decision-Making | |
| dc.subject | Machine Learning | |
| dc.subject | Prediction Model | |
| dc.subject | Risk Assessment | |
| dc.title | A review on machine learning applications: CVI risk assessment | |
| dc.type | Other |
