Birlik, Ayşe BanuTozan, HakanKöse, Kevser Banu2024-07-172024-07-172024Birlik, 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-202303260004801330-36511848-6339http://dx.doi.org/10.17559/TV-20230326000480https://hdl.handle.net/20.500.12511/12723Comprehensive 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.eninfo:eu-repo/semantics/openAccessCardiovascularDecision-MakingMachine LearningPrediction ModelRisk AssessmentOther3141422143010.17559/TV-20230326000480Q30012584352000442-s2.0-85197731497Q3