Review article

https://doi.org/10.17559/TV-20230326000480

A Review on Machine Learning Applications: CVI Risk Assessment

Ayşe Banu Birlik orcid.org/0000-0001-5148-3784 ; 1) Istanbul Medipol University, Graduate School of Engineering and Natural Sciences, Department of Healthcare System Engineering, 34810, Istanbul, Turkey 2) Istinye University, Department of Medical Services and Techniques, 34810, Istanbul, Turkey *
Hakan Tozan orcid.org/0000-0002-0479-6937 ; American University of the Middle East, College of Engineering and Technology, Egaila, Kuwait
Kevser Banu Köse orcid.org/0000-0002-1766-2778 ; Istanbul Medipol University, School of Engineering and Natural Sciences, Department of Biomedical Engineering, 34810, Istanbul, Turkey

* Corresponding author.

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.

Keywords

cardiovascular; decision-making; machine learning; prediction model; risk assessment

Hrčak ID:

318504

URI

https://hrcak.srce.hr/318504

Publication date:

27.6.2024.

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