A Review on Machine Learning Applications: CVI Risk Assessment
| Autor: | Ayşe Banu Birlik, Hakan Tozan, Kevser Banu Köse |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Tehnički Vjesnik, Vol 31, Iss 4, Pp 1422-1430 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1330-3651 1848-6339 |
| DOI: | 10.17559/TV-20230326000480 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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