Machine Learning-Based Prediction of Long-Term Outcomes in Patients With Chronic Total Occlusion of the Coronary Artery
The ML models are reliable in predicting 5-year clinical outcomes in patients with CTO, demonstrating their potential for clinical application.
Authors
- Hwa Jung Kim (ORCID: https://orcid.org/0000-0002-2227-6775)
- Hyeonyong Hae (ORCID: https://orcid.org/0000-0001-5802-8119)
- Soo‐Jin Kang
- Cheol Hyun Chung (ORCID: https://orcid.org/0000-0001-8981-6011)
- Joon Bum Kim (ORCID: https://orcid.org/0000-0001-5801-2395)
- Seung‐Whan Lee (ORCID: https://orcid.org/0000-0002-2662-5952)
- Ho Jin Kim (ORCID: https://orcid.org/0000-0002-0809-2240)
- Tae Oh Kim (ORCID: https://orcid.org/0000-0002-7275-9302)
Institutions
- Ulsan College (KR)
- Asan Medical Center (KR)
- University of Ulsan (KR)
Publication Details
- Journal
- Korean Circulation Journal
- Published
- 2025-01-01
- DOI
- https://doi.org/10.4070/kcj.2025.0016
- Citations
- 2
- Primary Topic
- Coronary Interventions and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 4.01
Funders
- Asan Institute for Life Sciences, Asan Medical Center
- Ministry of Science and ICT, South Korea