Risk Prediction of Major Adverse Cardiovascular Events Within One Year After Percutaneous Coronary Intervention in Patients With Acute Coronary Syndrome: Machine Learning–Based Time-to-Event Analysis
The proposed time-to-event ML model effectively captured dynamic risk patterns after PCI and identified key predictors with time-varying effects. These findings may support individualized postdischarge management and early intervention strategies to prevent MACE in high-risk patients.
Authors
- Hack‐Lyoung Kim (ORCID: https://orcid.org/0000-0002-6703-1472)
- Hong‐Jae Choi (ORCID: https://orcid.org/0000-0002-1931-1883)
- Youn‐Jung Son (ORCID: https://orcid.org/0000-0002-0961-9606)
- Changhee Lee (ORCID: https://orcid.org/0000-0002-8681-4739)
Institutions
- Seoul National University (KR)
- Korea University (KR)
- Seoul Metropolitan Government (KR)
- SNUH SMG-SNU Boramae Medical Center (KR)
- Chung-Ang University (KR)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-11-27
- DOI
- https://doi.org/10.2196/81778
- Primary Topic
- Acute Myocardial Infarction Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00