Using Machine Learning to Predict MACEs Risk in Patients with Premature Myocardial Infarction
The prognostic model constructed based on RSF can accurately assess and stratify the risk of long-term MACEs in PMI patients. This can help clinicians make more targeted decisions and treatments, thus delaying and reducing the occurrence of poor prognoses.
Authors
- Jing Gao (ORCID: https://orcid.org/0000-0002-1729-2500)
- Zhuang Cui (ORCID: https://orcid.org/0000-0001-6495-766X)
- Yan Liang (ORCID: https://orcid.org/0000-0003-4145-3463)
- Yuhang Wang (ORCID: https://orcid.org/0009-0008-9763-0549)
- Changping Li (ORCID: https://orcid.org/0000-0003-4194-3470)
- Yin Liu (ORCID: https://orcid.org/0009-0005-4735-3350)
- Jing Wang (ORCID: https://orcid.org/0000-0003-0875-2453)
- Ping Lü (ORCID: https://orcid.org/0000-0002-1680-3241)
- Jingxian Wang (ORCID: https://orcid.org/0000-0002-9484-6570)
- Menglong Zhang (ORCID: https://orcid.org/0000-0002-7667-5974)
- Miaomiao Liang
- Anran Jing
Institutions
- Tianjin University (CN)
- Tianjin Chest Hospital (CN)
- Tianjin Medical University (CN)
Publication Details
- Journal
- Reviews in Cardiovascular Medicine
- Published
- 2025-05-20
- DOI
- https://doi.org/10.31083/rcm31298
- Citations
- 2
- Primary Topic
- Acute Myocardial Infarction Research
- Type
- article
- Field-Weighted Citation Impact
- 2.78