Application of Machine Learning in the Prediction of the Acute Aortic Dissection Risk Complicated by Mesenteric Malperfusion Based on Initial Laboratory Results
This study employed machine learning algorithms to develop a model capable of identifying MMP risk based on initial preoperative laboratory test results. This model can serve as a basis for making decisions in the treatment and diagnosis of MMP.
Authors
- Zhaofei Ye
- Chengxiang Li (ORCID: https://orcid.org/0009-0007-7109-7697)
- Yuyu Li (ORCID: https://orcid.org/0000-0002-3984-0889)
- Ping Li (ORCID: https://orcid.org/0000-0002-6121-8367)
- Jiale Dong
- Yulin Li (ORCID: https://orcid.org/0009-0003-2053-1458)
- Zhili Ji (ORCID: https://orcid.org/0009-0007-6642-6652)
- Zhechuan Jin (ORCID: https://orcid.org/0009-0000-3804-1172)
- Jian Yang (ORCID: https://orcid.org/0000-0003-4255-9622)
- Zequan Li (ORCID: https://orcid.org/0000-0002-2348-4437)
Institutions
- Capital Medical University (CN)
- Beijing Anzhen Hospital (CN)
Publication Details
- Journal
- Reviews in Cardiovascular Medicine
- Published
- 2025-06-27
- DOI
- https://doi.org/10.31083/rcm37827
- Citations
- 2
- Primary Topic
- Aortic Disease and Treatment Approaches
- Type
- article
- Field-Weighted Citation Impact
- 3.04
Funders
- National Natural Science Foundation of China