Development and validation of an interpretable machine learning model to predict major adverse cardiovascular events after noncardiac surgery in geriatric patients: a prospective study
In this prospective study, the AutoGluon model could accurately predict MACEs after noncardiac surgery in geriatric patients, outperforming existing models and the RCRI. Subsequent interpretability analysis can provide insight into how our model works and help personalize surgical strategies.
Authors
- Xuechao Hao (ORCID: https://orcid.org/0000-0002-8312-7060)
- Ruihao Zhou (ORCID: https://orcid.org/0000-0002-2119-0945)
- Xiran Peng
- Tao Zhu (ORCID: https://orcid.org/0000-0002-0715-4366)
- Jiayu Yu (ORCID: https://orcid.org/0000-0003-2573-8741)
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Sichuan University (CN)
Publication Details
- Journal
- International Journal of Surgery
- Published
- 2024-12-26
- DOI
- https://doi.org/10.1097/js9.0000000000002203
- Citations
- 7
- Primary Topic
- Cardiac, Anesthesia and Surgical Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 2.81