Application of an interpretable machine learning method to predict the risk of death during hospitalization in patients with acute myocardial infarction combined with diabetes mellitus
This study demonstrates the potential of ML methods, particularly the RF model, in predicting in-hospital mortality risk for AMI patients with DM. The SHAP and LIME methods enhance the interpretability of ML models.
Authors
- Zhaolan Liu (ORCID: https://orcid.org/0000-0003-2992-1578)
- Man Liu (ORCID: https://orcid.org/0000-0001-7086-7767)
- Enze Lei (ORCID: https://orcid.org/0000-0001-8977-5758)
- Zhijun Bu (ORCID: https://orcid.org/0009-0006-0051-1662)
- Zhi‐Yan Han (ORCID: https://orcid.org/0000-0003-2543-1627)
- Jian-Ping Liu (ORCID: https://orcid.org/0000-0003-4814-4284)
- Siyu Bai
- Youzhu Su
- Yao Chen (ORCID: https://orcid.org/0000-0002-2336-3432)
- Linyan Wang
- Jingge Li
- Chan Yang
- Guanhang Lu
Institutions
- Nanjing University of Chinese Medicine (CN)
- Beijing University of Chinese Medicine (CN)
- Hubei University of Chinese Medicine (CN)
- Hubei Provincial Hospital of Traditional Chinese Medicine (CN)
Publication Details
- Journal
- Acta cardiologica. Supplementum
- Published
- 2025-04-08
- DOI
- https://doi.org/10.1080/00015385.2025.2481662
- Citations
- 5
- Primary Topic
- Renal function and acid-base balance
- Type
- article
- Field-Weighted Citation Impact
- 7.15
Funders
- National Natural Science Foundation of China