Development of an interpretable machine learning model to predict short-term bleeding risk in patients receiving dual antithrombotic therapy following cardiac surgery
This study demonstrates the feasibility and clinical utility of ML-based prediction of short-term bleeding risk in cardiac surgery patients on dual antithrombotic therapy. The RF model enables individualized risk assessment, with SHAP interpretation supporting clinical applicability.
Authors
- Jason Zhensheng Qu (ORCID: https://orcid.org/0000-0001-7586-8312)
- Jifan Zhang
- Xia Wang
- Hui Han (ORCID: https://orcid.org/0009-0008-7660-5504)
- Weihong Ge
Institutions
- Nanjing Foreign Language School (CN)
- Massachusetts General Hospital (US)
- Nanjing Drum Tower Hospital (CN)
Publication Details
- Journal
- International Journal of Clinical Pharmacy
- Published
- 2025-09-24
- DOI
- https://doi.org/10.1007/s11096-025-02016-9
- Citations
- 1
- Primary Topic
- Antiplatelet Therapy and Cardiovascular Diseases
- Type
- article
- Field-Weighted Citation Impact
- 1.39