Predicting blood pressure variability in hemodialysis using an explainable boosting machine model
The EBM models demonstrated excellent cross-branch generalizability while providing feature-level transparency that reflects clinical understanding of dialysis physiology, unlike black-box models that require additional post-hoc explanation methods such as SHapley Additive exPlanations (SHAP). These findings provide a foundation for developing patient-specific solutions for individuals with highly variable blood pressure patterns.
Authors
- Yaping Zheng (ORCID: https://orcid.org/0000-0003-4650-6624)
- Fang‐Ju Sun
- Chih-Jen Wu (ORCID: https://orcid.org/0009-0005-9198-319X)
- Wun-Yi Huang (ORCID: https://orcid.org/0009-0009-0990-6734)
- Kun‐Pin Wu (ORCID: https://orcid.org/0000-0002-3108-0221)
- Hung-Ting Chen
- Cheng-Jui Lin
Institutions
- National Yang Ming Chiao Tung University (TW)
- China Medical University (TW)
- Mackay Memorial Hospital (TW)
- Mackay Medical University (TW)
- China Medical University Hospital (TW)
- Mackay Junior College of Medicine, Nursing and Management (TW)
Publication Details
- Journal
- Clinical Kidney Journal
- Published
- 2025-11-14
- DOI
- https://doi.org/10.1093/ckj/sfaf349
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00