A machine-learning method for predicting the 1-year risk of death in maintenance hemodialysis patients based on continuous compliance with dialysis quality indicators
The indicators time-to-standard ratio and fluctuation value may serve as potential methods for the continuous reaching of dialysis quality. Preliminary evidence suggests that machine learning model based on the indicator time-to-standard ratio of dialysis quality could have a reasonably good predictive effect on the prognosis of HD patients.
Authors
- Yongchun Ge (ORCID: https://orcid.org/0000-0002-0824-3977)
- Jianhua Dong (ORCID: https://orcid.org/0000-0002-7346-1647)
- X. Jessie Yang (ORCID: https://orcid.org/0000-0001-6071-0387)
- Chuan Li (ORCID: https://orcid.org/0000-0002-5612-522X)
- Bian Wu (ORCID: https://orcid.org/0000-0001-9890-4865)
- Mingwei Zhang (ORCID: https://orcid.org/0000-0003-1407-1531)
Institutions
- University of Shanghai for Science and Technology (CN)
- Nanjing University (CN)
Publication Details
- Journal
- BMC Nephrology
- Published
- 2025-10-15
- DOI
- https://doi.org/10.1186/s12882-025-04488-3
- Primary Topic
- Dialysis and Renal Disease Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00