Interpretable machine learning models for the prediction of all‐cause mortality and time to death in hemodialysis patients
Two new interpretable clinical tools have been proposed to predict all-cause mortality and time to death in HD patients using machine learning models. The minimal and readily accessible data on which Model B is based makes it a valuable tool for integrating into clinical decision-making processes.
Authors
- Yuanwen Xu (ORCID: https://orcid.org/0009-0005-8835-4050)
- Yunuo Wang
- Youbing Zeng (ORCID: https://orcid.org/0009-0005-3320-2500)
- Mengting Liu (ORCID: https://orcid.org/0000-0003-4972-9006)
- Minjie Chen (ORCID: https://orcid.org/0000-0002-2642-5803)
- Zhenghui Li (ORCID: https://orcid.org/0000-0002-9791-9333)
- Jiazhen Wu
- Xuan Tian
Institutions
- Sun Yat-sen University (CN)
- Shantou University (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
Publication Details
- Journal
- Therapeutic Apheresis and Dialysis
- Published
- 2024-09-26
- DOI
- https://doi.org/10.1111/1744-9987.14212
- Citations
- 6
- Primary Topic
- Dialysis and Renal Disease Management
- Type
- article
- Field-Weighted Citation Impact
- 2.04
Funders
- Sun Yat-sen University
- China Postdoctoral Science Foundation