Dynamic survival prediction of end-stage kidney disease using random survival forests for competing risk analysis
We successfully developed and validated a dynamic survival prediction model using common longitudinal clinicopathological data. This model predicts ESKD with death as a competing risk and aims to assist clinicians in dialysis planning for patients with CKD.
Authors
- Aron Chakera (ORCID: https://orcid.org/0000-0003-3369-4187)
- Giles Walters (ORCID: https://orcid.org/0000-0003-4854-9353)
- Daniel Christiadi (ORCID: https://orcid.org/0000-0003-3279-3634)
- Kevin Chai (ORCID: https://orcid.org/0000-0003-1645-0922)
- Aaron Chuah (ORCID: https://orcid.org/0000-0003-3066-701X)
- Bronwyn Loong (ORCID: https://orcid.org/0000-0002-1409-8892)
- Simon Jiang (ORCID: https://orcid.org/0000-0002-3973-5114)
- T. Daniel Andrews (ORCID: https://orcid.org/0000-0003-3922-6376)
Institutions
- Australian National University (AU)
- Curtin University (AU)
- Sir Charles Gairdner Hospital (AU)
- Canberra Hospital (AU)
- Sir Charles Gairdner Osborne Park Health Care Group
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2024-12-11
- DOI
- https://doi.org/10.3389/fmed.2024.1428073
- Citations
- 2
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.77
Funders
- McCusker Charitable Foundation
- Australian Government
- La Trobe University
- Deakin University
- Curtin University of Technology
- Department of Health, Government of Western Australia
- National Health and Medical Research Council