Machine learning-based risk prediction of acute kidney disease and hospital mortality in older patients
The LGBM models provide a valuable tool for early prediction of AKD, AKI, and mortality in older patients, facilitating timely interventions. This study highlights the potential of machine learning in improving older adult care, with the developed online tool offering practical utility for healthcare professionals. Further research should aim at external validation and integration of these models into clinical practice.
Authors
- Xiaofei Man
- Lin Che (ORCID: https://orcid.org/0009-0005-2619-9382)
- Yan Xu (ORCID: https://orcid.org/0000-0003-2049-3976)
- Chenyu Li (ORCID: https://orcid.org/0000-0002-5402-584X)
- Yanfei Wang (ORCID: https://orcid.org/0000-0001-7992-3999)
- Xinyuan Wang (ORCID: https://orcid.org/0009-0009-9265-5955)
- Lingyu Xu
- Guan Chen (ORCID: https://orcid.org/0000-0001-8834-039X)
- Daojun Xu (ORCID: https://orcid.org/0000-0003-3699-9488)
Institutions
- Qingdao University (CN)
- Linyi People's Hospital (CN)
- Affiliated Hospital of Qingdao University (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2024-08-15
- DOI
- https://doi.org/10.3389/fmed.2024.1407354
- Citations
- 2
- Primary Topic
- Chronic Kidney Disease and Diabetes
- Type
- article
- Field-Weighted Citation Impact
- 0.68
Funders
- Qingdao Key Health Discipline Development Fund
- National Natural Science Foundation of China