Risk prediction for acute kidney disease and adverse outcomes in patients with chronic obstructive pulmonary disease: an interpretable machine learning approach
The incidence of AKI and AKD is significant in patients with COPD. Renal function trajectory is crucial for predicting mortality in these patients. Web applications were developed to predict AKI, AKD, and mortality, improving prognosis by identifying high-risk patients and reducing adverse events and disease progression.
Authors
- Lin Che (ORCID: https://orcid.org/0009-0005-2619-9382)
- Chen Guan (ORCID: https://orcid.org/0000-0002-3709-1247)
- Chenyu Li (ORCID: https://orcid.org/0000-0002-5402-584X)
- Yanfei Wang (ORCID: https://orcid.org/0000-0002-4797-7498)
- Xinyuan Wang (ORCID: https://orcid.org/0000-0002-4378-7566)
- Yan Xu (ORCID: https://orcid.org/0000-0001-9032-0718)
- Xuefei Shen
- Lingyu Xu
- Siqi Jiang
Institutions
- Qingdao University (CN)
- Affiliated Hospital of Qingdao University (CN)
Publication Details
- Journal
- Renal Failure
- Published
- 2025-04-07
- DOI
- https://doi.org/10.1080/0886022x.2025.2485475
- Citations
- 3
- Primary Topic
- Chronic Obstructive Pulmonary Disease (COPD) Research
- Type
- article
- Field-Weighted Citation Impact
- 4.56
Funders
- Qingdao Key Health Discipline Development Fund
- National Natural Science Foundation of China