Prediction of acute kidney injury in intensive care unit patients based on interpretable machine learning
The XGBoost algorithm can predict the occurrence of AKI more accurately. Interpretative analysis of the model reveals the mechanisms of key features, and reflects the individual differences between patients, providing an important clinical reference.
Authors
- Hong Wu (ORCID: https://orcid.org/0000-0002-9424-6022)
- Chi Zhang (ORCID: https://orcid.org/0000-0002-5215-4058)
- Chengcheng Wang (ORCID: https://orcid.org/0000-0002-3397-9900)
- Li Zhang (ORCID: https://orcid.org/0000-0003-1620-1512)
- Mingyu Li (ORCID: https://orcid.org/0009-0003-0126-7241)
Institutions
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- Digital Health
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1177/20552076241311173
- Citations
- 5
- Primary Topic
- Acute Kidney Injury Research
- Type
- article
- Field-Weighted Citation Impact
- 7.15
Funders
- National Natural Science Foundation of China