Machine Learning Model-Based Prediction of In-Hospital Acute Kidney Injury Risk in Acute Aortic Dissection Patients
The CatBoost model, constructed using risk factors including maximum and minimum BUN levels, BMI, urine output, and maximum GLU, effectively predicts the risk of in-hospital AKI in AAD patients and shows compelling results in further validations.
Authors
- Yang Chen (ORCID: https://orcid.org/0000-0002-8494-0141)
- Guangzu Liu (ORCID: https://orcid.org/0000-0002-3592-2713)
- Bing Song (ORCID: https://orcid.org/0000-0003-0772-0265)
- Yuan Hu
- Shidong Liu (ORCID: https://orcid.org/0009-0008-6320-0161)
- Zhili Wei (ORCID: https://orcid.org/0000-0001-8612-5207)
- Hongxu Liu (ORCID: https://orcid.org/0000-0003-2898-6046)
Institutions
- First Hospital of Lanzhou University (CN)
- Lanzhou University (CN)
Publication Details
- Journal
- Reviews in Cardiovascular Medicine
- Published
- 2025-02-21
- DOI
- https://doi.org/10.31083/rcm25768
- Citations
- 5
- Primary Topic
- Cardiac, Anesthesia and Surgical Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 6.94
Funders
- Natural Science Foundation of Gansu Province