Personalized Prediction of Long-Term Renal Function Prognosis Following Nephrectomy Using Interpretable Machine Learning Algorithms: Case-Control Study
An interpretable ML model effectively elucidated its decision-making process in identifying patients at risk of AKD and CKD following nephrectomy by enumerating critical features. The web-based calculator, found on the LightGBM model, can assist in formulating more personalized and evidence-based clinical strategies.
Authors
- Zhihui Zhu (ORCID: https://orcid.org/0000-0002-0037-194X)
- Bin Zhou (ORCID: https://orcid.org/0000-0002-2857-6648)
- Quandong Bu (ORCID: https://orcid.org/0000-0002-9579-0759)
- Yan Xu (ORCID: https://orcid.org/0000-0003-2049-3976)
- Liwei Zhang (ORCID: https://orcid.org/0000-0002-1015-5482)
- Shuang Gao (ORCID: https://orcid.org/0000-0001-5988-9287)
- Chenyu Li (ORCID: https://orcid.org/0000-0002-5402-584X)
- Lingyu Xu (ORCID: https://orcid.org/0000-0002-6818-530X)
- Xuefei Shen
- Long Zhao (ORCID: https://orcid.org/0000-0003-3503-2371)
- Chengyu Yang (ORCID: https://orcid.org/0000-0002-1505-754X)
- Cheng Guo (ORCID: https://orcid.org/0000-0002-8773-3068)
- Chen Guan (ORCID: https://orcid.org/0009-0003-0755-8461)
Institutions
- Qingdao University (CN)
- Capital Medical University (CN)
- Helmholtz Zentrum München (DE)
- LMU Klinikum (DE)
- Affiliated Hospital of Qingdao University (CN)
- Beijing Anzhen Hospital (CN)
- German Association of Joint Industrial Applied Research Institutes (DE)
- Ocean University of China (CN)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2024-09-20
- DOI
- https://doi.org/10.2196/52837
- Citations
- 6
- Primary Topic
- Chronic Kidney Disease and Diabetes
- Type
- article
- Field-Weighted Citation Impact
- 2.04