Establishment and comparison of prediction models for early-stage diabetic kidney disease
This article establishes multiple ES-DKD prediction models using CSMs and MLMs. New ideas and methods for the diagnosis, treatment, and prevention of ES-DKD in clinical practice are presented. This article also compares two modeling methods. A comprehensive model was established, which has excellent predictive and generalization ability and stability. Therefore, the integration of the advantages of MLMs based on CSMs is a very fruitful attempt. Fused models have a high chance of being the main research direction for future research to develop better models.
Authors
- Caimei Zhang (ORCID: https://orcid.org/0000-0001-6512-8505)
- Qian Xiao (ORCID: https://orcid.org/0000-0003-0040-3443)
- Yingda Sheng
- Feifei Ma (ORCID: https://orcid.org/0000-0001-8013-1569)
- Jianwen Zhang (ORCID: https://orcid.org/0000-0001-8292-6760)
- Xiaoqin Ha (ORCID: https://orcid.org/0000-0002-3280-413X)
- Jianguo Cheng (ORCID: https://orcid.org/0000-0002-2318-0812)
- Dan Wang (ORCID: https://orcid.org/0000-0003-3209-1616)
Institutions
- Gansu Agricultural University (CN)
- Chinese People's Liberation Army (CN)
- First Hospital of Lanzhou University (CN)
- Beijing Chaoyang Emergency Medical Center (CN)
- Lanzhou University (CN)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251355448
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00