A SuperLearner approach for predicting diabetic kidney disease upon the initial diagnosis of T2DM in hospital
We developed and validated a DKD risk prediction model for patients with newly diagnosed T2DM. Using routinely available clinical measurements, the SuperLearner model could predict DKD during hospital visits. Prediction accuracy and SHAP-based model interpretability may help improve early detection, targeted interventions, and prognosis of patients with DM.
Authors
- Xiaomeng Lin (ORCID: https://orcid.org/0000-0003-3084-5074)
- Jiming Xu (ORCID: https://orcid.org/0000-0003-4291-9018)
- Xiaohui Fan (ORCID: https://orcid.org/0000-0002-6336-3007)
- Xin Peng (ORCID: https://orcid.org/0000-0002-6168-9505)
- Yao Wang (ORCID: https://orcid.org/0000-0003-4121-7313)
- Linfeng Li (ORCID: https://orcid.org/0000-0002-8736-6464)
- Huaiyu Wang (ORCID: https://orcid.org/0000-0002-9093-8793)
- Chao Liu (ORCID: https://orcid.org/0009-0009-6163-641X)
- Xudong Cai (ORCID: https://orcid.org/0000-0003-4911-1089)
- Changlin Li (ORCID: https://orcid.org/0000-0003-0649-5449)
Institutions
- Zhejiang Chinese Medical University (CN)
- Beijing University of Chinese Medicine (CN)
- Cloud Computing Center (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2025-03-26
- DOI
- https://doi.org/10.1186/s12911-025-02977-x
- Citations
- 2
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 4.96